Mohammad Alajmi
email: maa.alajmi@paaet.edu.kw

Huda Farhan
email: hudafarhan@gmail.com

The intersection of source, message, and recipient characteristics on information-exchange activity via Twitter

Keywords: source characteristics, message characteristics, recipient characteristics, microblogging, information seeking, information sharing, information exchange

The purpose of the research is to explain how Twitter supports real-time information exchange in popular domains, and examine the influence of source, message, and recipient characteristics on information exchange. University students were surveyed using a structured questionnaire. A model was developed and statistically tested to examine the influence of characteristics on information exchange via Twitter. Different characteristics were demonstrated to be significant predictors of information exchange. For source characteristics, authoritative knowledge, social connections, and attractiveness have a positive influence, while for recipient characteristics, prior knowledge, community engagement, and demographics influence information exchange. For message characteristics, information usefulness and information quality are effective. Overall, influences vary based on the domain. The results help Twitter operators and users to understand the most important source, message, and recipient characteristics for information exchange. This study informs sources about the characteristics that make tweets more informative, and more likely to be exchanged by recipients. It assists Twitter operators to understand what characteristics are important in future system designs.

Introduction

The recent development of social networking sites (SNSs) has transformed the World Wide Web into a social and participatory place in which users create, broadcast, modify, and share information and knowledge [START_REF] Ribière | The impact of national culture traits on the usage of web 2.0 technologies[END_REF]. [START_REF] Shen | Understanding information adoption in online review communities: The role of herd factors[END_REF] stated that "online communities, including discussion forums, blogs, social networking sites, and microblogs, appear to be the most active places where users' information sharing behavior takes place" (p. 604).

Twitter, a social networking and microblogging service, is now used widely for broadcasting and exchanging messages, including information in various domains [START_REF] Java | Why we Twitter: Understanding microblogging usage and communities[END_REF]. Twitter is a valuable source of user-generated content, with users able to send and receive messages ("tweets") freely. Therefore, Twitter use potentially differs from other means of communication because of its timeliness and convenience within a context of reduced control and direct access to personal thoughts and opinions [START_REF] Peterson | To tweet or not to tweet: Exploring the determinants of early adoption of Twitter by House members in the 111th Congress[END_REF]. According to statistics from Statistic Brain Research Institute, in March 2015 Twitter had 645 million Twitter users, posting an average of 58 million tweets per day. This raises the question of what type of information is exchanged via Twitter and what are the most efficient characteristics that influence this activity. One of Twitter's key advantages is that users can interact with others and exchange information and knowledge. According to [START_REF] Wilson | Models in information behavior research[END_REF], users engage in information exchange by seeking and sharing information with each other within a communication medium. Users may aim to exchange information to discover more about topics of interest, evaluate particular information, and benefit others by sharing information. Thus, users typically inspect information that they exchange and determine what is valuable and what is not.

In recent years, Twitter has emerged as a popular research topic [START_REF] Peterson | To tweet or not to tweet: Exploring the determinants of early adoption of Twitter by House members in the 111th Congress[END_REF], [START_REF] Johan | Twitter mood predicting the stock market[END_REF][START_REF] Witkemper | Social media and sports marketing: Examining the motivations and constraints of Twitter users[END_REF][START_REF] Sreenivasan | Tweeting the friendly skies: Investigating information exchange among Twitter users about airlines[END_REF][START_REF] Scanfeld | Dissemination of health information through social networks: Twitter and antibiotic[END_REF]. Studies have identified several motivations, gratifications, and drawbacks affecting Twitter usage, and have generally concluded that Twitter is an important platform for information seeking and sharing during real-time events. Research has examined the factors affecting information adoption, credibility, diffusion, and exchange via social media, focusing on a particular domain or event [START_REF] Shen | Understanding information adoption in online review communities: The role of herd factors[END_REF], [START_REF] Sreenivasan | Tweeting the friendly skies: Investigating information exchange among Twitter users about airlines[END_REF], [START_REF] Chung | Blogging activity among cancer patients and their companions: Uses, gratifications, and predictors of outcomes[END_REF], [START_REF] Liu | Determinants of information retweeting in microblogging[END_REF][START_REF] Zarrella | Science of retweets[END_REF][START_REF] Hersberger | Examining information exchange and virtual communities: An emergent framework[END_REF].

Although research has shown the importance of Twitter in information exchange, little is known about the effect of source, message, and recipient characteristics on the success of information exchange when users share and seek information and in particular domains. Previous studies suffer from conflicting conclusions, restrictions in the factors considered, and limitations of subjects or events. This study examines how Twitter supports information exchange in various domains, and comparatively examines the influence of source, message, and recipient characteristics on the most popular information-exchange domains.

Literature review 2.1 Information exchange and social media

Studies of collaborative information use in electronic environments have indicated that users are amenable to information exchange in terms of sharing experiences and recommendations, and connect with others who have similar concerns via social networks [START_REF] Virkus | Use of Web 2.0 technologies in LIS education: Experiences at Tallinn University[END_REF][START_REF] Matschke | Motivational factors of information exchange in social information spaces[END_REF]. A number of studies discussed the use of social media as a communication and information-sharing and -seeking resource in the context of different events and subjects [START_REF] Liu | Determinants of information retweeting in microblogging[END_REF], [START_REF] Chung | Blogging activity among cancer patients and their companions: Uses, gratifications, and predictors of outcomes[END_REF], [START_REF] Belkin | Information concepts for information science[END_REF]. According to [START_REF] Buckland | Information as thing[END_REF][START_REF] Dervin | Useful theory for librarianship: Communication not information[END_REF][START_REF] Kim | Factors affecting the use of social software: TAM perspectives[END_REF], people seek and share information for comfort, support, empowerment of the knowledge they have, or simply to learn. Some information can simply be stored and recalled, while in other cases, it can be passed to others to make decisions and/or affect attitudes and behaviors.

Theories such as uses and gratifications, diffusion of innovation, and technology acceptance model promote an understanding of the use and acceptance of SNSs and identify determinants that influence SNS usage. Among several factors, the motivation for information exchange has played a sufficient role in SNSs general usage [START_REF] Smock | Facebook as a toolkit: A uses and gratifications approach to unbundling feature use[END_REF], features [START_REF] Holton | Seeking and sharing: Motivations for linking on Twitter[END_REF][START_REF] Earp | Facilitating educators' knowledge sharing with dedicated information systems[END_REF], and usage types [START_REF] Witkemper | Social media and sports marketing: Examining the motivations and constraints of Twitter users[END_REF], [START_REF] Chung | Blogging activity among cancer patients and their companions: Uses, gratifications, and predictors of outcomes[END_REF]. In the context of health, [START_REF] Chung | Blogging activity among cancer patients and their companions: Uses, gratifications, and predictors of outcomes[END_REF] investigated the factors that influence cancer patients and their companions in using blogs for health information. They found that blogging activity is most helpful for information sharing, and this is affected by perceived credibility and posting comments on others' blogs. [START_REF] Witkemper | Social media and sports marketing: Examining the motivations and constraints of Twitter users[END_REF] examined which motivational and constraint factors impact sport Twitter consumption with regard to athletics. Information motivations were found to guide sport organizations and practitioners in utilizing social media as an information source. Most, if not all, studies, explain that the need for information is an essential motivation to use SNSs.

Information exchange is a sufficient and necessary condition for knowledge creation and collaboration [START_REF] Mir | Factors affecting consumer attitudes and intentions toward usergenerated product content on YouTube[END_REF]. Because of the importance of information exchange in knowledge creation and construction, substantial research has investigated a wide variety of factors for information diffusion via SNSs [START_REF] Shen | Understanding information adoption in online review communities: The role of herd factors[END_REF], [START_REF] Belkin | Information concepts for information science[END_REF], [START_REF] Jansen | Twitter Power: Tweets as electronic word of mouth[END_REF]. [START_REF] Belkin | Information concepts for information science[END_REF] classified the motivations for information exchange into four types: environmental, personal, interpersonal, and socio-cultural. They identified motivational factors for participation in information exchange that are different from the motivations for hindering contribution. While previous studies focused on the use of SNSs for information exchange, these still suffer from a lack of theoretical framework in understanding information-exchange activity via SNSs.

Information exchange and Twitter

Twitter has recently developed rapidly because of its sufficient characteristics in relation to information pervasiveness [START_REF] Java | Why we Twitter: Understanding microblogging usage and communities[END_REF]. Because of its timeliness, convenience and large number of users, Twitter has been widely adopted by the public to publish/share information on different topics, such as marketing, sport, politics, health, and education. Unlike traditional media and other SNSs, Twitter provides real-time information and status updates in these domains. [START_REF] Johan | Twitter mood predicting the stock market[END_REF] investigated the effect of collective intelligence from Twitter information on predicting events in the stock market. [START_REF] Zhang | A case study of microblogging in the enterprise: Use, value, and related issues[END_REF] studied Twitter as a platform for online word-of-mouth branding, suggesting that Twitter could play an important role in marketing. Twitter information was also used in predicting trends in health [START_REF] Chung | Blogging activity among cancer patients and their companions: Uses, gratifications, and predictors of outcomes[END_REF], as well as airline products and services [START_REF] Sreenivasan | Tweeting the friendly skies: Investigating information exchange among Twitter users about airlines[END_REF] and enterprise collaboration [START_REF] Romero | Differences in the mechanics of information diffusion across topics: Idioms, political hashtags, and complex contagion on Twitter[END_REF].

Understanding information-exchange activity and its determinants on Twitter can help in the design of information systems and in assisting users to gain influence. A growing body of research explores factors that contribute to the process of information diffusion via Twitter [START_REF] Zarrella | Science of retweets[END_REF], [START_REF] Remy | Information diffusion on Twitter: everyone has its chance, but all chances are not equal[END_REF][START_REF] Suh | Want to be retweeted? Large scale analytics on factors impacting retweet in Twitter network[END_REF][START_REF] Bakshy | Everyone's an influencer: Quantifying influence on Twitter[END_REF][START_REF] Cress | Mass collaboration -an emerging field for CSCL research[END_REF]. [START_REF] Cress | Mass collaboration -an emerging field for CSCL research[END_REF] analyzed 1.6 million tweets to identify their attributes and relative influence for disseminating information. [START_REF] Zarrella | Science of retweets[END_REF] focused on the features of Twitter, including the features of tweets and retweets, that affect information diffusion and exchange. Overall, the research on information exchange via Twitter is still restricted to some determinants, and conducting research on other factors is highly recommended.

Twitter use in Arabic cultures

In developing countries, particularly the Arab region, Twitter has grown strongly in terms of the number of Arabic users and the amount of content posted in Arabic, indicating that Twitter is being adopted by broad segments of society. A growing body of work has started to identify the effect of national culture on information/knowledge sharing, diffusion, and adoption [START_REF] Shen | Understanding information adoption in online review communities: The role of herd factors[END_REF], [START_REF] Belkin | Information concepts for information science[END_REF]. Many studies have focused on cultures in the Far East, Europe, or North America. However, and despite the substantial increase in Twitter use in the Arab world to disseminate information and raise awareness of local and global events, to our knowledge, no research has explored in detail the domains of Twitter usage and the determinants that influence information exchange via Twitter in Middle Eastern cultures.

In order to build knowledge via social media, information exchange is a necessary condition that needs to be investigated [START_REF] Hovland | Communication change and persuasion: Psychological studies of opinion change[END_REF]. Social media makes it very convenient for users to share and seek information. Users log on to different social media sites to gather information to support their knowledge, and to make decisions. Our work extends existing research in two ways. First, we investigate the type of information that users share and seek via Twitter. Second, we investigate several characteristics to identify which are most related to information-exchange activity on Twitter.

Several theories have been applied to interpret how people are influenced by receiving information, such as Yale's model (exposure, attention, comprehension, acceptance, retention, and action) [START_REF] Deutsch | A study of normative and informational social influence upon individual judgments[END_REF], [START_REF] Petty | Communication and persuasion: Central and peripheral routes to attitude change[END_REF] dual-process theory of normative and informational influences, the elaboration likelihood model (ELM) [START_REF] Chaiken | Heuristic versus systematic information processing and the use of source versus message cues in persuasion[END_REF], and the heuristics systematic model (HSM) [START_REF] Doyle | Trust intentions in readers of blogs[END_REF]. These four theories concentrate on three major factors: source, message, and recipient. Yale's model posits that three factors-message, source, and audience-influence people's attention, comprehension, and acceptance of a message, which could later affect their opinions, perceptions, and actions. Dualprocess theory of human information processing considers how different types of influences (normative and informational factors) affect processing information, establishing its validity assessments, and ultimately form decision outcomes. ELM and HSM are the most prevalent dualprocess models in information-processing research. ELM posits that attitudes and behaviors are influenced by a message both centrally and peripherally. The former entails high-cognitive efforts to observe information, while the latter uses the environmental cues of the message to form judgments. HSM also posits that there are two types of information processing: heuristic and systematic. Heuristic processing uses cognitive heuristic or learned knowledge structure to assess a message, whereas systematic processing examines all relevant pieces of a message to form a decision.

Other categorizations have also been used. [START_REF] Belkin | Information concepts for information science[END_REF] classified the motivations for information exchange in social information spaces into environmental, personal, interpersonal, and sociocultural. [START_REF] Bongwon | Want to be retweeted? Large scale analytics on factors impacting retweet in Twitter network[END_REF] examined trust in bloggers of marketer and non-marketer sources using three categorizations of characteristics, namely bloggers, blog, and blog-reading outcomes. [START_REF] Zarrella | Science of retweets[END_REF] proposed a conceptual model based on HSM to investigate the determinants of information retweeting during emergency events, while [START_REF] Bakshy | Everyone's an influencer: Quantifying influence on Twitter[END_REF] investigated a number of features that might affect information diffusion via retweeting based on content features and context features.

We propose a theoretical framework with three categories of characteristics-source, message, and recipient-to comparatively identify the factors determining information-exchange activity in different domains on Twitter. We have formulated a number of hypotheses focusing on these categorizations (Figure . 1).

Source characteristics

Source characteristics refer to the extent to which the tweet's writer is perceived by recipients to be capable of providing high-quality information. [START_REF] Zarrella | Science of retweets[END_REF], [START_REF] Burgoon | Interactivity in human-computer interaction: A study of credibility, understanding and influence[END_REF] investigated the influence of source trustworthiness, expertise, and attractiveness on information retweeting in emergency events. [START_REF] Bongwon | Want to be retweeted? Large scale analytics on factors impacting retweet in Twitter network[END_REF] identified several blog authors' characteristics in the trust domain, including authoritative Sources are judged as credible based on perceived knowledge, competence, and expertise [START_REF] Chen | A paradigmatic and methodological examination of information systems research from 1991 to 2001[END_REF]. The knowledge of an information communicator depends on factors such as personal work, social experience, and personal background. Perceived knowledge can be assessed based on authoritative knowledge and engagement knowledge [START_REF] Bongwon | Want to be retweeted? Large scale analytics on factors impacting retweet in Twitter network[END_REF]. Authoritative knowledge is the extent to which the source is perceived to be capable of making accurate assertions in a particular topic area; it accrues over time, depending on factors such as personal work and personal background. [START_REF] Nahapiet | Social capital, intellectual capital and the organization advantage[END_REF] discussed the essential role of the positivist tradition, academicians, and portable knowerindependent knowledge on authoritative knowledge. The accuracy of authoritative knowledge is supported by formal credentials, for example university degrees, and signs of accomplishment, such as awards.

Engagement knowledge refers to the extent to which tweets' sources are perceived to be capable of providing information, on a longitudinal basis, from personal efforts and social interaction. Unlike authoritative knowledge, which develops primarily through detached inquiry of the topic area, engagement knowledge develops through involvement "within" the topic area [START_REF] Nahapiet | Social capital, intellectual capital and the organization advantage[END_REF]. Engagement knowledge depends on topic-area passion, involvement, and wide-ranging experience. Participating in a topic-area Twitter community, for example, by posting strong comments on other tweets and retweets, requires Twitter users to read as much topic-area content as possible. Thus, engagement knowledge is mostly consistent with the tasks of experience in other studies. [START_REF] Zarrella | Science of retweets[END_REF] explored the positive effect of source experience on information retweeting. [START_REF] Bongwon | Want to be retweeted? Large scale analytics on factors impacting retweet in Twitter network[END_REF] found that engagement knowledge has more influence than authoritative knowledge on trust formation in the blogosphere.

Source attractiveness-the extent to which writers are preferred by others in social mediacan be measured through profile features, namely number of followers and followees, age of the account, number of favorited tweets, and frequency of tweets. [START_REF] Zarrella | Science of retweets[END_REF], [START_REF] Burgoon | Interactivity in human-computer interaction: A study of credibility, understanding and influence[END_REF] identified the importance of source attractiveness on information retweeting.

Source characteristics are also assessed through social connections deriving from social capital. Social capital is the resources available through the structure, relational, and cognitive characteristics of interpersonal connections [START_REF] Tsai | Social capital and value creation: the role of intrafirm networks[END_REF][START_REF] Steinfield | Social capital, self-esteem, and use of online social network sites: A longitudinal analysis[END_REF]. Individuals with a large, diverse network of contacts allow users to exchange information with others, who willingly share information about their activities, actions, and experiences [START_REF] Lin | Building a network theory of social capital[END_REF]. Although research has underlined the necessary role of social capital in receiving and accruing information [START_REF] Dike | Applications of social capital in educational literature: A critical synthesis[END_REF][START_REF] Mayer | An integrative model of organizational trust[END_REF], indicators of its role in information exchange via social media do not exist. [START_REF] Bongwon | Want to be retweeted? Large scale analytics on factors impacting retweet in Twitter network[END_REF] investigated the effect of bloggers' social connections on trust intention and blog-reading outcomes, but neither trust intention nor blog-reading were predicted by bloggers' social connections.

The character of sources revolves around the totality of their personal integrity and benevolence toward readers. Perceived integrity is the extent to which a source is seen as honest, based on neutrality and commitment to moral and ethical conduct, whereas perceived benevolence is a source's caring and motivation toward members' needs and inquiries [START_REF] Mcknight | Developing and validating trust measures for e-commerce: An integrative typology[END_REF][START_REF] Wathen | Believe it or not: Factors influencing credibility on the web[END_REF]. Research in the context of the blogosphere has found that character has a significant influence on both trust intentions and blog-reading outcomes [START_REF] Bongwon | Want to be retweeted? Large scale analytics on factors impacting retweet in Twitter network[END_REF]. Thus, we hypothesize the following: H1: Source characteristics (authoritative knowledge, engagement knowledge, attractiveness, social connection, and character claims) influence information exchange in topic areas ((a) politics, (b) health, (c) sport, (d) business). [START_REF] Sin | International students' everyday life information seeking: The information value of social networking sites[END_REF] reviewed the literature on credibility of information and stated that "obviously, the message itself is critical for information credibility" (p. 140). Content, relevance, currency, accuracy, and usability have been identified as strongly influencing information credibility in electronic media.

Message characteristics

Twitter messages cannot exceed 140 characters, so tweets typically consist of short phrases and comments. Despite this limit, governments, institutions, enterprises, and organizations have adopted Twitter to facilitate formal and informal communication, to share information and to discover topics of interest. In their study of airline users' tweets, [START_REF] Sreenivasan | Tweeting the friendly skies: Investigating information exchange among Twitter users about airlines[END_REF] identified the main themes of information exchange, such as gathering information, sharing compliments, seeking and sharing information, providing community support, collaborating and helping with each other's problems, and reporting daily routines. Thus, Twitter users obviously perceive sources' tweets to be useful. This paper investigates the constructs of information usefulness and information quality. Information usefulness is the extent to which recipients find the information of messages useful for topic-area knowledge; information quality is the accuracy, comprehensiveness, and timeliness of information provided by messages.

Twitter users can mitigate the information volume deficiency using three mechanisms: URLs, mentioning, and multimedia. The short URL allows a link to be inserted into a message, through which users can access further information. Mentioning allows other users to be referenced in a tweet using the "@" symbol. To some extent, [START_REF] Zarrella | Science of retweets[END_REF] realized the analogy between URLs and "mentioning" roles in providing a higher volume of information. Multimedia enables Twitter users to attach videos, images, and audios to tweets, creating richness, which relates to a message's depth, intensity, and vividness of information. Recent studies indicated that these mechanisms positively influence the action of information retweeting [START_REF] Hersberger | Examining information exchange and virtual communities: An emergent framework[END_REF], [START_REF] Burgoon | Interactivity in human-computer interaction: A study of credibility, understanding and influence[END_REF] and partly influence retweeting in emergency events [START_REF] Zarrella | Science of retweets[END_REF], but the efficiency of these attempts in the form of information exchange need to be explored within various domains. Thus, we hypothesized the following:

H2: Message characteristics (information usefulness, information quality, and connected materials) influence the information exchange in topic areas ((a) politics, (b) health, (c) sport, (d) business).

Recipient characteristics

This refers to both stable factors, such as demographics, and variable personal characteristics. Understanding the information described in SNSs is a sufficient way to exchange information and build knowledge [START_REF] Bongwon | Want to be retweeted? Large scale analytics on factors impacting retweet in Twitter network[END_REF]. Twitter users introduce information from different domains, so information on Twitter comes from users with different knowledge. Using SNSs as an information-exchange strategy places the responsibility on recipients to share and find information that needs to be compared with the knowledge they hold, and measure the outcomes [START_REF] Fulk | A social influence model of technology use[END_REF]. Thus, the importance of information exchange via Twitter might be determined by recipients' knowledge.

According to the motivational models, community engagement is an important determinant for adopting social media. [START_REF] Ribière | The impact of national culture traits on the usage of web 2.0 technologies[END_REF] studied the importance of social relations on the expressive usage (social usage) and instrumental usage (information-sharing and -seeking usage) of social media. They identified the importance of community relations in expressive usage, but not in instrumental usage. In the community of practice, [START_REF] Belkin | Information concepts for information science[END_REF] underlined the importance of social-culture factors for increasing information exchange; their findings support the assumptions of the social influence model of technology use [START_REF] Awad | Establishing trust in electronic commerce through online word of mouth: An examination across genders[END_REF].

The influence of gender and age has been directly and indirectly explored in the context of SNSs [START_REF] Ribière | The impact of national culture traits on the usage of web 2.0 technologies[END_REF], [START_REF] Holton | Seeking and sharing: Motivations for linking on Twitter[END_REF], [START_REF] Katungi | Gender, social capital, and information exchange in rural Uganda[END_REF][START_REF] Liu | Gender differences in the determinants of sharing information via mobile phones[END_REF][START_REF] Pfeil | Age differences in online social networking: A study of user profiles and the social capital divide among teenagers and older users in Myspace[END_REF], though not in relation to information exchange across different domains via Twitter. Some studies found that younger users are more likely to use SNSs frequently and have more SNS friends than older users [START_REF] Valenzuela | Is there social capital in a social network site? Facebook use and college students' life satisfaction, trust and participation[END_REF][START_REF] Salem | Citizen engagement and public services in the Arab world: The potential of social media[END_REF]. Regarding gender, the Arab Social Media Report found that about 64% of male respondents used Twitter, while the percentage for female respondents was 34% [START_REF] Mazman | Gender differences in using social networks[END_REF] [START_REF] Salem | Citizen engagement and public services in the Arab world: The potential of social media[END_REF]. Men have been found to use SNSs for developing new contacts, whereas women use SNSs for maintain existing relationships [START_REF] Lin | Why people use social networking sites: An empirical study integrating network externalities and motivation theory[END_REF]. Men use SNSs more for task-oriented and less for interpersonal reasons [START_REF] Easterby-Smith | Working with pluralism: Determining quality in qualitative research[END_REF]. In measuring the effect of age and gender differences on the information value of SNSs, [START_REF] Fulk | A social influence model of technology use[END_REF] found no notable effects on respondents' perceived usefulness of the acquired everyday information in meeting their daily needs. Based on this background, we infer that recipients' characteristics bolster information-exchange activity via Twitter. Thus, we assume the following:

H3: Recipient characteristics (topic-area knowledge, community engagement, gender, and age) influence information exchange in topic areas ((a) politics, (b) health, (c) sport, (d) business).

Research methodology 4.1 Questionnaire design

One of the most efficient means of data collection when the researcher knows what data is required to answer the research questions and measure the research variables is the questionnaire [START_REF] Bosman | The review credibility of electronic word-ofmouth communication on e-commerce platforms[END_REF]. Participants were directed to answer questions on information-exchange activities in four domains (sport, health, politics, and business) using five-point Likert scales (5 = "always"; 1 = "never"). We selected these domains for three reasons. First, they are the most frequently identified domains in social media research [START_REF] Ribière | The impact of national culture traits on the usage of web 2.0 technologies[END_REF], [START_REF] Sreenivasan | Tweeting the friendly skies: Investigating information exchange among Twitter users about airlines[END_REF], [START_REF] Kabe | Twenty five influenced tweets in the general political issue[END_REF]. Second, an Ipsos study in Kuwait identified these as the most popular domains for Twitter users [START_REF] Head | How college students use the web to conduct everyday life research[END_REF]. Finally, these are the top domains for information seeking for graduate and undergraduate students [START_REF] Fulk | A social influence model of technology use[END_REF], [START_REF] Gall | Educational research: An introduction[END_REF].

This study measures the influence of source, message, and recipient characteristics on the domains mentioned. Source characteristics comprise engagement knowledge, authoritative knowledge, attractiveness, social connections, and character claims; message characteristics comprise information quality, information usefulness, and connected materials; and recipient characteristics comprise topic-area knowledge, community engagement, age, and gender.

Sample and data collection

We collected data from Twitter users from Kuwait. A pilot study was used to validate the questionnaire after it was translated into Arabic and double-checked by specialists to ensure the accuracy of the translation. Based on feedback, a few minor changes were made to the wording. For convenience sampling, paper versions of the questionnaire were distributed to 900 undergraduates at a Kuwaiti university, within classes that all students are required to take. We described the purpose of the study and how the questionnaires should be completed via an oral presentation by the principle investigators. All students were informed that their answers would be anonymous and would be used for academic purposes. We collected 872 responses, and after discarding incomplete questionnaires, the number of effective responses was 820.

Data analysis

We used a series of multiple regression analyses to test the hypothesized relationships among the theoretical model presented in Figure 1. In order to screen potential multicollinearity problems, an exploratory factor analysis (EFA) and the correlation matrix were computed for the variables.

Because several items were loaded at 0.3 and 0.4 in previous studies, and further items were suggested for some constructs, EFA was used to identify relationships among items using the maximum likehood analysis extraction in SPSS. The factor loadings presented in Table 1 demonstrate that the items were well grouped with acceptable and high loadings. Since the results indicated that all factor loadings are ≥ .5, it can be said that the instruments are reliable and valid. The correlation matrix of all predictor variables was calculated to assess the possible correlation between factors. No high correlation between the variables was found. Table 2 shows the factor correlation of the variables.

Results

Sample characteristics

The respondents' demographic information is shown in Table 3. Women accounted for 66.3% and men 33.7% of responses. One explanation is that the proportion of women in the population is larger than the proportion of men, and the sample reflects this. Most respondents (84.1%) were aged 18-24 years. a Five-point Likert-type scale, 1=never, 5=always

Hypotheses testing and results

Multiple regression analysis was used to examine the effect of a combination of two or more predictor variables on a criterion variable [START_REF] Zhou | The effect of flow experience on mobile SNS users' loyalty[END_REF]. The results of the four multiple linear regressions are presented in Table 5. It is worth noting that the R2 for sport information exchange (38.3%) was much higher than it was for politics (11%), health (6.4%), and business (4.3%).

Politics

With regard to source characteristics (H1.a), authoritative knowledge (β = .153, p < 0.001) and social connections (β = .090, p < 0.05) were positively significant, while engagement knowledge (β = -.125, p < 0.01) had a negative influence. Sources who attend major political events and have social connections with other experts positively influenced political information exchange. Character claims and attractiveness (p > 0.05) were insignificant in this domain. Message characteristics were also assessed (H2.a), with none of the characteristics found to have a positive influence. However, connected materials (β = -.154, p < 0.001) negatively influenced political information exchange. With regard to recipient characteristics (H3.a), topic knowledge (β = .226, p < 0.001) was statistically very significant. The age hypothesis (β = .111, p < 0.01) was also confirmed: the youngest in our sample are less likely than the oldest to use Twitter for political information exchange. Gender was negatively significant (β = -.168, p < 0.001), with women less interested than men in political information exchange. Community engagement was insignificant.

Sport

In terms of the effect of source characteristics (H1.b), social connections (β = .127, p < 0.001) were positively significant, while authoritative knowledge (β = -.066, p < 0.05) and engagement knowledge (β = -.096, p < 0.05) were negatively significant. However, when sources have higher social connections, their tweets are considered significant by users. Two of the message characteristics were also supported (H2.b). Information usefulness (β = .034, p < 0.01) and connected materials (β = -.080, p < 0.05) were statistically significant. Connected materials negatively contributed to sport information exchange. Regarding recipient characteristics (H3.b), topic-area knowledge (β = .123, p < 0.01) and community engagement (β = .079, p < 0.05) positively affected sport information exchange. However, gender (β = -.630, p < 0.001) negatively contributed in the model, with women less likely than men to exchange sport information.

Business

The effect of source characteristics on business information exchange (H1.c) was influenced by the characteristics of attractiveness (β = .126, p < 0.01) and character claims (β = -.104, p < 0.05). However, character claims negatively contributed to business information exchange. Interestingly, none of the message characteristics (H2.c) were significant in business information exchange. Regarding recipient characteristics (H3.c), topic-area knowledge (β = .106, p < 0.05) was the only characteristic that was positively statistically significant. As in the political and sport domains, gender contributed negatively to business information exchange: women were less likely than men to use Twitter for business information exchange. Community engagement and age characteristics were insignificant.

Health

Source characteristics (H1.d) were significant through sources' authoritative knowledge (β = .107, p < 0.01) and attractiveness (β = .155, p < 0.001); the other source characteristics were insignificant. With regard message characteristics (H2.d), only information quality (β = .196, p < 0.001) was significant. No recipient characteristics (H3.d) were significant.

Table 5. Results of the regression analysis on the hypotheses

Politics information exchange

Sport information exchange

Business information exchange

Health information exchange B(SE) Health information was found to be the most prominent of the four domains for information exchange on Twitter. This indicates that Twitter is an important resource for finding more than for sharing health information. Twitter allows users to seek and share information on sensitive health topics, such as drug use, sexual health, or depression. Political information exchange is the next most popular domain, but the results show a remarkable gap between information seeking and information sharing. Twitter users prefer to seek rather than share political information. This might relate to recent legislation and judicial rulings that restrict the freedom to participate in political issues. Sport is the third most active domain for information exchange. Consistent with the other domains, users seek rather than share sport information. Thus, the top priority for users is to find sport news, results, or upcoming events rather than share such information. Business information is the domain with the lowest rate of information exchange, with a small difference in information-seeking and information-sharing activities. Because most of the respondents are young students, business information is not a priority for them. To our knowledge, there is no directly comparable research on the importance ratings of the information-exchange activity for this population. Therefore, comparisons are made with literature that has a slightly different focus. While not a study of SNSs, [START_REF] Gall | Educational research: An introduction[END_REF] surveyed over 8,000 American undergraduates and identified news or current events, product and purchasing information, and health and wellness information as the top three everyday information needs. [START_REF] Fulk | A social influence model of technology use[END_REF] surveyed international students in the USA and found the top five information needs among SNSs to be finance, health, news of one's home country, housing, and entertainment. [START_REF] Head | How college students use the web to conduct everyday life research[END_REF] showed the greater importance of Twitter for political issues than for health, sport, or business issues. It is remarkable that the top five domains are similar in nature, with a slight difference in ranking. Extending the domains is encouraged to test whether different samples would yield similar results to this study.

β(t-value) B(SE) β(t-value) B(SE) β(t-value) B(SE) β(t-value)
With regard to the factors that influence information-exchange activity, regression analysis showed that source characteristics partially correlated with information exchange for the four domains. The traditional knowledge sources, authoritative knowledge and engagement knowledge, do not efficiently drive all information-exchange topics. Political and health tweets posted by sources who have academic degrees and awards and conduct accomplishments are most likely to be sought and shared by Twitter users. However, these characteristics significantly detract from sport tweets in information exchange. Twitter users were dubious of the activities of engagement knowledge in information exchange, especially for political and sport tweets. Many Twitter accounts that tweet sensitive information use pseudonyms, but users may not trust and interact with information from such accounts. [START_REF] Zarrella | Science of retweets[END_REF] identified the role of source expertise on information retweeting, which is consistent with our findings in political and health information exchange. Inconsistently, [START_REF] Bongwon | Want to be retweeted? Large scale analytics on factors impacting retweet in Twitter network[END_REF] found that trust formation is predicted by engagement knowledge of a blogger. It is also interesting that social network connections have more of a role in political and sport information exchange than in business and health information exchange. Therefore, a source with personal, professional, or institutional connections with political and sport experts and events provides information that is likely to be sought and shared by Twitter users. Character claims do not correlate with information-exchange activity, and actually detract from information exchange, especially in business tweets. This suggests that Twitter users consider sources' claims unneces-sary in information exchange. It can be asserted that character claims do not influence information diffusion via SNSs, the blogosphere [START_REF] Bongwon | Want to be retweeted? Large scale analytics on factors impacting retweet in Twitter network[END_REF], and Twitter in particular. Source attractiveness affects business and health information exchange. Sources whose accounts have high numbers of followers, followees, tweets, and favorited tweets are of interest to Twitter users. Active sources in business and health on Twitter should have higher numbers of followers, followees, tweets, and favorited tweets. A source's credibility might be influenced by the number of followees, followers, tweets and favorited tweets in some subjects. These findings are in line with research on information retweeting on emergency events [START_REF] Zarrella | Science of retweets[END_REF]. Generally, if sources are qualified with authoritative knowledge, are active in terms of social connections, and have accounts with high numbers of followers, followees, tweets, and favorited tweets, recipients seek out and share their tweets.

Message characteristics partially influence information-exchange activity. Information quality was important in health and sport information exchange, suggesting that Twitter users are highly motivated to seek and share health and sport information if tweets contain high-quality information. Because health information is necessary and sensitive for everyone [START_REF] Sin | International students' everyday life information seeking: The information value of social networking sites[END_REF], it must be accurate, up to date, and easily understood. Information quality acts as a trust signal for information acquirement and communications [START_REF] Sussman | Informational influence in organizations: An integrated approach to knowledge adoption[END_REF], but the findings suggest that it is not related to knowledge acquirement on all topics [START_REF] Bongwon | Want to be retweeted? Large scale analytics on factors impacting retweet in Twitter network[END_REF]. The nature of the sample generation is sensitive due to the rapidly changing actions and behaviors in general. Therefore, using social media as a credible main source of information might be vital for their future health and decisions. This requires special efforts by information specialists and health professionals to develop a mechanism to monitor information flow on one side and publish high-quality information through Twitter on the other side. Information usefulness impacts only sport information exchange. It may be normal for sport to be the most useful domain for gaining information for our sample. This suggests that the length limitation of tweets might be efficient for sport information, but weak for political, health, and business information. Some topics might need additional content length to attract Twitter users' interest and encourage them to seek and share. Although previous research found that information usefulness is highly correlated with information adoption and electronic word-of-mouth communication [START_REF] Cheung | The impact of electronic word-of-mouth communication: A literature analysis and integrative model[END_REF][START_REF] Shin | What do people do with digital multimedia broadcasting? Path analysis of structural equation modeling[END_REF], our findings suggest that information usefulness influence varies between domains. Contrary to expectations, none of the connected materials were found to be useful in information exchange, particularly for politics and sport. This suggests that users prefer tweets without URLs, mentions, or multimedia materials. This is in line with [START_REF] Zarrella | Science of retweets[END_REF] findings, particularly for URLs and mentions, but completely contrasts with studies by [START_REF] Burgoon | Interactivity in human-computer interaction: A study of credibility, understanding and influence[END_REF] and [START_REF] Hersberger | Examining information exchange and virtual communities: An emergent framework[END_REF] regarding information retweeting. Future studies should consider particular situations within each domain and compare the characteristics of the current study on certain phenomena belong to different domains.

Information-exchange activity in politics, business, and sport appears to be driven primarily by recipient interaction to gain knowledge. Although Twitter users realize the limited usefulness of tweets in general, they are still determining the impacts of the tweets in supporting their current knowledge, except in health information exchange. Thus, the source must provide contents that facilitate goal attainment for recipients [START_REF] Bongwon | Want to be retweeted? Large scale analytics on factors impacting retweet in Twitter network[END_REF]. Community engagement was found to facilitate information-exchange activity only in the sport domain. It is interesting that sport information exchange connects Twitter users with their communities more than those who exchange political, business, and health information. Gender is a significant, but negative, characteristic of information exchange, except in health information exchange. Women are less likely than men to be interested in politics, business, and sport information exchange via Twitter. It seems that women do not interact with short contents as much as men do, so they may prefer other SNSs that do not limit message length. Also, the topics of the study might be more relevant to men than women. Research on the expressive and instrumental usages of Web 2.0 did not find gender differences [START_REF] Ribière | The impact of national culture traits on the usage of web 2.0 technologies[END_REF].

Age is also important, though only in political information exchange. Older participants were more interested than younger ones in political information exchange, suggesting that older users engage more with in-depth topics.

Implications

This study uniquely investigated the influence of source, message, and recipient characteristics on information-exchange activity via Twitter. Unlike previous research, this study comparatively investigated the determinants of the most popular domains of information exchange. Previous studies examined the factors affecting information retweeting or information exchange on a particular subject or situation [START_REF] Zarrella | Science of retweets[END_REF], [START_REF] Belkin | Information concepts for information science[END_REF], while this study aimed to explain the cognitive process underlying information-exchange activity and enhance the understanding of information seeking and sharing via Twitter. Thus, the findings extend the literature on information diffusion, and information exchange in particular, in various domains on Twitter.

This study facilitates an in-depth understanding of the key characteristics of information exchange based on source, message, and recipient characteristics. Information-exchange activity is influenced not only by the various characteristics, but also by the domains of information exchange. Not all subjects of information exchange might be influenced in the same way by the same characteristics. We also identify which tweets need to include URLs, mentions, or multimedia.

In this paper, sources' knowledge was distinguished between two human-capital ability resources: authoritative knowledge and engagement knowledge. As both contributed as predictors in this study and in [START_REF] Bongwon | Want to be retweeted? Large scale analytics on factors impacting retweet in Twitter network[END_REF] research, further research should consider the contribution of human-capital resources across populations, contexts, and social media applications.

These findings have practical implications for Twitter users. To increase informationexchange activity, sources should have particular characteristics, and these vary according to the specific domain. Although message characteristics had limited influence on information exchange, such characteristics still suggest some guidelines for posting tweets. Again, this varies according to the domain.

In terms of system design, Twitter needs to be an effective system not only for seeking and sharing information but also for categorizing tweet subjects. The length limit of tweets needs to be reviewed, especially for particular domains, to assist sources to provide more information. Some tweet contents should be excluded from the length limit, such as hyperlinks and character claims. Number of followers, followees, tweets, and favorited tweets are likely to attract users, and setting additional profile characteristics would increase information exchange and diffusion.

Limitations

The limitations of this study represent opportunities for future research. First, data were collected from a convenience sample of undergraduates. Although students are heavy users of social media, a convenience sample limits the validity of findings. Respondents might not represent the whole population because most SNS users are young people [START_REF] Kim | Crying for me, Crying for us: relational dialectics in a Korean social network site[END_REF]68]. Also, the favorite Twitter domains of the sample might restrict the influence of characteristics and the generalizability of the study to other groups. Future research should use samples across different generations and communities.

Second, this study focused on general information rather than a particular situation or event in each domain. Future research should comparatively investigate the influence of the current research's characteristics on information-exchange activity in particular situations, such as financial or political events.

Third, the findings found low variance within the models in terms of information-exchange activity. Further characteristics need to be investigated to increase the R2 value of our models. Finally, only gender and age were examined as antecedents in information-exchange activity. Other demographic factors should be examined as antecedents and moderators in future studies.

Conclusion

This research contributes to information-diffusion literature in general, and research on information exchange via Twitter in particular, by understanding the comparative influence of source, message, and recipient characteristics on exchanging information within four popular domains on Twitter. Source and recipient characteristics play a more important role than message characteristics in information-exchange activity. In source characteristics, authoritative knowledge, social connections, and attractiveness have a positive influence on information exchange, while in recipient characteristics, recipients' prior knowledge, community engagement, and demographics influence information exchange. In messages characteristics, information usefulness and information quality were demonstrated to be effective. However, overall the influences vary based on the domain of information exchange. This avenue of research will not only extend the scope of information diffusion and SNSs research; it will also facilitate the effective design and use of SNSs for different users and domains.

Figure 1 .

 1 Figure 1. Research model representing the hypotheses of the study

  knowledge, character of the author, author's character claims, and author's social connections. Liu et al.'s and Doyle et al.'s characteristics, with some modifications, are detailed enough to represent the source characteristics in this study.

  0.05. ** p < 0.01. *** p < 0.001. Gender: Dummy-coded with male = 0 and female = 1. Age: Dummy-coded with 18-24 years = 0, 25-34 years = 1, and 35 and over = 2.

Table 1 .

 1 Results of constructs' descriptive statistics, reliability and validity

	Variables	Alpha	Factor	Mean	SD
			loadings
	Authoritative knowledge Has written a book about the topic Teaches courses on the topic Organizes events on the topic area	0.69	.791 .501 .524	3.54 3.71 3.81	0.98 0.86 0.89
	Engagement knowledge Has extensive experience in the topic area Has resources that others do not have Is heavily involved in the area Is passionate about the topic Is interested in topic-area improvements	0.82	.630 .686 .772 .714 .568	4.26 4.13 4.12 4.09 3.93	0.76 0.86 0.82 0.84 0.77

Table 2 .

 2 Correlation between constructs

		M	SD	1	2	3	4	5	6	7	8	9 10
	Attractiveness 3.62 .94 1.00
	Engagement	4.10 .62 .401 1.00
	knowledge
	Information	4.19 .62 .359 .543 1.00
	quality
	Topic-area	4.03 .71 .278 .404 .542 1.00
	knowledge
	Connected	3.75 .79 .346 .100 .311 .193 1.00
	materials
	Authoritative	3.69 .72 .147 .420 .239 .288 .125 1.00
	knowledge
	Information	4.21 .55 .310 .478 .566 .418 .296 .315 1.00
	usefulness
	Character	3.93 .75 .494 .521 .414 .366 .369 .256 .425 1.00
	claims
	Community	3.90 .85 .156 .309 .270 .480 .229 .305 .308 .207 1.00
	engagement
	Social	3.78 .82 .255 .354 .118 .098 .204 .477 .194 .304 .206 1.00
	connections

Table 3 .

 3 Demographic information of respondents (N=820)

	Demographics	Value	Frequency	Percentage
	Gender	Male	276	33.7
		Female	544	66.3
	Age	18-24 years	690	84.1
		25-34 years	110	13.4
		35 and over	20	2.4

Table 4 .

 4 Activity rates for information sharing and information seeking through Twitter

	Activity a

Table 6 .

 6 Summary of the results of hypotheses test

	Source characteristics
	Authoritative knowledge	237(.062)	.153(3.824)***	-.126(.064)	-.066(-1.975)*	.055(.065)	.035(.847)	.174(.067)	.107(2.606) **
	Engagement knowledge	-.222(.081)	-.125(-2.753)**	-.212(.083)	-.096(-2.549)**	-.100(.084)	-.055(-1.179)	-.031(.087)	-.017(-.357)
	Social connections	.122(.058)	.090(2.096)*	.212(.060)	.127(3.537)**	-.033(.061)	-.024(-.540)	-.074(.063)	-.052(-1.187)
	Character claims	.032(.062)	.022(.510)	-.028(.064)	-.015(-.431)	-.153(.065)	-.104(-2.353) *	-.085(.067)	-.055(-1.265)
	Attractiveness	.008(.046)	.007(.168)	-.034(.047)	-.023(-.719)	.151(.048)	.126(3.144) **	.193(.049)	.155(3.914) ***
	Message characteristics
	Information quality	.016(.083)	.008(.195)	.259(.086)	.105(3.026)*	.158(.087)	.078(1.821)	.354(.089)	.169(3.953) ***
	Information usefulness	.025(.075)	.014(.340)	.075(.077)	.034(.971)**	-.016(.078)	-.009(-.199)	.036(.081)	.019(.442)
	Connected materials	-.216(.052)	-.154(-4.114)***	-.139(.054)	-.080(-2.560)*	-.059(.055)	-.042(-1.070)	-.043(.057)	-.029(-.757)
	Recipient characteristics
	Topic-area knowledge	.353(.068)	.226(5.170)***	.238(.071)	.123(3.378)**	.168(.072)	.106(2.348) *	-.049(.074)	-.030(-.660)
	Community engagement	.040(.051)	.030(.779)	.129(.053)	.079(2.442)**	-.078(.053)	-.059(-1.454)	.045(.055)	.033(.811)
	Gender	-.395(.087)	-.168(-4.527)***	-1.840(.090)	-.630(-20.418)***	-.279(.091)	-.117(-3.053) ***	-.089(.094)	-.036(-.945)
	Age	.276(.085)	.111(3.252)**	-.160(.088)	-.052(-1.819)*	.067(.089)	.027(.755)	.096(.092)	.036(1.046)
	F value	8.30***		41.70***		3.30***		4.57***
	R 2	.110		.383		.043		.064
	Adjusted R 2	.097		.374		.029		.050
	Hypotheses		Information exchange activity Politics Sport Business Health
	Source characteristics
	Authoritative knowledge	√	-√		√
	Engagement knowledge	-√	-√
	Social connections		√	√
	Character claims				-√
	Attractiveness				√	√
	Message characteristics
	Information quality				√
	Information usefulness		√
	Connected materials	-√	-√
	Recipient characteristics
	Topic-area knowledge	√	√	√
	Community engagement		√
	Gender			-√	-√	-√
	Age			√
	Note: -Negatively significant influence.

Acknowledgments

This work supported and funded by The Public Authority of Education and Training, Research project No (BF-15-12), Research Title (The Intersection of Source, Message, and Recipient Characteristics on Information-Exchange Activity via Twitter).


Salman Qayyum Mian
email: salman.mian@oulu.fi

Matti Mäntymäki
email: matti.mantymaki@utu.fi

Jukka Riekki
email: jukka.riekki@oulu.fi

Harri Oinas-Kukkonen
email: harri.oinas-kukkonen@oulu.fi

Social Sensor Web: Towards a Conceptual Framework

Keywords: social web, sensor, monitoring, context awareness, dual connectivity, health, and wellness

Sensor technology has become increasingly pervasive, leading to its use in many areas, such as health care and wellness. Current technologies and services are based on one-way connectivity, such that sensors are primarily used to transmit information. Though data generation is essential to the pursuit of meaningful information, having the right information at the right time in the right context is what successfully integrates the social web into real-world situations. This paper extends the concept of the Internet of Things and develops a conceptual framework of the Social Sensor Web that employs sensors to elaborate context-aware information in order to determine an effective approach to social interaction. The paper also highlights the importance of dual connectivity of sensor devices through workshops based on the state of the art. We present five dimensions of the Social Sensor Web that can serve as a guide for designing future health and wellness technologies.

Introduction

The rapid development and increasing affordability of sensor technologies, together with the spread of Internet connectivity, have led to a massive number of connected sensors. In the health care context, connected sensors are used to transmit individuals' blood glucose levels and blood pressure information to health care professionals. Such functions can improve the quality and decrease the costs of health care [START_REF] Aggarwal | Managing and Mining Sensor Data, Chapter: Social Sensing[END_REF].

Furthermore, in the domain of personal wellness, heart rate monitors and fitness tracking devices enable users to share information about their exercise routines and training progress with other people. Today, consumers who are interested in monitoring their health and wellbeing have access to several commercial applications, devices, and personal solutions for fitness, sports activities, and wellbeing [START_REF] Sellen | Do life-logging technologies support memory for the past?: an experimental study using sensecam[END_REF].

Given this plethora of options, people utilize technological tools to monitor their health in various ways. For example, 'lifelogging' is an emerging concept referring to the continuous recording of physiological data and everyday life activities. Similarly, the 'quantified self' is a movement incorporating technology into the data acquisition of inputs (e.g. food consumed, quality of surrounding air), states (e.g. mood, arousal, blood oxygen levels), and performance (e.g. mental and physical) from users' daily lives. These trends of self-monitoring and self-sensing make use of wearable sensors (electrocardiogram, electroencephalogram, video, etc.) and wearable computing [START_REF] Neff | Essential Knowledge series[END_REF].

While the use of sensor technologies creates significant value for both individuals and society, this value could be increased through further advancements. For example, transmitting blood glucose levels or physiological data through sensors represents only one-way web connectivity. Sensor devices could play an expanded role if, first, their information they gather could be combined for realistic contextual perception and, second, if the sensor devices themselves could be empowered with basic interactive capabilities for consuming and producing meaningful information.

In this paper, we propose the concept of the Social Sensor Web. We define the Social Sensor Web as the interplay between sensors and the social web phenomenon, such that sensors elaborate on contextual space in order to determine an effective interaction approach. The essence of this concept sheds light on the dual connectivity between sensors and the web, such that sensors take on the form of social entities [START_REF] Oinas-Kukkonen | Humanizing the Web, Change and Social Innovation[END_REF]. For example, information from the social web can be used to predict the spread of flu; this information could be refined and transmitted to users (e.g. to suggest changes in their workout programs). Thus, social sensor technologies could be used in the healthcare sector to design more efficient interventions or to persuade people to develop and sustain healthier lifestyles.

We present the results of two workshops, in which interdisciplinary groups of experts discussed and assessed the state of the art related to social sensor technologies and identified the most prospective applications of social sensors. Based on the findings of these workshops, we developed the conceptual framework of the social sensor web. Further, by setting a research agenda, the paper contributes to the research on social sensor technologies; its framework can serve as a guide for designing future applications based on the collaboration of sensors and the social web.

Context-aware Sensors in the Social Web

Sensors that measure physical quantities, such as pressure or light, have grown increasingly pervasive. Smartphones and mobile devices typically feature sensor technologies (e.g. touch screens), which collect different types of data from changes to the environment or an object (e.g. temperature, vibrations, or movement) [START_REF]Sensors: Sensors, convert the measurement into a signal[END_REF].

As sensor technologies have developed, people have changed their ways of using the Internet. Due to the large-scale adoption of social media and online social networking, the Internet has become a social space, to the extent that Oinas-Kukkonen and Oinas-Kukkonen [START_REF] Oinas-Kukkonen | Humanizing the Web, Change and Social Innovation[END_REF] proposed the concept of the social web. This idea highlights the role of the web as a social space characterized by connectivity and interactions, both among people and between people and information [START_REF] Oinas-Kukkonen | Humanizing the Web, Change and Social Innovation[END_REF]. Oinas-Kukkonen and Oinas-Kukkonen [START_REF] Oinas-Kukkonen | Humanizing the Web, Change and Social Innovation[END_REF] describe the social web as follows:

The term social web refers to a pattern of thinking in which end-users jointly create or generate much of the content for the web, whereas companies try to harness the end-users with tools with which they can participate and engage themselves in content production and sharing. […] An essential feature of the social web phenomenon is that social web is not only a virtual world; what is remarkable about it is its interconnection with one's own life. Since the majority of people and an increasing share of physical objects have online digital presences, we propose the concept of a "Social Internet of Things" (SIoT). Whereas the Internet of Things focuses on devices that are connected to the Internet, SIoT shifts the emphasis to objects that interact on the web [START_REF] Antonio | The Social Internet of Things": from objects that communicate to objects that socialize in the Internet[END_REF]. Sensors equipped with basic interactive capabilities (e.g. reception and processing capabilities) could interact with other sensors, people, and social web applications.

Aggarwal and Abdelzaher [START_REF] Aggarwal | Social Network Data Analytics, Book Chapter, Integrating Sensors and Social Networks[END_REF] discussed the drivers of integrating sensors with the social web, focusing primarily on social networks. One of the key incentives for users is real-time awareness of others' updates. Such integration could facilitate a better understanding of users' global behaviors. Sensors could also enable measurements of users' environments, thus facilitating context awareness.

Humans' role in this phenomenon is particularly important in terms of data generation and social relations. Although users who use contextual sensors might decrease their self-report data production, their activities will produce more data. Therefore, humans still play a critical role in terms of user-generated activities.

Recent sensor applications have sought to collect data that can be directly associated with individuals' real-time personal information. Some examples of such applications include GPS applications on mobile devices, accelerometers, and location sensors designed to track humans or vehicular traffic. These types of sensors can support a rich variety of applications in which sensor data can be used to model underlying relationships and interactions [START_REF] Aggarwal | Managing and Mining Sensor Data, Chapter: Social Sensing[END_REF].

At an aggregate level, behavior measurements can be used to investigate and predict traffic conditions through collecting and processing massively distributed data (e.g. the collection of vehicular GPS trajectories to develop street maps) [START_REF] Ahmed | A comparison and evaluation of map construction algorithms using vehicle tracking data[END_REF]. Currently, this use of sensor data involves very little, if any, social component. However, the same sensor-generated information can also be used on a real-time, daily basis to avoid traffic congestions. As an example of the viability of this vision, Google has acquired Waze, a company specializing in social mapping techniques. Google plans to use Waze's crowd-sourced data on users' locations to suggest alternate routes to outsmart traffic congestions [START_REF]Google Blog, Google maps and Waze, outsmarting traffic together[END_REF].

Furthermore, an application called City Sense, a subscription that provides information on where the people in a city are through GPS-enabled phones, can be used to plan activities [START_REF]Sensor Networks[END_REF]. For instance, the service can inform a user walking in the city that his/her friends are sitting in a nearby cafeteria. Turning sensor devices into intelligent objects that communicate with users' social network sites can challenge the conventional information flow between a sensor object and the social web.

Networked sensor data have also been applied in social web applications to aid in the detection of earthquakes [START_REF] Kurata | Risk monitoring of buildings with wireless sensor networks[END_REF]. Integrating data from sensor networks into the web would further enhance the web integration of everyday devices and objects, resulting in a giant sensor network and wide-scale connection between the social web and the physical world [START_REF] Oinas-Kukkonen | Humanizing the Web, Change and Social Innovation[END_REF]. In addition, the nomadic nature of the web, which can be accessed from anywhere through mobile devices, requires portable and environmental sensors.

According to O'Reilly and Battelle [START_REF] O'reilly | Web Squared: Web 2.0 Five years on, Web 2.0 Summit[END_REF], the new direction of the web is on a realtime collision course with the physical world. Hence, using information generated by sensors requires real-time context awareness. This is where contextual information developed by sensors can complement information from other sensors by better utilizing their measurements. Schmidt [START_REF] Schmidt | Context-Aware Computing: Context-Awareness, Context-Aware User Interfaces, and Implicit Interaction[END_REF] considers context awareness to be the key enabler for understanding situations in urban environments with ubiquitous devices. The goal of system context awareness is to arrive at a close representation of the user's perception of the surrounding world. Though the user's perceptions are multifaceted and based on human senses, multiple sensors are required to develop contextual awareness close to human perception. This will help the system provide users feedback related to situation-based decision-making. Tamminen et al. [START_REF] Tamminen | Understanding mobile context[END_REF] point out that the social acceptability of a context-aware system is dependent on how well the system fits into the routine processes of everyday life in a society. Therefore, the better a system is able to develop context awareness, the greater its chances succeeding in the race to technological adoption.

To address the issues discussed in this section, we now discuss the workshops held in this study and present the conceptual framework for the Social Sensor Web.

Towards the Social Sensor Web

Study Setting

We collected empirical data from two expert workshops on social sensor technologies, comprising a total of 14 experts from various fields, such as information systems, mobile technologies, and physical education. The participants had diverse cultural backgrounds in terms of gender and countries: Most were PhDs who are actively engaged in research, and half also had industry experience. The workshops were organized and facilitated by the first author. The objective of the workshops was to obtain participants' perceptions regarding social sensors and, ultimately, to develop a conceptual framework of the social sensor web. The purpose of the first session was to familiarize the participants with one another, the research objectives, the focal concepts, and the state of the art of social sensor technologies. To this end, the participants were presented a number of different sensor devices (see Figure 1), which they were then asked to scrutinize and discuss.

The first was FitBit Surge (device A), a fitness wrist device [START_REF]FitBit Products: Surge, One, and Aria[END_REF] with a built-in GPS; continuous heart rate monitoring capabilities; and tracking for daily activities, such as distance, steps taken, sleep pattern, and floors climbed. It synchronizes measurements wirelessly with a smartphone and presents the data in the FitBit app, as shown in the first two screenshots in Figure 2. Additionally, the device displays calls and notifications from the smartphone, as shown in Figure 3A later. The FitBit One (device B) is a limited version [START_REF]FitBit Products: Surge, One, and Aria[END_REF] of the Fitbit Surge; it lacks heart rate monitoring and is used with the FitBit Surge to provide measurement accuracy. The FitBit Aria (device C) is a non-wearable wireless device [START_REF]FitBit Products: Surge, One, and Aria[END_REF] that tracks weight and Body Mass Index (BMI) to determine body fat percentage. The device allows users to analyze weight data in relation to data on calories burned. Alternatively, the FitBit app can be used to manually log diet information, including food plans, water intake, etc., while also displaying all kind of measurements in graphical charts. Spire (device D) is waist-worn [START_REF]Spire Product: Discover your calm[END_REF] device that monitors a user's breathing pattern in real time and assesses stress levels accordingly. It categorizes breathing patterns into three types: "low and uniform" corresponds to a user being calm, "high but uniform" corresponds to focus, and "high and erratic" corresponds to stress. Based on the collected data, the device sends notifications to the user's smartphone. For example, it may suggest that a stressed user needs to relax or encourage a user who has been sitting idle to stretch his/her legs (see Figure 3B). The device synchronizes with a smartphone app, which lets the user set goals and presents statistical analyses of their everyday breathing patterns, as shown in the last screenshot of Figure 2.

Temperature (device E) is a non-wearable device that measures indoor and outdoor temperature using a small sensor probe that needs to be placed outside the window. BiiSafe (device F) is a smartphone accessory [START_REF]BiiSafe Product: More than a key finder[END_REF] that uses a smartphone's GPS via Bluetooth to enable users to share their locations to connect with or alert friends and family and help them find important lost items, such as keys, phones, wallets, etc. It is the only product on the market to combine location sharing, alert/distress functionalities and lost-item finding capabilities. A smartphone (device G), in addition to providing communication services and social web apps, has several sensors, including an accelerometer, a gyroscope, etc.

In addition to these items, the workshop participants were provided access to three weeks of data from the first author's Life Log. This Life Log included personal health measurements and assessments from the use of these state-of-the-art sensors; a calendar for the past three weeks of logged activities such as sport sessions, locations, work meetings and travel; and a mockup of social contacts. The participants were asked to familiarize themselves with the sensor devices and their features and to consider their value and potential, as well as to provide suggestions for additional functionalities that could add value to users. For example, the participants suggested that Spire device notifications, which were currently displayed on the smartphone (Figure 3B), could additionally be displayed on the device itself, as in the Fitbit Surge device (Figure 3A).

In the second workshop, the same participants were divided into three groups. Whereas the purpose of the first workshop was to familiarize participants with stateof-the-art technologies, the second workshop encouraged participants to think "outside of the box" regarding sensor-based solutions not limited to the presented sensors. In consultation with the participants, the use scenarios were created to target three different domains: healthcare monitoring, fitness and wellness, and social interaction. The participants worked in teams for an hour, collaborating on how stateof-the-art sensors could provide effective solutions to the use scenarios. The scenarios ranged from monitoring the elderly at home, to child safety, to decreasing stress through personalized activities, to keeping in touch with family and friends.

The participants then presented their solutions and identified their core solution concepts. The teams' work was discussed, and remarks were noted, merged, and deliberated for the rest of the workshop. Ultimately, a consensus was reached; this consensus formed the dimensions of the framework.

The Framework

The following are quotes from the participants during their collaboration efforts in the second workshop:

One of the exciting aspects of sensors is their ability to work continuously and all the time, whether it's controlling traffic signals or monitoring my health. -Participant 2

It is great that sensors can know more about me in order to help me, but it scares me to think that this information, unless controlled, might get out of the circle of people I trust. -Participant 5

I think, no matter how much information can generate, it still needs to be taken into account with artificial intelligence in parallel to precisely identify real-life context issues and provide solutions accordingly. -Participant 11

Our conceptual framework of the Social Sensor Web is based on the participants' consensus and feedback, which can serve as guidance for designing future applications based on the combination of sensors and the social web. The framework contains four horizontal dimensions: keep alive monitoring, context awareness, interaction intelligence, and the personal social web (presented in Figure 4). These four horizontal dimensions are connected via the fifth, vertical dimension: dual connectivity. The concept of monitoring serves as a basis for contextual awareness in systems, which is critical for determining suitable interaction approaches. Social interactions towards a target must take place within users' own social web. Within this exemplary system, dual connectivity allows the dimensions to coexist and work together in an effective way. Further sections elaborate on each dimension's importance and potential.

Dual Connectivity

The conventional information flow between a sensor and the social web has traditionally been one-way, meaning information extraction or placement: either sensor data are shared on the social web or a request is sent from the sensors and information is retrieved from the social web. Both cases require the user of the sensor device to initiate the information flow. Though this certainly serves a purpose, it involves minimal intelligence. A more sophisticated sensor could automatically invoke the social web to collect relevant information that could influence the user. Alternatively, the social web could serve as the initiator and provide useful information to the sensor [START_REF] Oinas-Kukkonen | Humanizing the Web, Change and Social Innovation[END_REF]. The dual connection perspective presented in Figure 5A can completely change the way we think about new solutions of information flow. For example, consider an individual's daily fitness workout, which is planned through a sensor that is a part of the social web. With one-way connectivity, workout data are shared on the social web. However, let us assume that a flu is spreading throughout the user's region and a local hospital shares information on the social web. If the social web were able to share this information with the sensor, this would allow the sensor to modify the user's workout to address the risk of flue. This is a clear case of a dual connection between sensors and the social web, in which each can invoke the other. For instance, in Figure 5B, a notification is displayed to the user depicting information gathered from the social web by the sensor device, and the user can use the device's left button to let a nearby friend know of his/her proximity.

Keep Alive Monitoring

One of the key benefits of technology is its ability to keep working to serve its purpose 24/7. Increasing costs of healthcare are a major problem [START_REF] Orszag | The challenge of rising healthcare costs -A view from the congressional budget office[END_REF]. Remote health monitoring provides not only a way to address these rising costs but also opportunities to monitor petty health issues, which can turn into major health problems over time [START_REF] Lymberis | Advanced Wearable Health Systems and Applications -Research and Development Efforts in the European Union[END_REF]. On a technical level, this has been possible due to the development of wireless devices and sensing equipment [START_REF] Leijdekkers | Smart Homecare System for Health Telemonitoring[END_REF]. Many research groups are investigating novel and cheaper ways to support healthcare issues related to diverse groups, including the elderly and disabled, chronic disease patients, and sports enthusiasts [21].

To achieve continuous health and wellbeing management, monitoring and timely interventions are extremely important. For sensor-based systems to become instruments of continuity and control for end users, monitoring must remain alive for extended periods [START_REF] Biswas | Health and wellness monitoring through wearable and ambient sensors: exemplars from home-based care of elderly with mild dementia[END_REF][START_REF] Loe | Doing it my way: old women, technology and wellbeing[END_REF]. This notion of keep-alive monitoring, which provides information on users' health and surrounding contexts at all times, can only fulfill its potential if the information can help in identifying threat-related information patterns. This is only possible if such information is reliable and continuously monitored.

Power consumption in these technologically advanced devices remains a concern. As a precaution, these boundary conditions should, by default, be considered within the overall monitoring scheme, where a sensor's power consumption is monitored. According to Satyanarayanan [START_REF] Satyanarayanan | Pervasive computing: vision and challenges, Personal Communications[END_REF], the monitoring of futuristic sensing devices should make use of smart spaces and involve minimal user distraction; this will allow them to achieve both user satisfaction and their goals simultaneously. As Mark Weiser [START_REF] Weiser | The computer for the 21st century[END_REF] stated: "The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it."

Context Awareness

The value of "context" in the field of human-computer interaction has been widely acknowledged, particularly as the use of sensors to obtain context information has been widely practiced in the domains of robotics and machine vision [START_REF] Schmidt | There is more to context than location[END_REF]. Recently, the concept of context has received growing attention due to developments in mobile computing, which have led to the emergence of pervasive computing [START_REF] Schmidt | Context-Aware Computing: Context-Awareness, Context-Aware User Interfaces, and Implicit Interaction[END_REF]. Context refers to a description of facts that add meaning. This is best described by Dey [START_REF] Dey | Understanding and Using Context[END_REF] as, "any information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is considered relevant to the interaction between a user and an application, including the user and applications themselves."

Knowledge of both the physical environment and the situational environment is important. Schmidt [START_REF] Schmidt | Context-Aware Computing: Context-Awareness, Context-Aware User Interfaces, and Implicit Interaction[END_REF] presented a model for the concept of context, which is a context feature space that distinguishes context related to human factors from context related to the physical environment over time. The human factor categories rely on user information, biosensor readings, the user's social environment (e.g. social interaction), and the user's tasks (e.g. goals). The model's physical environment aspects also hold importance (e.g. tracking location and indoor/outdoor temperature).

One distinguishable feature of wearable computing is awareness of both a user and his/her environment. For instance, a heart rate measurement of 90 beats per minute (bpm) is considered to be average; however, for an individual whose average heart rate is 76 bpm, this is high and a possible cause for alarm. Here information revealing that the elevated heart rate is caused by jogging makes a difference.

Location is the most commonly used context information in present technology. However, detailed and reliable context awareness of various parameters would allow systems to make proper judgments concerning not only providing context-based services but also automatically executing these services for an enhanced user experience. Active context awareness, in which a service or application automatically adapts to discovered context through changes to its behavior, is required [START_REF] Chen | Data-centric middleware for context-aware pervasive computing[END_REF]. Santos et al. [START_REF] Santos | Providing context for mobile and social networking applications[END_REF] summarized several system prototypes for context identification that support social networking services.

Interaction Intelligence

This dimension is the nucleus of the decision-making process based on contextual awareness. In its most simple form, interaction can comprise the act of notifying some social contact or user of the nature of a context. The type of interaction is dependent on the system domain and the type of contact.

In real-life scenarios, actions taken by a system can be proactive, preventive, or reactive, depending on the goals of the system or the problem domain. For example, in the case of healthcare, proactive measures or actions could include suggestions to eat healthy, while preventive actions could include specific suggestions such as to avoid obesity. Finally, reactive actions could correspond to emergency situations, such as a heart attack. For each of these actions, further decision-making on the part of the target recipient is needed. For instance, information related to personal fitness goals may be transmitted to the user or the trainer; the location of a kid in emergency care may be transmitted to parents or teachers; and, in the case of the elderly, information on heart issues may be transmitted to a caretaker or a hospital.

Personal Social Web

A user's personal social web comprises the user's network connections. The increased use of these connections through social web applications has increased people's comfort working with real-life social network connections at the technology level. Figure 6 presents a conceptual illustration of a personal social web as a network. It is common for users to create logical groups of the connections in their own networks (e.g. family, friends, or work colleagues), typically based on static parameters. However, a personal network could also be divided into logical groups based on dynamic parameters, such as current location. Engagement with a dynamic social network based on, for example, location could benefit from the use of sensors that are part of the network in real time. In a dance group, meaningful information generated from sensors and the dynamic dance-group social web regarding the participants' wellness and the class environment can help teachers perform better. In school, a child's school sub-network has priority in case of an emergency. The logical groupings of an individual's personal social web can be utilized to achieve social interaction targets based on the contextual nature of the situation.

The purpose of the study was to develop a conceptual framework for Social Sensor Web. Recent increases in lifestyle mobility increase the need for context-aware solutions based on sensor technologies. In practice, contextual awareness, when combined with the social web and sensor data, creates large volumes of data that need to be assessed. This information is collected through various sources; then, an accurate context is developed, and a meaningful result is given to the user in a presentable way. To achieve this, not only does the stream of information need to be intelligently processed, but, on a technological level, physical user interfaces (UIs) need to be dynamically advanced. Figure 7 shows the inherent concept behind this part of discussion. The user is considered to be the center of the information hierarchy, and streams of information are expected from the user's Social Sensor Web. In the current state-of-the-art, as well as in the workshops, sensors (right in Figure 7) are primarily connected to users' smartphones for wireless synchronization. However, for this user, the FitBit Surge is the main interaction device. The sensor's dynamic interface emphasizes the information that is most useful to the user based on a meaningful analysis of data from all streams. The streams of information from all channels are necessary to develop contextual awareness in the smartphone. This involves extensive real time data analysis on the smartphone, while simultaneously emphasizing the need for intelligent UIs in sensor devices like the FitBit Surge.

Insufficient protection of users' private information is a major security risk related to the extensive use of sensor-generated information. Mancini et al. [START_REF] Mancini | From spaces to places: Emerging contexts in mobile privacy[END_REF] proposed that the investigation of mobile privacy requires a diversified approach, in which crossinterpretations of data from complementary studies should be mandatory. Mobile privacy issues are difficult to study and poorly understood because direct observation methods are typically intrusive to study settings. On the other hand, exploiting user information is critical to success in proactivity, self-tuning, and behavioral change. Greater dependence on pervasive computing systems makes users aware of their own movements, behavior patterns, and habits [START_REF] Satyanarayanan | Pervasive computing: vision and challenges, Personal Communications[END_REF]. However, Patil et al. [START_REF] Patil | Reasons, Rewards, Regrets: Privacy Considerations in Location Sharing as an Interactive Practice[END_REF] found that the motivations for using location sharing data were connecting and coordinating within own circles to project an image of 'checking in' and receiving rewards.

From a critical perspective, sensors can be noisy, and machine-learning algorithms are not always accurate. The concept is too technology-centric, and the expectation of exposing users to extensive physiological monitoring is an issue. Morozov [START_REF] Berry | Critical Theory and the Digital[END_REF] gives a detailed and interesting perspective of the nonsense raised by the advent of the Internet, the rise of the social web, and the smart gadgets that are making humans "dumb." Further, David Berry, a critical theorist [START_REF] Morozov | To save everything, click here: Technology, solutionism, and the urge to fix problems that don't exist[END_REF] has pointed out that conceptualizing the world of digital media can have implications on society norms.

Accurate context detection is a challenging problem, as mentioned by Paul Dourish in his book, Where the Action Is [START_REF] Dourish | Where the Action is: The foundations of Embodied Interaction[END_REF]. Many assumptions might fail to hold true in challenging real-world situations. However, the pace at which the technology is progressing, the proposed framework presents an opportunity to be better prepared for future technology utilization. In this regard, it is essential for future research to focus on the robustness of the framework dimensions, and the links among these dimensions should be thoroughly studied.

The insights from our workshops suggest that the determination of context through sensors is emerging. The framework may have been limited by the participants' cultural backgrounds, professions, and the provided sensors, which may have introduced bias variables. The concept of a dynamic interface to give simple and reliable information is another issue in terms of user experience. In particular, content provided to a user needs to be credible and filtered in a such a way that objectivity and information value needs are maintained and the user's needs are addressed. This, in turn, calls for understanding the ethical implications related to social sensor technologies. Interestingly, however, ethical aspects have not been extensively discussed in relation to persuasive systems [START_REF] Torning | Persuasive system design: state of the art and future directions[END_REF].

Finally, our findings are subject to limitations which offer avenues for future research. First, our research approach was highly exploratory. Hence, additional research in the area is needed to increase the trustworthiness of the results and their interpretations. Second, in order to keep the scope of the study manageable, several important issues, such as security, privacy, trust, and user experience were excluded from our investigation. We thus suggest future research covering these areas.

Fig. 1 .

 1 Fig. 1. State-of-the-art sensors and technologies.

Fig. 2 .

 2 Fig. 2. Synchronization and presentation of physiological data.

Fig. 3 .

 3 Fig. 3. FitBit Surge display and Spire notifications on the smartphone.

Fig. 4 .

 4 Fig. 4. The Conceptual Framework of Social Sensor Web.

Fig. 5 .

 5 Fig. 5. Dual Connectivity.

Fig. 6 .

 6 Fig. 6. Personal Social Web.

Fig. 7 .

 7 Fig. 7. Dynamic Interface.