Peter Teufl
email: peter.teufl@iaik.tugraz.at

Stefan Kraxberger
email: stefan.kraxberger@iaik.tugraz.at

Extracting Semantic Knowledge from Twitter

Keywords: Semantic Patterns, Twitter Mining, e-Participation, Semantic Analysis, Trend Analysis, Semantic Search, Machine Learning, Social Network Analysis

Twitter is the second largest social network after Facebook and currently 140 millions Tweets are posted on average each day. Tweets are messages with a maximum number of 140 characters and cover all imaginable stories ranging from simple activity updates over news coverage to opinions on arbitrary topics. In this work we argue that Twitter is a valuable data source for e-Participation related projects and describe other domains were Twitter has already been used. We then focus on our own semantic-analysis framework based on our previously introduced Semantic Patterns concept. In order to highlight the benefits of semantic knowledge extraction for Twitter related e-Participation projects, we apply the presented technique to Tweets covering the protests in Egypt starting at January 25 th and resulting in the ousting of Hosni Mubarak on February 11 th 2011. Based on these results and the lessons learned from previous knowledge extraction tasks, we identify key requirements for extracting semantic knowledge from Twitter.

Introduction

A blog post from Twitter1 reveals numbers that give us an impression of this social network that turned five years old in March 2011. Twitter states that at each day during the month before the blog entry an average of 140 million Tweets were posted and that 460.000 user accounts were added daily. While Twitter does not mention the current number of users, their latest statistics were released in June 2010 and stated that there were 190 million2 users at this time. When comparing this with the 460.000 user accounts added per day, we can assume that Twitter has reached more than 200 million total users. This makes it the second largest social network after Facebook, which had reached the 500 million mark in July 2010.

The messages posted on Twitter are called Tweets and are comprised of maximum 140 characters. This is roughly similar to the 160 character limitation of the well established text messages (SMS) sent from our cell phones. Although, this limit seems to be rather short, it comes with a significant advantage -a user who posts a Tweet must carefully choose the terms and thereby compress the original information. This compression simplifies the manual and automated analysis of Tweets.

There are some basic concepts that are important for understanding how information is conveyed via the posted Tweets. Followers are persons that are interested in the Tweets of a specific user, whereas friends are other persons followed by a given user. Retweeting is the process of forwarding interesting Tweets to one's followers. Another important concept is the employment of Hashtags, which are arbitrary terms chosen by the users and preceded with a #. They are intended for the simple categorization of Tweets and allow the real-time monitoring of specific topics. Current examples are "#Libya", "#Egypt" or "#Syria". Since these hashtags are chosen by the community, they represent a self organizing process that evolves according to principles described by Halpin et al. [START_REF] Halpin | The complex dynamics of collaborative tagging[END_REF]. In general, the information conveyed by Tweets covers all aspects of our society ranging from simple daily activities, over news coverage to discussions and opinions about arbitrary topics. Due to the facts that most of these Tweets are publicly available, that there is a huge user base and that all information must be compressed to 140 characters, Twitter represents a valuable resource for knowledge-mining. Twitter has already been called The SMS of the internet3 , but one could even go further and describe it as The Online Presence of our Society.

During the last three years we have focused on the development of a framework for the automated extraction of semantic knowledge. This framework is based on the a new concept called Semantic Patterns that we have already successfully deployed in a broad area of domains. Here we apply the framework to data extracted from Twitter.

The remainder of this work is organized as follows: In the next section we cover various Twitter related research projects that highlight the wide range of possible applications. We then give an introduction to the employed Semantic Patterns concept and present its key advantages. In the subsequent section, we address how Tweets can be extracted from Twitter, and finally we demonstrate the technique by analyzing the evolution of Tweets relevant to the Egyptian revolution. Due to our broad application of this technique, we are able to present the learned lessons which lead us to several key requirements that are also valid for e-Participation projects.

Related ideas and e-Participation use cases

Due to the abundance of data covering a wide range of topics, Twitter is a wealth for knowledge mining. The most obvious source for information is the text (including the hashtags) contained within the Tweets. For the analysis of this information well-know processes from NLP, machine learning and statistics play an important role. However, in addition to the raw text message, Tweets carry other metadata that enables the extraction of additional knowledge. Apart from the timestamp that allows us to follow trends and detect emerging topics, the retweeting and reply features for Tweets, and the follower information of a user enable us to learn how information is distributed over the whole social network. Since the members of social networks and their interaction represent nodes and links within a graph, well-know graph analyses can be utilized for knowledge extraction [START_REF] Ediger | Massive Social Network Analysis: Mining Twitter for Social Good[END_REF].

Apart from knowledge mining related tasks, which could play an important role in e-Participation related projects, we must not forget the functionality of the service itself and how it is used around the world. The recent developments in Tunisia, Egypt and Libya show that social networks were extensively used by the population to communicate, spread news, and organize groups and protests. Although the regimes in these countries tried to block and manipulate the information spread via social networks4 , the processes can still be seen as a major self organizing e-Participation initiative. An earlier example is the utilization of Twitter during the 2009 election in Iran [START_REF] Burns | Twitter Free Iran: an Evaluation of Twitter's Role in Public Diplomacy and Information Operations in Iran's 2009 Election Crisis[END_REF].

The basic idea of sentiment analysis is to extract data from Twitter and determine the attitudes towards various subjects and their evolution over time. Due to the wide range of data on Twitter these subjects include things such as products or places, public figures such as politicians or actors, or entities such as companies or discussions about recent events. A good example for the latter one is the discussion about nuclear energy in Germany after the recents events in Fukoshima. Specific examples from current literature are the general discussion about Twitter and sentiment analysis by Go et. al [START_REF] Go | Twitter Sentiment Analysis[END_REF], the sentiment analysis of popular terms by Bifet et. al [START_REF] Bifet | Sentiment Knowledge Discovery in Twitter Streaming Data[END_REF], and the prediction of election outcomes in the paper by Tumasjan et. al [START_REF] Tumasjan | Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment[END_REF].

Another application-field is the analysis of health-related information. While Quincey et al. [START_REF] Quincey | The Potential of Twitter for Early Warning and Outbreak Detection[END_REF] discuss the possible application of Twitter for early warning and the detection of pandemics, Rittermann et. al focus on a specific one -the Swine flu pandemic [START_REF] Ritterman | Using Prediction Markets and Twitter to Predict a Swine Flu Pandemic[END_REF]. Another paper within the health sector by Scanfeld et al. analyzes the over-use of antibiotics by extracting information from Twitter [START_REF] Scanfeld | Dissemination of health information through social networks: Twitter and antibiotics[END_REF]. Obviously, another research field is related to the detection of breaking news events or following trends on Twitter [START_REF] Okazaki | Semantic Twitter: Analyzing Tweets for Real-Time Event Notification[END_REF], [START_REF] Phuvipadawat | Breaking News Detection and Tracking in Twitter[END_REF]. Twitter data has also been used in the financial sector where Wolfram et al. discuss the possibility to use Tweets for modeling the stock market [START_REF] Wolfram | Modelling the Stock Market using Twitter[END_REF]. There has also been an application where the information about published and spread Tweets is used for earthquake detection [START_REF] Earle | Earthquake Twitter[END_REF].

This broad range of applications highlights that Twitter is a vital source of information for all kind of data and should definitely be considered in e-Participation related projects.

Semantic Patterns (SemPs)

The Semantic Patterns (SemPs) technique was developed during the last three years and initially applied to data extracted from the Austrian e-Participation project Mitmachen [START_REF] Teufl | Automated Analysis of e-Participation Data by Utilizing Associative Networks, Spreading Activation and Unsupervised Learning[END_REF]. In order to identify shortcomings and to improve and extend the method, it was then applied to other domains. These domains include the analysis of malicious code, the correlation of events within Intrusion Detection Systems (IDS), the semantic analysis of RDF data, the investigation of privacy issues within WiFi networks and most recently an automated analysis of metadata extracted from 130.000 applications within the Android market. The application in such heterogenous domains helped us to gain a much better understanding which allowed us to improve the initial technique and integrate it into a Java framework that can be used for the analysis of arbitrary data. Since the in-depth description of the complete technique would go beyond this work, we refer the reader to the previously mentioned publications (especially [START_REF] Lackner | User Tracking based on Behavioral Fingerprints[END_REF]) for further details.

The main idea behind this technique is to transform a raw data vector containing arbitrary symbolic and real valued features into a pattern, which forms the basis for a wide range of subsequent analyses. This transformation process is depicted in Figure 1 and shows several processing steps that 1. extract terms (nouns, adjectives and verbs), hashtags and timestamps from Tweets and store them as nodes within a semantic network, 2. represent relations between terms, hashtags and timestamps, and the strength of these relations (e.g. defined by the number of co-occurences within a Tweet) as weighted links within this network, 3. apply spreading activation techniques to Tweets, which stimulates the network and spreads the activation of selected nodes according to their links to other regions of the network, 4. and finally extract the activation values for each Tweet from the network and store them within a vector that we call the Semantic Pattern.

The generated patterns represent the activation values of different regions within the network that are activated due to different input stimuli (e.g. the hashtag "#Egypt" and the term "protest"). The distance between two patterns and therefore their similarity can be calculated by the cosine-similarity distance measure. This distance is the basis for a wide range of standard machine learning algorithms.

The key advantages of SemPs are the employment of a single, easy-tointerpret model that eliminates the need for complex setups in different domains, and the ability to easily add analysis procedures. Another key advantage is that semantic relations between feature values and not the raw values themselves are stored in the patterns. This removes the need for normalization techniques and enables a straight forward combination of symbolic and real valued features5 .

Accessing Twitter

Although Twitter enables users to communicate via private messages, most of the Tweets are posted to public profiles accessible via a web browser. In addition Twitter offers simple and advanced search interfaces for extracting desired information. The results are chronologically sorted and limited to 1500 Tweets. Especially, when Tweets about hot topics are retrieved, this limit is reached within a few minutes. In addition the real-time results of search queries can also be displayed in widgets that can be added to arbitrary websites. This feature is often used by news sites that display further information from Twitter when covering breaking news events. Furthermore, Twitter offers a streaming API that allows access to the continuous stream of Tweets. This API is the only way to retrieve data which spans a larger timeframe, but comes with the disadvantage that information must already be captured during the unfolding of the monitored events. Therefore, there are several third party services that offer various data to paying customers.

There is also a free alternative that allows the retrieval of Tweets six month back -Google realtime 6 . It provides real time search results for social network related data. Although it covers various sources such as Facebook or Twitter, an inspection of the results yields that Twitter is the main data source. The service does not offer any APIs which limits its possible applications. However, since it provides a convenient way to access older Tweets, it still is an interesting alternative. An additional advantage comes with the pre-processing Google applies to the retrieved results. Although there are no specific details on these methods, an empirical analysis suggests that only unique and relevant Tweets are extracted. This could also be a disadvantage for certain analyses but fitted perfectly for the demonstration presented in this paper.

The revolution in Egypt

In this section we demonstrate how the SemPs concept can be applied to data extracted from Twitter. Before going into details, we bring the employed knowledge extraction methods into relation with possible e-Participation related projects based on Twitter data. These projects can be assigned to two main categories: The first one is related to projects that ask users to express their opinion on a given topic on Twitter. The topics could simply be separated from other Twitter data by introducing special hashtags. The second category includes projects that use existing Twitter data in order to extract information about arbitrary topics. Some specific examples for such topics are the attitude towards nuclear energy within the last 6 months, the sentiments about infrastructure projects within given regions or the attitude towards political decisions. For both categories we need to extract that Tweets related to the specific topic and use them as basis for subsequent knowledge extraction methods that allow us to draw conclusions on the expressed opinions. Typically, we cannot assume that a-priori knowledge about the analyzed data is available, therefore the applied knowledge extraction methods must enable us to get a good overview of the data and learn key facts before more in-depth analyses can be applied. The SemPs concept helps us to achieve this and to avoid the typical problems of setting up domain-specific knowledge extraction methods by using a generic model for a wide range of analyses techniques.

In order to demonstrate the framework and identify key requirements the remainder of this section covers the analysis of Twitter data related to the Egyptian revolution. The data-set was extracted from Google realtime 7 and covers Tweets from January 24 th to February 12 th 2011. The Tweets have been pre-processed by applying various NLP techniques such as stop-word removal, phrase chunking, part-of-speech (POS) tagging and inflection. Subsequently, the Tweets were parsed and the following three features and their corresponding feature values were extracted: the timestamp of the Tweet, the tokens within the Tweet (nouns, verbs, adjectives) and finally the hashtags. The extracted features and their feature values for each Tweet are the basis for generating the SemPs according to the process described in Section 3.

The analyzed data-set was chosen for two main reasons: First, the Egyptian revolution and similar events were also called the social network revolutions since information exchange was carried out over such networks. Therefore these revolutions could be assigned to a special category of e-Participation projects. This was also recognized by the Egyptian government which shutdown the Internet access in response. Secondly, the Egyptian revolution was covered extensively in the news which gives us detailed background knowledge that allows us to verify the results of the framework. However -as previously assumed -in the general case such a-priori knowledge is not available. Therefore, a knowledge extraction framework must fulfill these three key requirements:

Analyses layers: The employed algorithms should be able to extract knowledge that allows for an overview of the analyzed data and starting from there going into fine details in subsequent analyses. Further requirements are that the algorithms yield significant results by eliminating noise, and allow for an easy interpretation of these results. If these requirements can be fulfilled with a single model, then the further extension and addition of complex analyses is easier than the application of different algorithms for different tasks. In order to gain a superficial view on the analyzed data-set, the most important Tweets must be extracted automatically. The exact definition of important depends on factors like the existing a-priori knowledge of the data-set, the processed data or the desired knowledge. In typical scenarios this comes down to a certain compression or categorization of data. One key technological component here is the application of clustering algorithms. When an overview about the underlying data-set was gained, it is necessary that the analysis framework allows the user to use the superficial analyses as a starting point and go into specific details from there.

Representation: Once data has been extracted, it must be represented to the user. This data representation is a key component, since a bad choice in this area leads to a confusion of the user and cancels the benefits of even the best data extraction algorithms. The appropriate representation depends on the analyzed data, which in the Twitter domain could be a combination or a subset of data such as text, timestamps, or geo-locations. In addition the analyzed data could be of a static or dynamic nature (e.g. an event at a certain timestamp vs. a time frame spanning several months). The representation methods range from simple results lists, over visualizations of time series to maps that either show static or dynamic content.

User interface: The conducted analyses and the representations of the extracted data need to be accessed via a convenient user interface, which is the third key component. This interface must allow to make a seamless transition from layer to layer without the need to execute complex operations. The Analyses layers component is already covered with the Semantic Pattern concept. However, we are still in the progress of integrating meaningful visualizations, especially for dynamic data, and improving the user interface.

Getting an overview

For the analyzed Tweets we assume that a-priori knowledge is not available. Therefore, it is crucial that the analysis framework enables the user to gain a quick overview of the data. A common method here is to apply unsupervised learning, or more specific, clustering algorithms that automatically detect categories within the data. Due to the transformation of raw feature values into SemPs we are able to directly apply such algorithms. Semantic clustering can be applied to patterns of complete Tweets or to patterns of single feature values (tokens, hashtags, time-stamps) within the Tweets. While the first case is used to group Tweets covering the same topic into a cluster, the second case can be used to learn more about semantically related feature values (e.g. timestamps for similar events, terms that are used within the same semantic context). In our example this results in the extraction of clusters that cover various topics within the Egyptian revolution such as the protests on Tahrir square, the blocking of the Internet and mobile phone services, the arrest of journalists, or the reported violence during the protests. These clusters help us to gain an overview of the whole data-set and are the basis for further more specific analyses.

The inclusion of the timestamp also enables us to apply clustering algorithms to time series that are generated for Tweets or terms due to semantic changes over a given time frame. When sorting these clusters according to their strongest activity within the time series, we can automatically extract relevant events and arrange them in a timeline. For the analyzed data this includes the following chronologically sorted topics: the accusation of milititants for the bombing of a chruch in December 2010 (2011/01/23), the starting protests (2011/01/25), the following arrests and clashes with the police (2011/01/27), the shutdown of the Internet (2011/01/28), the arresting of journalists, the evacuation of U.S. citizens (2011/02/02), the involvement of the Egyptian army, the final resignation of Hosni Mubarak (2011/02/11), the appointment of an interim military council (2011/02/11) and the international reactions to the revolution.

Semantic relations of terms, timestamps and hashtags

One key aspect of any analysis is the consideration of semantic relations stored within the raw-dataset. This is highlighted via a Tweet that was extracted via the previously mentioned timeline analysis: "After access is shut down, some ponder if Internet access is a basic human right. 2011/01/29". By searching for semantically related Tweets we are able to find more about the incident and other related events. Examples for retrieved Tweets are "Apparently switching off Twitter is becoming the standard procedure of every country facing social unrest. 2011/01/25" and "RT @sharifkouddous I will eventually lose all communication here. But I will be out in the streets tomorrow. 2011/02/01". Although these Tweets do not share common terms they are semantically related -meaning they describe similar topics. These relations are domain-specific and typically cannot be transferred to another domain. However, it is still possible to include other domain-invariant information from other knowledge sources (e.g. details about Egypt extracted from DBpedia8 ) that could be used to augment domainspecific semantic relations.

While the semantic relations between terms, Tweets and topics are the most obvious, the concept can be extended to arbitrary data. For this analysis, we also take hashtags and timestamps of Tweets into consideration and link them to the terms within the Tweets. Another Tweet extracted from the timeline analysis reports the following: "Live footage shows Egypt's army vehicles deploy among protesters at scene of violence in Tahrir square -Al Arabiya TV. 2011/02/03".

By searching for the associated timestamp "2011-2-3-0" we can also retrieve other timestamps that are semantically related due to similar events. An example is highlighted by an event that happened on "2011-2-5-12". Here the following Tweet can be retrieved "Army removing burnt Police vehicle from Tahrir Sq -dark symbol for protestors. 2011/02/05". Since for both events similar terms have been used, the corresponding timestamp features are therefore semantically related. The incorporation of this semantic knowledge is the core idea behind the SemPs concept and plays a key role for all analyses.

Going into details

The generated semantic network allows us to extract information about the tokens, hashtags and timestamps stored within the analyzed Tweets. The links and their weight represent the strength of the relations between these features. By using one or more feature values as input we can easily find semantically related information. This information also enables search queries that go beyond simple term matching:

The first example retrieves Tweets that are semantically related to the term "protest". Obviously, Tweets like "Egypt cracks down on mass protests as Mubarak dissolves government. 2011/1/29" or "Egypt anti-government protests start for 3 rd day. 2011/1/27" are the best matching results since they contain the term themselves. Such Tweets could also be retrieved with simple term-matching techniques. However, Tweets like "Egypt unrest enters third day, El Baradei to return. 2011/01/27" and "Journalists now have to register with #Egypt's Ministry of Information if they wish to enter Tahrir, not good. 2011/02/07" can also be retrieved. They do not contain "protest" but other semantically related terms -"Tahrir", "unrest", and "El Baradei".

The second example executes a search query by using the timestamp "2011-1-25-12". At this time the mass protests in Egypt have started. The best matching Tweets are those that were written at that time (e.g. "Huge protest in Egypt right now as thousands in streets trying to topple Gov't like Tunisia. 2011/01/25"), but there are also tweets that describe a similar event one day after the first mass protests (timestamp "2011-03-26-0"): "Egypt's Mubarak faces unprecedented protests. Thousands march in the Egyptian capital demanding the end of Hosni. 2011/03/26".

The inclusion of the timestamps for each Tweet enables us to generate "semantic time"-patterns that represent the semantic relevance of each feature and Tweet over the complete timeframe. For this data-set twelve-hour intervals were used, which means that a time pattern has roughly 40 entries. By comparing these patterns, one can find Tweets, terms or hashtags that have a similar development over time. As an example we search for Tweets that are related to the event "Egypt Internet users report major network disruptions. 2011/01/28". The retrieved results have a similar activity over time, but do not need to be otherwise semantically related: "Wikileaks announces it will soon release numerous cables on Egypt. 2011/01/28" (Figure 2(a)), or "Egypt protesters, police brace for day of rage. 2011/01/28". The same procedure can be applied to single feature values, which is highlighted by the example "protest": Other terms that have a similar time-pattern are "police", "tunisia", "government", "people", or "video". Although some of these terms are also semantically related, for this example only the time information was utilized.

Outlook -Twitter and e-Participation

This paper discusses various application domains for data extracted from Twitter, demonstrates our own knowledge-extraction framework for analyzing Twitter data and based on the learned lessons identifies several key issues that need to be taken into consideration. Based on the findings we strongly argue that Twitter should be used in e-Particpiation related projects and highlight this by drawing the following conclusions: First, due to its huge user base and the continuous coverage of arbitrary topics, we see Twitter as The Online Presence of our Society that contains knowledge about arbitrary topics. Second, the automated analysis of Tweets and their carried metadata is vital for the successful extraction of knowledge. Due to the lessons learned from our own analysis framework based on SemPs we identify several key requirements for such a knowledge-extraction framework: The semantic knowledge extracted about an arbitrary topic must be presented in several layers that allow the user to make a seamless transition from a superficial overview to fine-grained analyses that extract semantic information. The meaningful representation of the extracted data has a huge impact on the capability of a user to understand important relations and draw further conclusions. Finally, the user interface must allow the user to make smooth transitions between the various analysis layers and address general requirements for intuitive user interfaces. The final conclusion is that Twitter offers the infrastructure for the discussion of topics for free and has a huge user base. Therefore, the future e-Participation projects should consider the possibility to discuss topics directly on the platform. Although there are several disadvantages compared to specific e-Participation related platforms, we argue that the advantages of the huge user base and the ease-of-use outweigh these shortcomings.

Related to our own framework we conclude, that the SemPs model represents a well-founded basis that can easily be applied to a wide range of applications and due to its structure can further be extended according to future needs (e.g the inclusion of geo-location based data). Currently, the main target of future improvements are not the analyses layers themselves but the employed data representation layers and the user interface.

Represent

  Tweets as patterns that store semantic information and are the basis for all further analyses.

Fig. 1 :

 1 Fig. 1: SemP transformation for the Tweets labeled with T1-T4.

  (a) Stacked graph for the semantic evolution over time of the Tweets "Wikileaks announces it will soon release numerous cables on Egypt. 2011/01/28" (upper graph) and "Egypt Internet users report major network disruptions. 2011/01/28" (lower graph). The peak at "2011-1-28-0" represents the initial shutdown of the Internet connections and and the peak at "2011-2-1-0" represents the shutdown of the last remaining ISP. The y-axis represents the semantic relevance of the Tweets at a given time stamp. (b) Terms and timestamps that are strongly related with "Mubarak" during the revolution. The size of the bars represent the activation values within the semantic network.

Fig. 2 :

 2 Fig. 2: Examples for semantic analyses

http://blog.twitter.com/2011/03/happy-birthday-twitter.html

http://techcrunch.com/2010/06/08/twitter-190-million-users/

http://www.business-standard.com/india/news/swine-flu%5Cs-tweet-tweet-causesonline-flutter/356604/

Attacks on regime critics on Facebook by the Tunisian Goverment: http://www.wired.com/threatlevel/2011/01/tunisia/, Blocking the Internet in Egypt: http://www.nytimes.com/2011/02/21/business/media/21link.html

The data analyzed in this paper only contains symbolic values, but the mixture of symbolic and real values is very typical for other application domains (e.g. the semantic relation between the unemployment rate and an export commodity).

http://www.google.com/realtime

One Google realtime search query with the term "Egypt", was executed. The results were parsed via a Java tool and used as input for the SemPs framework.

http://dbpedia.org


Joachim Åström
email: joachim.astrom@oru.se

Martin Karlsson
email: martin.karlsson@oru.se

Left and right in the blogosphere: Ideological differences in online campaigning

Keywords: Blogging, political parties, election campaigns, political individualism

This article investigates the usage of blogs in electoral campaigns in Sweden, a country characterized by strong political parties and a party centered form of representative democracy. The central argument is that blogs are utilized in different ways by different parties. The empirical analysis based on a survey among over 600 blogging politicians indicates a vast difference in uptake and usage of blogs between the right-and left wing politicians. The results indicate that ideological positions towards individualism and collectivism matters for the practice of blogging.

Introduction

There is little doubt that the internet is capable of transforming the mode and content of mass communication, perhaps most notably in election campaigning. Political aspirants in the United States, such as Bob Dole, Howard Dean and, most recently, Barack Obama, have all made headlines for their creative use of the internet to mobilize voters and generate support. Blogging, as a form of e-participation, is particularly recognized as a practice that allows candidates to engage in campaigns more directly and bypass parties to articulate individual opinions, thoughts, feelings, experiences and identities [START_REF] Wright | Read my day? Communication, campaigning and councillors' blogs[END_REF][START_REF] Coleman | The Blogs and the New Politics of Listening[END_REF]. However, not all countries have nurtured the blogosphere in the same way. Recent research has found that the adoption and use of political blogs, to a large extent, is shaped by institutional setting, i.e. the different roles given to political parties [START_REF] Anstead | Parties, election campaigning, and the Internet: toward a comparative institutional approach[END_REF][START_REF] Zittel | Political representation in the networked society: the Americanisation of European systems of responsible party government[END_REF]. In political systems where major party endorsement is still crucial to winning a seat, there is little incentive for candidates to openly champion divergent positions.

This article aims to investigate the usage of blogs in electoral campaigns in Sweden, a country characterized by strong political parties and a party centered form of representative democracy. This is where the shadow of party hierarchies is among the darkest in Europe. At the same time, internet use is more developed here than in almost any other country. It is a country of partisans as well as pirates. And, perhaps, it is therefore a country in which interesting intra-national differences can be found. The central argument of this article is that blogs are utilized in different ways by different parties. Just as blogging is shaped by how institutions support persons or parties, we propose that political blogging is also shaped by attitudes towards preferential voting, candidate-centered campaigning and, at a more basic level, ideological positions towards individualism and collectivism.

According to Benkler and Shaw [START_REF] Benkler | A Tale of Two Blogospheres: Discursive Practices on the Left and Right[END_REF], prior studies have argued that the left and the right are relatively symmetrical and embody uniform practices. However, few investigations have been conducted on the attitudes of political bloggers, and those that have are focused on small samples of elite bloggers [START_REF] Wright | Read my day? Communication, campaigning and councillors' blogs[END_REF][START_REF] Ekdale | Why blog? (then and now): Exploring the motivations for blogging by popular American political bloggers[END_REF][START_REF] Benkler | A Tale of Two Blogospheres: Discursive Practices on the Left and Right[END_REF]. Since this article draws upon a survey questionnaire of 600 blogging candidates in the run-up to the 2010 Swedish election, it has the potential to uncover new perspectives and knowledge.

The internet and political campaigning

According to most academic observers, the history of political campaigning creates three distinct phases [START_REF] Norris | A virtuous circle: Political communication in post-industrial democracies[END_REF]. The first "premodern", era [START_REF] Norris | A virtuous circle: Political communication in post-industrial democracies[END_REF], was characterized by decentralized and staff intensive campaign activities and high levels of party loyalty among voters. In the wake of changes in the media landscape, campaigning evolved towards a second "modern era" characterized by stronger centralization of campaign activities and a stronger focus on party leaders. In recent decades, a new era of campaigning has arisen. The so-called "post-modern era" of political campaigning refers to a mixture of developments with uncertain outcomes. On the one hand, there are signs that campaigns continue to professionalize, with power increasingly centralized in the hands of party elites. On the other hand, local campaign organizations seem to be experiencing a second spring [START_REF] Norris | A virtuous circle: Political communication in post-industrial democracies[END_REF], and local candidates have started to employ ICTs to run election campaigns, independent of their parties and the traditional media. This ambiguity is highlighted by Zittel [START_REF] Zittel | Lost in Technology? Political Parties and the Online-Campaigns of Constituency Candidates in Germany's Mixed Member Electoral System[END_REF], who offers two competing hypotheses regarding the internet's impact on the structure of election campaigns -one "orthodox", one "revisionist". The former perceives the internet as reinforcing the trend toward professionalized and centralized campaigns, allowing parties to target and mobilize groups of voters in more efficient and direct ways. Content is primarily about party program and party image, even though information on these topics may often be distributed "with a personal face" [START_REF] Zittel | Lost in Technology? Political Parties and the Online-Campaigns of Constituency Candidates in Germany's Mixed Member Electoral System[END_REF]. According to the revisionist view, it is not all that certain that parties are in the driver's seat. Instead, it is argued that the internet is a means for individual candidates to run candidatecentered campaigns, independent of their own parties, attracting as much attention as possible to themselves, rather than to their parties.

Blogs are touted as having the potential to reinvigorate political communication by facilitating decentralization, individualization and interactivity, in line with Zittel's revisionist view. At the same time, an unmistakable tension has arisen between orthodox and revisionist tendencies. According to Wright [START_REF] Wright | Read my day? Communication, campaigning and councillors' blogs[END_REF], "a battle is being fought". On one side are candidates trying to make use of the individualistic blog platform. On the other side are parties, trying to carry out professionalized campaigns, controlling their messages and presenting uniform fronts. Blogging candidates are, thus, implicated in a tug of war between two different cultures.

Politicians live in a world of certainty and tribal loyalty which is at odds with the blogging ethos of open-mindedness and knowledge-sharing. As long as politicians are expected to be never in doubt and ever faithful to catechismic party messages, their blogging efforts are always likely to look more like simulation than authentic selfexpression [START_REF] Coleman | The Blogs and the New Politics of Listening[END_REF].

In explanations of how blogging candidates handle this intricate situation, factors related to institutional setting have been most prominent [START_REF] Anstead | Parties, election campaigning, and the Internet: toward a comparative institutional approach[END_REF][START_REF] Zittel | Political representation in the networked society: the Americanisation of European systems of responsible party government[END_REF]. Much less attention has been directed to the fact that a candidate-versus-party emphasis within parties may affect the extent to which candidates distance themselves from their parties and loosen central party control over their campaigns. This is somewhat surprising, considering that previous research emphasizes party affiliation as one of the most important explanations for members´ support of different principles of representation [START_REF] Esaiasson | Representation from above: members of Parliament and representative democracy in Sweden[END_REF][START_REF] Gilljam | Representationsprinciper i riksdag och kommuner[END_REF] and that this is often explained with reference to ideological differences. Holmberg [START_REF] Holmberg | Riksdagen representerar svenska folket: empiriska studier i representativ demokrati[END_REF], for instance, points to the tradition of collectivism within socialism. For leftist parties, the guiding principle has been party solidarity, while right-wing parties have been influenced by more liberal and individualistic traditions of representation. The left-right axis has also been important when explaining different approaches towards reform proposals that support more candidate-centered electoral systems [START_REF] Möller | The Swedish Election 1998: A Protest Vote and the Birth of a New Political Landscape?[END_REF][START_REF] Nielsen | Ett decennium med personval: erfarenheter och utfall[END_REF]. Again, parties´ positions and arguments have been proven to reflect their ideological views on collectivism and individualism. Nonetheless, theoretical arguments or empirical evidence to support the view that the left and right blogospheres should vary remains in short supply. Instead, prior studies have argued that the left and the right are relatively symmetrical and embody uniform practices [START_REF] Benkler | A Tale of Two Blogospheres: Discursive Practices on the Left and Right[END_REF].

The central argument of this article is that blogs are utilized in different ways by different parties. Just as blogging is shaped by how institutions support persons or parties, we propose that political blogging is shaped by party affiliation and ideological positions on individualism and collectivism. We will also consider that blogging candidates are situated within the blogosphere, as well as the party-sphere, and that the depth of their involvement in these spheres might influence their attitudes and strategies. Along the lines of cultural theory, less party-involvement and more blog-involvement could be expected to increase the probability of individualized campaigning -and vice versa. Another assumption we will test is that innovations such as blogs spread to an increasingly large group only after a period of time. When a phenomenon is new, it is limited to a relatively small group. From a diffusion perspective, we would, therefore, expect that some groups -including young, highlyeducated candidates -are particularly likely to use blogs to individualize their campaigns. nominated by the political parties, and all representatives are organized in party groups. Thus, local and national party organizations are traditionally most influential in the organization of electoral campaigns in Sweden. Although political parties in Sweden, as in most western democracies, have experienced a weakening public support few signs are apparent that the power of political parties in political assemblies is weakening. During the last 40 years, Swedish parties have lost the lion's share of their members, voters have become increasingly mobile between elections and party identification among citizens has strongly decayed [START_REF] Holmberg | Väljare: svenskt väljarbeteende under 50 år[END_REF]. Still, the level of party loyalty among elected representatives has steadily increased during the same period. Almost half of representatives say that they would choose to adhere to party views in a conflict between the (known) views of voters, their own views and the views of their party [START_REF] Gilljam | Representationsprinciper i riksdag och kommuner[END_REF]. At the same time, only 15 percent of voters now say that they identify with a party [START_REF] Holmberg | Väljare: svenskt väljarbeteende under 50 år[END_REF]. The current political climate, thus, resembles the picture painted by Katz and Mair in the middle of the nineties: "The parties are at once stronger, but also more remote; at once more in control, but also less powerful; at once more privileged, but also less legitimate" [START_REF] Katz | How Parties Organize: Change and Adaptation in Party Organization in Western Democracies[END_REF].

In an attempt to revitalize Swedish democracy by strengthening candidates' incentives for seeking personal mandates, a preferential vote was introduced through constitutional reform in the mid-nineties [START_REF] Holmberg | Premiär för personval: forskningsrapporter[END_REF]. The reform was a compromise between the Liberals and Moderates, who supported a stronger individual vote, on the one hand, and the Social Democrats and Left Party, who strongly opposed individual voting, on the other [START_REF] Möller | The Swedish Election 1998: A Protest Vote and the Birth of a New Political Landscape?[END_REF]. The result was an optional preferential vote, allowing voters to make a preferential choice for one candidate on their party's list. If a candidate on the list received enough votes from his or her constituency to reach a pre-set limit (8% for the national parliament and 5% for local and regional assemblies), he or she was put at the top of the party list and got the first mandate won by the party in that constituency. The Swedish system for individual votes has been characterized as a weak system of preferential voting that gives candidates increased, but still limited, incentives to conduct personal campaigns and does not challenge the central position of political parties in Swedish democracy [START_REF] Möller | The Swedish Election 1998: A Protest Vote and the Birth of a New Political Landscape?[END_REF].

The political parties' opinions regarding the preferential vote are strongly polarized. While political representatives from the Left Party and the Social Democrats resist a strengthened preferential voting system and the Greens are only moderately in favor of it, all the parties in the right bloc are strongly in favor of such a reform [START_REF] Gilljam | Politik på hemmaplan. Tiotusen svenska fullmäktigeledamöter tycker om politik och demokrati[END_REF]. Among skeptical politicians on the left, several arguments for limiting the influence of preferential voting have been made; it was thought to Americanize Swedish democracy, threaten the existence of political parties, give campaign financing a decisive role in elections and undermine intra-party democracy, as well as party cohesion [25,29]. Increased opportunities for public control and accountability, as well as greater opportunity to strengthen the relationship between voters and representatives and increase activity among both voters and candidates in election campaigns, were arguments put forward by proponents of a strong preferential vote, especially politicians from the conservative Moderates and the Liberals [START_REF] Nielsen | Ett decennium med personval: erfarenheter och utfall[END_REF] Taking the description above as a point of departure, the likelihood of finding individualized campaigning in Sweden should be fairly low. Online campaigning in Sweden would be likely to be collectivistic and party-centered. However, the diffusion of the internet has been shown to have some significance when it comes to campaign strategies [START_REF] Norris | A virtuous circle: Political communication in post-industrial democracies[END_REF][START_REF] Persson | Förtroendevalda i kommuner och landsting 2007: en rapport om politikerantal och representativitet[END_REF]. Sweden is one country where internet access is most widely diffused. In 2010, over 85% of the Swedish population had access to the internet [START_REF] Findahl | Svenskarna och Internet[END_REF]. Hence, Sweden is a country where conflicting forces surround political campaigns. On the one hand, it is a country characterized by a strong party-centered tradition and weak institutional incentives to conduct individualized campaigns. On the other hand, it is a country much evolved, in terms of the social and technological developments that encourage individualization. The subject of political blogging appears to be an important arena in which to study these two forces. Blogging politicians are socialized into collectivistic parties and are dependent on partycentered electoral institutions but are also employing individually-oriented technologies.

Methods and measurements

This study is based on a web survey sent to the authors of all blogs listed on the political blog-ranking site "Politometern" (www.politometern.se) for which e-mail addresses could be found. The survey was answered by 730 political bloggers. The 604 who were politicians from the seven parties represented in the Swedish national parliament during the parliamentary term 2006-2010 were included in the study. Included respondents were either holding political office at the time of the survey (the study includes Members of the European Parliament, members of the national parliament, local councillors and local board members) or candidates in the 2010 elections. The survey was answered during the two months following the 2010 Swedish election. The empirical material accumulated gives a broad overview of the Swedish political blogosphere and makes it possible to analyze bloggers' background, strategies and behavior, in connection to their blogging. This study is primarily based on quantitative analyses of bloggers' survey responses and positions in the network centrality index. The variables used are presented in the following sections. As dependent variables a set of measurements of individualization among political bloggers are analyzed. The independent variables analyzed in this study can be divided into three categories that correspond with the three areas discussed in the theoretical section: party bond, blog-related variables and social characteristics.

Individualism -collectivism

First, we investigate bloggers' attitudes to preferential voting and separate supporters of a stronger preferential voting system (Support stronger preferential vote) from other respondents. Second, respondents' motives for using blogging as an instrument in personal election campaigns are investigated by dividing respondents into two categories: those who report that using blogging as an instrument in a personal campaign was a strong motive for blogging during the 2010 election campaign (Strong personal campaign motif) and other bloggers. The third dimension of individualism -collectivism -is measured by separating bloggers with an individual focus (Blog focusing on person) from bloggers with a party focus (Blog focusing on party).

Party bond

Bloggers' party affiliation is divided into two party blocs: left-oriented (the Left Party, the Social Democrats and the Greens) and right-oriented (the Centre Party, the Liberals, the Christian Democrats and the conservative Moderates). Level of party activity is operationally defined as the number of party meetings respondents reported having attended during the year preceding the survey (10 party meetings or more/less than 10 party meetings). We also analyze bloggers' positions within their parties by separating incumbents, who held a political position in the last parliamentary term, from challengers, who were candidates in the 2010 election and had not held a political position during the last term.

Blog-related variables

Level of experience and activity in the blogosphere are investigated using three variables. First, how long respondents have blogged is studied using a variable that separates bloggers with at least two years of experience (Long-time bloggers) from other bloggers (Short-time bloggers). Second, we operationalize level of blogging activity with a variable that reflects the frequency with which bloggers write blog posts, dividing bloggers into two categories: those that update their blogs at least three to five times a week (Frequent bloggers) and those who write posts less frequently. We also investigate level of activity in the blogosphere by creating two categories for respondents who read others' blogs: those who follow ten blogs or more (Heavy blog readers), and those who follow fewer than ten blogs.

Social characteristics

Several social characteristics are analyzed: gender (Man/Woman); educationspecifically, whether or not respondents have a post-secondary school education (High education/Low education); age -specifically, whether respondents are born before or after 1970 (Under 40 years old / 40 years or older).

Empirical analysis

Who are Swedish political bloggers?

The distribution of political, social and blogging variables reveals many similarities between the left and right blogosphere in the run-up to the 2010 Swedish election, but there are some interesting differences, as well. Perhaps most important, there seems to be an ideological bias towards the right. First, the data show that bloggers from the right are somewhat overrepresented among respondents (352, against 252). Second, a general right orientation of the Swedish political blogosphere becomes evident when comparing left-right self-positioned blogging politicians 1 to other Swedish politicians. A majority of bloggers are in harmony with their parties, in the sense that they position themselves at the mean self-positioning value among political representatives from their parties [33] 2 . However, while only 11% of bloggers position themselves to the left of their parties, a quarter of bloggers position themselves to the right -a tendency which is greatest for bloggers on the left (Table 1). Moreover, the mean selfposition on the left-right scale among all bloggers is 6,3 when individual positions are weighted against their respective parties' share of seats in national, regional and local assemblies. In comparison to surveys of Swedish representatives, the political bloggers in this study are, in general, remarkably more right-oriented. Surveys among Swedish politicians in general shows that representatives at both the local and regional levels had a mean left-right position of 4,8, while national representatives positioned themselves more to the right and had a mean value of 5,2 [START_REF] Gilljam | Politik på hemmaplan. Tiotusen svenska fullmäktigeledamöter tycker om politik och demokrati[END_REF]. Thus, the blogosphere during the 2010 Swedish election cycle shows an ideological bias towards the right. Blogging more often attracts candidates to the right of the ideological spectrum, both between and within parties.

Table 1, furthermore, shows that candidates from both the left and the right are highly involved in the party-sphere, as well as the blogosphere. More than 80% of respondents' have participated in ten or more party meetings during the last year, and a third of blogging politicians hold incumbent positions. The remaining two-thirds were candidates in the 2010 elections. A large share of respondents is also comprised of highly active and experienced bloggers. The majority had blogged for more than two years before the 2010 Swedish elections, and almost a third wrote daily blog posts during the election campaigns. Respondents are also active as readers of political blogs: about a fifth follows ten or more political blogs, and over 40 percent follow between four and ten. No major differences are visible between left-and rightoriented bloggers when it comes to frequency of writing or reading blog posts. However, a significantly higher number of bloggers from leftist parties have been blogging for two years or more.

When it comes to social characteristics, we find that blogging candidates are predominantly male, representing six out of ten respondents. This difference roughly reflects the gender difference among local and regional Swedish politicians, in general (42% women) but is somewhat higher than the gender differences among national parliamentarians (47% women) [START_REF] Persson | Förtroendevalda i kommuner och landsting 2007: en rapport om politikerantal och representativitet[END_REF]. The share of female political bloggers is slightly higher among the left-oriented than the right-oriented parties. As we would expect, the age structure among bloggers is different from that of politicians, in general. Young politicians are overrepresented among bloggers, but there is, nonetheless, a fairly high number of bloggers over the age of 40. There is a significant difference in age structure between the two party groups, as left-oriented bloggers are more often under forty. No similar difference is apparent among left and right politicians, in general [START_REF] Persson | Förtroendevalda i kommuner och landsting 2007: en rapport om politikerantal och representativitet[END_REF]. Notes: Chi-square test, significance levels are displayed as follows, *** : p<,01, ** : p<,05, * :p<,1.

Political blogging, collectivism and individualism

Let us turn from the backgrounds of blogging candidates to the question of how and why they blog. A key finding in our survey (Table 2) is that blogging politicians on the right are much more individualized than those on the left, who are more strongly tied to the collective identities of their parties. While more than two-thirds of bloggers from parties on the right support a "stronger personal vote" in the Swedish election system, only a quarter of bloggers on the left do the same. Almost two-thirds of rightoriented bloggers display a strong personal campaign motive for blogging, compared to only a fifth of bloggers on the left. Additionally, 43% of blogging politicians on the right report that they focus more on themselves than on their parties in their blogs. The comparable figure for bloggers on the left is 20%. This pattern remains strong, even when bloggers are categorized according to political, social and blogging variables. With few exceptions, 3 significant differences are apparent between leftand right-oriented bloggers, at all levels of individualization in all categories of bloggers. Thus, our findings strongly suggest that the blogosphere plays different roles for the left and right. The right appears to embrace an individualized form of candidate-centered blogging, while the left gives the impression of having a more collective agenda.

Besides ideology, do less party-involvement and more blog-involvement increase the probability of individualized campaigns? Sometimes the evidence points in such a direction, but the results are rather complex. Left and right bloggers with challenger positions and relatively low party activity tend to be more supportive of a stronger personal vote, especially challengers. They also tend to focus somewhat more on person, rather than party, in their blogs, especially within the left bloc. However, incumbents do, just as often, have personal campaign motives for blogging as challengers, and high party activity is positively correlated with such motives. Thus, even though ideology is important, it looks as if there is a degree of pragmatism involved in blogging, as well. Furthermore, when it comes to involvement in the blogosphere, there are some significant relationships. The data roughly indicate that actively reading and/or writing blogs is positively associated with support for a stronger personal vote and a campaign motive for blogging, on both the left and the right. No significant relationships are found in relation to blog focus. However, it is interesting to note that long-time blogging means different things to the left and right. On the right, long-time bloggers are significantly more individualized (in their support for a stronger personal vote). On the left, long time-bloggers are significantly less individualized (in terms of their motives for blogging). It is, therefore, far from certain that the cumulative effect of blogging will push the leftist blogosphere towards a more individualized approach over time.

How about social characteristics? Are some groups -such as young, highlyeducated candidates -particularly likely to use blogs to individualize their campaigns? In general, the results show moderate differences between groups, indicating that the threshold for using blogs to conduct individualized campaigns is relatively low. Within the right, there are significant differences in relation to age, education and gender. Young and highly-educated bloggers are more supportive of a stronger personal vote than are older and less educated bloggers. Men are both more individually focused and motivated by personal campaign aspirations than are women. The fact that no age differences are found within the left-oriented group is interesting and suggests -in line with the argument above -a continuing divergence, rather than convergence, of the left and right blogospheres. There does not seem to be a younger generation of political bloggers on the left coming to age with more individualized attitudes to blogging and politics. Rather, young bloggers on the left appear to resemble their older colleagues, while individualism is amplified among young bloggers on the right. Notes: Chi-square test, significance levels are displayed as follows, *** : p<,01, ** : p<,05, * :p<,1.

Conclusions

In the popular debate on online campaigning, deterministic perspectives are at the center. The new opportunity structure of the internet is assumed to change everything or nothing at all. Institutional contexts make all the difference -or none. According to Wright [START_REF] Wright | Political as usual? Revolution, normalisation and a new agenda for online deliberation[END_REF], this deterministic framework often distorts how researchers make sense of their empirical findings by creating undue expectations. In the face of all the hyping of technology, there is danger of an implicit pessimistic mindset being adopted. Considering the institutional context in Sweden, with parties of unparalleled strength and an election system with limited incentives for candidate-centered campaigns, we should, thus, be careful not to set too-high expectations. It is important to note that a pretty large number of candidates blogged during the 2010 electoral campaign and that the blogging uptake has been quite broad. More than the usual number of pioneers have been blogging, and their blogs have often been used in candidate-centered campaigns. Furthermore, many bloggers perceive their blogs as having given them more influence, and the more they have been involved in the blogosphere, the more influence they report. Hence, the medium appears to be important.

The main conclusion to be drawn from this study is, nonetheless, that the uptake and usage of blogging, to a considerable degree, is mediated by ideology and party affiliation. First, blogs seem to be more attractive to politicians who stand to the right, both among and within parties. A possible explanation for this ideological asymmetry within the Swedish blogosphere lies in common understandings of blogging as an individualistic practice and, therefore, more attractive to politicians with liberal ideological convictions [START_REF] Benkler | A Tale of Two Blogospheres: Discursive Practices on the Left and Right[END_REF]. Second, we find that right-oriented bloggers are widely embracing political individualization, while bloggers on the left are more hesitant. As left-oriented politicians are socialized into collectivistic party organizations that have traditionally opposed reforms for a stronger preferential voting system and traditionally been characterized by stronger party loyalty, this duality is quite logical [START_REF] Esaiasson | Representation from above: members of Parliament and representative democracy in Sweden[END_REF][START_REF] Gilljam | Representationsprinciper i riksdag och kommuner[END_REF][START_REF] Holmberg | Riksdagen representerar svenska folket: empiriska studier i representativ demokrati[END_REF][START_REF] Nielsen | Ett decennium med personval: erfarenheter och utfall[END_REF]. Within these parties, a wide support for a stronger preferential vote, as well as an increase in the number of blogs used for personal election campaigns and individually-focused blogs, would be nothing less than a landslide. Instead, we see the prevailing influence of preexisting party cultures that mediate the uptake, and use of political blogs. The available evidence does not suggest that further blog experience or the younger generation will automatically change this. The results therefore underline the importance for researchers to consider ideological frameworks as well as institutional contexts, in order to fully understand the differentiated impact of e-participation

Table 1 .

 1 The Swedish candidate blogger; left and right (percentages).

		All	Left	Right	Difference
	Incumbent position (elected)	32	29	33	4
	Challenger position (not elected)	68	71	67	-4
	Visited 10 or more party meetings	81	81	81	0
	Visited less than 10 party meetings	19	19	19	0
	Ideologically left of party	11	12	9	-3
	Ideologically same as party	64	57	69	12 ***
	Ideologically right of party	24	29	22	-7 **
	Long time blogger > two years	55	66	46	-20 ***
	Short time blogger < two years	45	34	54	20 ***
	Blogged daily	29	28	30	2
	Blogged weekly	45	44	46	2
	Blogged less often	26	28	24	-4
	Follows more than 10 blogs	18	19	18	-1
	Follows 4-10 blogs	43	41	45	4
	Follows 0-3 blogs	39	40	38	-2
	Men	61	58	63	5
	Women	39	42	38	-5
	Younger than 40 years	46	55	44	-11 **
	40 years or older	54	45	56	11 **
	High education	72	71	72	1
	Low education	29	29	28	-1
	N	604	252	352	100

Table 2 .

 2 Blogging and individualism; left and right (percentages).

			Support "a stronger		Personal campaign		Blog focusing on person
			personal vote"		motif for blogging
		Left	Right	Diff.	Left	Right	Diff.	Left	Right	Diff.
	All	24	69	45 ***	21	63	42 ***	20	42	22 ***
	10 party meetings	21	67	46 ***	26	73	47 ***	18	42	24 ***
	Less	28	75	37 ***	10	44	34 ***	39	44	5
	Difference	7	8		-16 *	-29 ***		21 **	2
	Incumbent	18	64	46 ***	24	70	46 ***	19	42	23 ***
	Challenger	31	79	48 ***	25	71	46 ***	23	43	20 ***
	Difference	23 *	15 **		1	1		4	1
	Left of party	17	63	46 ***	19	69	49 ***	54	52	-2
	Same as party	28	51	23 ***	13	64	51 ***	37	41	4
	Right of party	22	74	52 ***	25	62	37 ***	40	52	12 **
	Difference	6	11		6	-7		-14	0
	Short time blogger	23	63	40 ***	36	70	34 ***	19	38	19 **
	Long time blogger	20	74	54 ***	19	70	46 ***	21	48	27 ***
	Difference	-3	13 *		-23 ***	0		2	10
	Frequent blogger	24	71	47 ***	20	63	43 ***	21	44	23 ***
	Infrequent blogger	18	67	49 ***	30	77	47 ***	18	40	22 ***
	Difference	-6	-4		-10	-14 **		-3	-4
	Reading few blogs	18	66	48 ***	15	62	40 ***	23	48	21 **
	Reading many	41	77	46 ***	25	72	46 ***	19	41	23 ***
	Difference	23 ***	11		10	-10		-4	-7
	>40 years old	24	59	35 ***	25	67	42 ***	24	40	16 ***
	<40 years old	24	79	55 ***	17	58	41 ***	19	47	28 ***
	Difference	0	20 **		-8	-9		-5	7
	Men	24	75	51 ***	21	67	46 ***	19	47	28 ***
	Women	18	57	39 ***	30	75	45 ***	23	35	12
	Difference	-4	-18 ***		9	8	***	4	-12 **
	High education	22	63	41 ***	26	72	46 ***	20	41	21 ***
	Low education	19	80	61 ***	21	64	43 ***	19	43	24 ***
	Difference	-3	14 ***		-5	-6		-1	2
	N	221	311		247	342		220	308

The Swedish political contextSweden is characterized by strong political parties with a central position in the representative democratic system. Parties have traditionally taken a central role in organizing political representation and electoral campaigns; all candidates are

We investigate self positioning on an eleven-point scale, reaching from 0 (most left-oriented) to 10 (most right-oriented), that is included in several earlier studies of Swedish politicians and voters (see[33] for an overview)

The results of the blog survey are compared with a survey among political representatives in Sweden. The party mean values from the general survey presented are rounded to the closest whole number, Left=1, Social democrats=3, Greens=4, Centre party=6, Liberals=6, Christian democrats=7, Conservative moderates=8

Significant differences between bloggers on the left and right are not found regarding the level of focus on person (in comparison to focus on party) for bloggers that attend few party meetings, among female bloggers.