Victor Santoni
email: victor.santoni@cyu.fr

Samuel Rufat
email: samuel.rufat@u-cergy.fr

Keywords: Social media, Emergency management, iVGI, Big data, Attacks, Early warning, Disasters

Social media have sparked increasing appeal for improving emergency management. Many have advocated the use of Social Media for Emergency Management (SMEM) to disseminate early warnings. Resorting to the massive but unstructured inVoluntary Geographic Information (iVGI) from social media during disaster response phase has been encouraged for rapid assessment, location and mapping of people and events, for monitoring the expressed needs and for observing human behavior in crisis dynamics. SMEM encompasses top-down -public information, community engagement -bottom-up -situational awareness, urgent needs collection -and many-to-many information flows -allowing a large number of users to communicate with each other simultaneously in real-time. However, operationalizing social media data in real-time during emergency situations might not be as practical as ex post facto retrospective analyses usually suggest. We question the use of SMEM and the unchallenged expectations that they allow for a faster and more widespread dissemination of early warnings, more citizens involvement and offer a richer and more contextualized crowdsourced feedback than classic emergency channels. We analyze the usefulness of geotagged information to monitor the unfolding of a crisis in real time and to accurately detect events and map people. We also question if the use of social media for emergency management is conditioned by local context and governance, by making a comparison between European countries. We offer a critical review of the literature, an SMEM analytical framework, and then empirical work based on large Twitter datasets collected from three different terror attacks in Europe: the Paris attacks in November 2015, the Brussels attacks in March 2016, and the Nice attack in July 2016. The results reveal important delays in warnings and information sharing, and that the press overshadows official accounts. Moreover, first-hand information is often too scattered, too thinly geotagged, and too late to be reliably used during emergency situations.

Introduction

After a series of terror attacks and false alarms, the French national early warning mobile application was discontinued in 2017. After this application was discredited by a series of failures and delays in sending warnings [START_REF] Douvinet | Why keep alert sirens in France?[END_REF], many have advocated the use of social media for emergency management (SMEM) to disseminate early warnings. The French government subsequently signed agreements with Twitter, Facebook and Google to develop crisis communications 1 . In the US, [START_REF] Graham | The role of social media in local government crisis communications[END_REF] survey shows that 71% out of 307 local 1The French Ministry of Interior partners with major actors to warn populations in case of an emergency -Press release (in French) (May 2018) https://www.interieur.gouv.fr/fr/Actualites/Information-de-la-population-en-cas-de-danger/ governments engage social media for crisis communication purpose. Other countries had already chosen a similar path, like New Zealand [START_REF] Rive | Social Media in Emergency: A Best Practice Guide[END_REF]. Being able to collect the massive amount of data from social media looked like a major opportunity for emergency management [START_REF] Liu | The Emergent Role of On-Line Photo Sharing in Times of Disaster[END_REF][START_REF] Palen | Crisis in a Networked World[END_REF].

Resorting to social media in real time during disaster response and early recovery phases has also been encouraged to gain situational awareness to be used for rapid assessment, location and mapping of people and events, as well as for the monitoring of expressed needs and for detecting human behavior in crisis dynamics. SMEM encompasses top-down -public information, community engagement -bottom-up -situational awareness, urgent needs collection -and manyto-many information flows -allowing a large number of users to communicate with each other simultaneously in real-time. As an example, after the Jakarta floods from 2013 to 2015, many-tomany flows were used to reveal the difference between official information and users' geotagged tweets about shelter locations [START_REF] Kusumo | Utilising volunteered geographic information to assess resident's flood evacuation shelters. Case study: Jakarta[END_REF]. Recent investigations use social media data in post-disaster assessments, aggregating, mapping and exploring the inherent complexities and spacetime dynamics of emergencies and disasters. Geotagged tweets are collected to better understand human activity during a disaster event, the spatial and temporal patterns of digital trails are used to monitor the evolution of the crisis, while content or metadata (such as hashtags, followers, shares, multimedia, etc.) are extracted to increase understanding, improve situational awareness, or for interaction and network analysis. Geotagging, access, scope and speed often make Twitter a favored channel. However, monitoring and operationalizing social media data in real time during emergency situations might not be as practical as ex post facto retrospective analyses usually suggest. Major challenges arise from practical issues that are too often overshadowed by the novelty effect.

In this paper, we question the use of social media and the unchallenged expectations that they allow for a faster and more widespread dissemination of early warnings. We also question other common expectations, that social media foster citizens involvement and offers a richer and more contextualized crowdsourced feedback than classic emergency channels during emergency management. We also explore the usefulness of geotagged information to monitor the unfolding of a crisis in real time and to accurately detect event and map people. Finally, we question if the use of social media for emergency management is conditioned by local context and governance, which is currently not addressed in the existing literature, by making a comparison between France and Belgium. We start with a critical review of the literature, an SMEM analytical framework, and then present an empirical work based on Twitter datasets collected from three different terror attacks: the Paris attack in November 2015, the Brussels attacks in March 2016, and the Nice attack in July 2016.

Crowdsourcing and crowd mapping in emergency management and disaster relief

Emergency management is the process of "developing and implementing policies that are concerned with mitigation, preparedness, response and recovery" (Petak, 1985) regarding crises and disasters. Emergency management concerns every geographic levels of government (local, regional, and federal) and requires the efforts of many disciplines to overcome disasters consequences. As such, emergency management systems include a crisis cell with decision-makers, public and private stakeholders providing expertise, communication system ensuring the information flows, and firstresponders tackling the event on the field (Lagadec, 1995).

During the past decade, volunteered geographic information (VGI) has led to consider citizens as sensors [START_REF] Elwood | Volunteered geographic information: future research directions motivated by critical, participatory, and feminist GIS[END_REF][START_REF] Goodchild | Citizens as sensors: the world of volunteered geography[END_REF]. VGI had early successes in emergency management, most notably through Open Street Map [START_REF] Zook | Volunteered Geographic Information and Crowdsourcing Disaster Relief: A Case Study of the Haitian Earthquake[END_REF] or Ushahidi [START_REF] Okolloh | Ushahidi, or "testimony": Web 2.0 tools for crowdsourcing crisis information[END_REF]. VGI was Alerte-et-information-de-la-population-en-cas-de-danger-immediat so successful, fast and cheap that the US government decided to use such crowdsourced information for coordinating international disaster relief in the aftermath of the 2010 earthquake in Haiti (Yates and Paquette, 2011). However, despite the expectations generated by early successes, VGI remains limited of its demographic bias [START_REF] Li | Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr[END_REF], because of its spatial bias induced by the volunteers opportunistic observation effort [START_REF] Zhang | The representativeness and spatial bias of volunteered geographic information: a review[END_REF], and because of the number of volunteers.

Twitter is a microblogging social media platform where users can post short messages named tweets. It is quite successful in academic research [START_REF] Acar | Twitter for crisis communication: lessons learned from Japan's tsunami disaster[END_REF][START_REF] Kryvasheyeu | Performance of Social Network Sensors during Hurricane Sandy[END_REF][START_REF] Li | Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr[END_REF][START_REF] Takahashi | Communicating on Twitter during a disaster: An analysis of tweets during Typhoon Haiyan in the Philippines[END_REF]Zhang and Zhu, 2018, etc.). Firstly, because the wide majority of tweets are public, and secondly, because metadata can be extracted through many application programming interfaces (API). Despite the expectations generated by early successes, VGI remains limited because of its demographic bias resulting from the lack of representativeness of contributors' profile among the total population [START_REF] Li | Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr[END_REF], because of its spatial bias induced by the volunteers' opportunistic observation effort [START_REF] Zhang | The representativeness and spatial bias of volunteered geographic information: a review[END_REF], and because of the reduced number of volunteers. User-generated information from the field evolved from being limited but well-structured VGI, to being massive but unstructured across social media. Such information is referenced as "involuntary geographic information" (iVGI) (Fischer, 2012) because users do not have any control on how their data will be used after being posted. With Twitter, in addition to semantic information within tweets, geographic information is to be found in the metadata: Internet access point, latitude / longitude, and/or voluntary geotags provided by the user [START_REF] Bouillot | How and why exploit tweet's location information[END_REF]. Collecting and refining Twitter's geographic information is an ongoing effort [START_REF] Williams | Improving geolocation of social media posts[END_REF] to make tweets relevant for geospatial analysis [START_REF] Saldana-Perez | When Twitter Becomes a Data Source for Geospatial Analysis 18[END_REF]. Tweets are used in many domains such as human mobility [START_REF] Luo | Explore spatiotemporal and demographic characteristics of human mobility via Twitter: A case study of Chicago[END_REF], detecting population density at different times [START_REF] Landwehr | Using tweets to support disaster planning, warning and response[END_REF], or even predicting pandemic outbreaks [START_REF] Zhao | Online Spatial Event Forecasting in Microblogs[END_REF].

Twitter data was used as a "sensor" after Hurricane Sandy to collect geospatial data on the impacted areas [START_REF] Albuquerque | A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management[END_REF][START_REF] Kryvasheyeu | Performance of Social Network Sensors during Hurricane Sandy[END_REF], with a positive correlation between Twitter activity and per-capita disaster damage [START_REF] Kryvasheyeu | Rapid assessment of disaster damage using social media activity[END_REF]. However, other studies cross-referencing Twitter data with surveys after the Tohoku earthquake in Japan [START_REF] Acar | Twitter for crisis communication: lessons learned from Japan's tsunami disaster[END_REF] or Typhoon Haiyan in the Philippines [START_REF] Takahashi | Communicating on Twitter during a disaster: An analysis of tweets during Typhoon Haiyan in the Philippines[END_REF] revealed a variety of uses: people in affected areas tweet more about unsafe and uncertain conditions, while people in remote areas tweet about being safe. Social media are nevertheless mostly used to disseminate second-hand information and to memorialize those affected. Twitter data has been used as a VGI substitute, to identify the shelter locations preferred by flood evacuees in Jakarta for instance [START_REF] Kusumo | Utilising volunteered geographic information to assess resident's flood evacuation shelters. Case study: Jakarta[END_REF]. In fact, SMEM have found applications in every disaster phase [START_REF] Landwehr | Using tweets to support disaster planning, warning and response[END_REF]. Twitter data has even been used for detecting earthquakes in real time [START_REF] Sakaki | Earthquake shakes Twitter users: real-time event detection by social sensors[END_REF]. In Belgium and France however, fewer studies address SMEM as of today. While very few analyses of the bottom-up and many-to-many information flows have been done so far [START_REF] Santoni | De l'urgence d'une cartographie de crise au centre de crise zonal[END_REF], geographers have teamed up with first-responders to improve top-down early warning uses or to test possible mapping applications [START_REF] Douvinet | Une place pour les technologies smartphones et les Réseaux Sociaux Numériques (RSN) dans les dispositifs institutionnels de l'alerte aux inondations en France ? Cybergeo[END_REF].

Top-down SMEM flow encompass early warnings' and official posts or information from emergency managers accounts during a disaster [START_REF] Rive | Social Media in Emergency: A Best Practice Guide[END_REF]. Bottom-up SMEM flow is the user-generated information addressed to emergency managers accounts. It can include three cases: answers to emergency manager posts, direct messages to official accounts, or tags referencing emergency manager accounts to call on them or to draw their attention to some post or media content [START_REF] Rive | Social Media in Emergency: A Best Practice Guide[END_REF]. Many-to-many SMEM flow refers to all the content posted by all social media users (many) about a crisis or an emergency without targeting a specific audience (many). Such information can be monitored or harvested in bulk as soon as the relevant keywords, hashtags, or geotags referring to the event and location have been identified (SPFI, 2015). While many-tomany flows are accessible to all users and might include relevant information, users might not have produced it with the objective to help or call on first-responders. They might not even be fully aware of the situation. Emergency managers expect users to spontaneously and promptly share firsthand media-rich information during unusual events.

Social Media for Emergency Management: Debate and Bias

While the sheer volume of social media data has raised high expectations for emergency management, there is a debate opposing "big data" and "small data" [START_REF] Kitchin | Small data in the era of big data[END_REF], or even "micro" datasets [START_REF] Tsou | Visualization of social media: seeing a mirage or a message? Cartogr[END_REF] that generate significant results with less than 350 tweets [START_REF] De Longueville | OMG, from Here, I Can See the Flames!": A Use Case of Mining Location Based Social Networks to Acquire Spatio-temporal Data on Forest Fires[END_REF]. We argue that this debate is about focus rather than size. Social media data can be examined through a "telescope", with high volume quantitative analysis, or through a "microscope", with qualitative analysis and/or focusing on the granularity of the individual [START_REF] Beckouche | Les Nouveaux Territoires du numérique[END_REF]. Such debates derive four possible biases in SMEM:

Bias 1: "Big data" is not whole data (boyd and [START_REF] Crawford | Critical Questions for Big Data[END_REF], meaning that social media users are not an average representation of the population. Socio-spatial inequalities have been highlighted, with whiter, wealthier, tourist-oriented users during Hurricane Katrina [START_REF] Crutcher | Placemarks and waterlines: Racialized cyberscapes in post-Katrina Google Earth. Geoforum, Themed Issue: The 'view from nowhere'? Spatial politics and cultural significance of high-resolution satellite imagery[END_REF] and with blacker and wealthier users during the Michael Brown case [START_REF] Bunting | Exploring geospatial characteristics of hashtag activism in Ferguson, Missouri: An application of social disorganization theory[END_REF]. Such inequalities reflect Twitter "data shadows" [START_REF] Shelton | Mapping the data shadows of Hurricane Sandy: Uncovering the sociospatial dimensions of 'big data[END_REF]. While physically vulnerable communities had more intense responses on social media during Hurricane Sandy, socially vulnerable communities were digitally left behind [START_REF] Wang | Are vulnerable communities digitally left behind in social responses to natural disasters? An evidence from Hurricane Sandy with Twitter data[END_REF]. On the other side, non-human accounts, i.e. bots or automated accounts that are used for mechanically producing, promoting or manipulating specific information weigh and visibility, are also tweaking data accuracy and representativeness [START_REF] Crawford | The limits of crisis data: analytical and ethical challenges of using social and mobile data to understand disasters[END_REF].

Bias 2: Social media are plagued by noise, rumors and false flags [START_REF] Starbird | Rumors, False Flags, and Digital Vigilantes: Misinformation on Twitter after the 2013 Boston Marathon Bombing[END_REF][START_REF] Vicario | The spreading of misinformation online[END_REF]. Inaccurate Twitter geographic information and subsequent retweets induce noise, SMEM can consequently hinder post-disaster data collection and response rather than improve it (Earle et al., 2010). However, it has been argued that noise and rumors are not such a concern because of a self-correcting mechanism in social media: as users correct inaccuracies and dispel rumors, the sheer number of users is expected to be working as a safety net to correct the information flow (Jong and Dückers, 2016).

Bias 3: Social media might lead to over-communication and backfire during an emergency, generating stress and panic [START_REF] Spence | Variability in Twitter Content Across the Stages of a Natural Disaster: Implications for Crisis Communication[END_REF]. Conversely, minimal social media communication gives free rein to rumors and has been shown to have detrimental impacts [START_REF] Jones | Distress and rumor exposure on social media during a campus lockdown[END_REF].

Bias 4: Twitter geotagged information only represents between 0.7% [START_REF] Graham | Where in the World Are You? Geolocation and Language Identification in Twitter[END_REF] to 6% [START_REF] Takhteyev | Geography of Twitter networks[END_REF] of a total volume of tweets. Some studies have tried to circumvent this by using the profile location in addition to the tweets thin geospatial data [START_REF] Sakaki | Earthquake shakes Twitter users: real-time event detection by social sensors[END_REF][START_REF] Williams | Improving geolocation of social media posts[END_REF][START_REF] Zou | Mining Twitter Data for Improved Understanding of Disaster Resilience[END_REF] or text analytics [START_REF] Bahir | Geospatial extreme event establishing using social network's text analytics[END_REF]. However, the "lack of correlation between profile and device location" [START_REF] Graham | Where in the World Are You? Geolocation and Language Identification in Twitter[END_REF] casts doubts on their conclusions. Moreover, putting a tweet on a map is insufficient to explain complex socio-spatial relations [START_REF] Crampton | Collect it all: national security, Big Data and governance[END_REF], and the statistical correlation between population density and geotagged tweets might generate false positive [START_REF] Li | Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr[END_REF]. In addition, [START_REF] Yuan | The Missing Parts from Social Media-Enabled Smart Cities: Who, Where, When, and What? Ann[END_REF] are showing that tweets located in places with predefined coordinates are 44% off-scene.

Fig.1 Analytical SMEM Framework

We propose an analytical framework to appraise how the SMEM biases are addressed in the literature and relevant across case studies (fig. 1). The framework is split into three dimensions to capture how the biases are addressed (fig. 1A), to assess the geographical scope and duration of the events considered in the literature (fig. 1B), as well as the number of affected people, and the existence of prior warnings (fig. 1C). While the path of Hurricane Sandy was correctly predicted and official warnings issued, this is not possible for other events, especially earthquakes or attacks. We populated the framework with two case studies per disaster type. To the best of our knowledge, no study tackles all the SMEM biases at once, as the fourth bias is often overlooked (fig. 1A).

Conversely, most studies address the first bias. While terror attack and hostage situation case studies did not address this bias, it does not seem to derive from any specific disaster type. Studies addressing the third bias seem to focus on local events with less than 1000 affected people, whereas biases 2 and 4 do not translate into any specific pattern. It is worth mentioning that studies from before the creation of the relevant emergency managers social media accounts -and any before 2009 -could not explicitly address bias 3, as top-down SMEM communication was not previously possible. We could not find studies for all the scope and duration combinations (fig. 1B), the covid-19 pandemic should promptly fill one of the identified gaps though. While riots and attacks are difficult to predict, other events are preceded by prior warnings (fig. 1C) and disseminating official early warnings over social media is less relevant for the latter. There are currently no commons indicators in the literature to assess the reactions in social media to the variety of disasters, warnings, their scopes, duration, or number of affected people.

As a result of some of these biases, many have argued that social media data has to be crossreferenced with a precise understanding of the context [START_REF] Shelton | Spatialities of data: mapping social media 'beyond the geotag[END_REF]. It is also claimed that a thorough geospatial analysis takes time [START_REF] Crampton | Collect it all: national security, Big Data and governance[END_REF] and that it is encouraging to combine content analysis with "multidisciplinary collaborations" [START_REF] Tsou | Research challenges and opportunities in mapping social media and Big Data[END_REF]. We argue that emergency-managers and decision-makers enthusiasm for volume and speed as acquired by SMEM overlooks such issues and can lead them to overrate real-time usability.

Research design

We used Twitter data from terror attacks in Paris (France), Nice (France) and Brussels (Belgium) from 2015 to 2016 to test such expectations. The precise time and space settings of terror attacks allow for a detailed comparison of the specific unfolding of events with the dissemination of information in space and over time on social media. The time (in minutes) can also be accurately tracked before an official alert is issued and the first crowdsourced information becomes available. 1), the Paris and Brussels attacks fall into the same category because they were both composed of multiple attacks at different places over several hours, while the Nice attack was a shorter and single event.

We used these three events to test expectations for real-time SMEM usability. In France, decisionmakers expect to have a better dissemination of early warnings and improved communication with citizens during emergencies2 . At a local level, emergency-managers also expect improved warnings, awareness and data collection with SMEM; many have implemented Virtual Operations Support Teams (VOST) [START_REF] Douvinet | Une place pour les technologies smartphones et les Réseaux Sociaux Numériques (RSN) dans les dispositifs institutionnels de l'alerte aux inondations en France ? Cybergeo[END_REF]. In Belgium, a more comprehensive SMEM approach was implemented in 2013, including top-down early warnings, bottom-up participatory data collection, and a many-to-many approach 3 : monitoring social media to detect first-hand information from the field during emergencies. We group these expectations in four assumptions often assumed to be true, and we add one final hypothesis derived from our framework:

1. Social media allow for a faster and more widespread dissemination of early warnings to the general population than conventional channels. 2. Social media foster bottom-up processes; they bring citizens closer to first-responders and decision-makers, and thus involve citizens in emergency management. 3. Geotagged information is useful to monitor the unfolding of a crisis in real time, to accurately detect events and map people, their expressed needs and actual behaviors. 4. Social media data offers richer, more complete and contextualized crowdsourced feedback than calls to emergency hotlines; such information is easily and rapidly accessible and processable in real time. 5. Emergency communication and SMEM usability is conditioned by local governance and geographical context.

The selected case studies are close in time and space, and are located in dense and developed cities where the case for social media is the most obvious. They are however in different national emergency management contexts while having varying degrees of political clout, multi-linguistic, and touristic backgrounds. The novelty of our approach is to use such a Belgium and France comparison as well as our SMEM framework (fig. 1) to specifically test for the fifth hypothesis, which is least studied in the existing literature.

Methods

Data scraping

We built our datasets using the Twitter Intelligence Tool (Twint), an open-source python library dedicated to scrape Twitter's data. We chose this method rather than Twitter's API v1 because by the time of our dataset extraction, it was not possible to extract tweets that were published more than 7 days before the query time or only small samples4 . Twint uses Twitter's operators to scrape specific requested data. The query has to be made by selecting either an account, a keyword or a location in a time period. The amount of collected tweets seems unlimited, allowing for the creation of larger datasets without limitation. Twint does not provide the retweets unless specified in the query. Yet, there are drawbacks to not using Twitter's API, as it implies not having access to the detailed latitude and longitude fields on the geotagged tweets. Twint only returns the city where the tweets were geolocated by the users. All datasets were restricted approximately 3 hours after each event in the case of multi-attacks.

Keywords selection

We extracted 3 datasets per attack, bringing on a total of 9 datasets. For each attack, we extracted one top-down, one bottom-up and one many-to-many dataset according to the three main SMEM uses [START_REF] Latonero | Emergency Management, Twitter, and Social Media Evangelism[END_REF]. These three flows of information and the three corresponding uses are acknowledged in the French agreement with Twitter, Facebook and Google (op. cit.) and detailed in the Belgium crisis center documentation (op. cit.). Top-down datasets collected the messages sent by the Twitter official accounts of local and national governments, first-responders, emergency managers, police, news and press. These datasets were used to test our first hypothesis on the speed and scope of early-warnings and our fifth hypothesis on the impact of local governance. Bottom-up datasets collected the tweets sent to the previously targeted official accounts (e.g. all tweets sent from the Belgian crisis center account were collected in the top-down dataset, whereas all tweets sent to or mentioning @CrisisCenterBe were collected in the bottom-up dataset). These datasets were used to test our second hypothesis on the involvement of the general public in emergency management. Many-to-many datasets collected all the tweets mentioning the 10 most recurrent keywords during the first 3 hours of each attack. Our queries were tested with plural forms (e.g. attack/attacks), different languages (English, Flemish and French), and collected all tweets with occurrences of the keywords (e.g. attacks) including hashtags (e.g. #attack). Manyto-many datasets are thus broader and can include the tweets already collected in the top-down and bottom-up datasets. They were used to test all our hypotheses, including on geotagged information, richer content, and contextualized feedback.

Mapping

Based on our many-to-many datasets, we used the QBano QGIS plugin to place geotagged tweets on a map. This relates to our third hypothesis. We wanted to identify the proportion of tweets geotagged on-site, as opposed to geotagged in other parts of the country, in order to compare the overall spatial distribution with population density [START_REF] Li | Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr[END_REF] and with previous results that showed low geotagged rates [START_REF] Graham | Where in the World Are You? Geolocation and Language Identification in Twitter[END_REF]. As on-site geotaged tweets are more likely to provide first-hand information, the overall geotagged rate alone is not sufficient to evaluate the usability of the information. We have also mapped the geotagged tweets rate per 10,000 inhabitants at the finest possible granularity.

Timelines

We investigated for each case study the detailed timelines of Twitter uses during the attacks. To test the reaction time during the terror attacks, we recorded the comprehensive timelines of relevant tweets. Starting at the hour before each event to detect the signal-to-noise ratio, we plotted the total amount of tweets for each keyword or account every five minutes, up to three hours after the attacks. For each case study, we reconstructed four timelines of tweets, one for the top-down, one for the bottom-up, one for many-to-many datasets, and one for the extraction of the geotagged tweets; this enabled us to compare the sequences and reaction times of the different uses. We also pinpointed the first tweet after the start of the events by officials, emergency responders accounts and by the press, both at the local and national levels, to accurately measure the time lag between each event and the first related warning on social media.

Finding needles in a haystack

We used the many-to-many datasets to test our first and fourth hypotheses. Unlike ex post facto retrospective analyses [START_REF] Cheng | OSNI: Searching for Needles in a Haystack of Social Network Data 4[END_REF], we decided not to use algorithms to look for needles in a haystack to be closer to the real crisis conditions because emergency managers are not using algorithms for harvesting information yet [START_REF] Santoni | De l'urgence d'une cartographie de crise au centre de crise zonal[END_REF].

From a SMEM perspective, useful information has to be available, first-hand, on site, geotagged, properly referenced with the event's specific keyword, published within a short period of time, and before first-responders arrive on site [START_REF] Albuquerque | A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management[END_REF][START_REF] Douvinet | Une place pour les technologies smartphones et les Réseaux Sociaux Numériques (RSN) dans les dispositifs institutionnels de l'alerte aux inondations en France ? Cybergeo[END_REF][START_REF] Rive | Social Media in Emergency: A Best Practice Guide[END_REF][START_REF] Saldana-Perez | When Twitter Becomes a Data Source for Geospatial Analysis 18[END_REF]. We extracted subsets spanning 15 minutes before and 15 minutes after each event. Such a 15 minutes time interval reflects the average time first-responders take to reach an emergency situation5 . We also extracted the 15 minutes before the events to check the signal to background noise ratio.

For each subset, we manually checked the proportion of tweets that truly related to the event. We recorded the time lag by calculating the delay between the start of each event and the very first related tweet. We also extracted geotagged tweets, tweets with pictures and videos, and calculated the delay before the first picture or video of the event was published. We separated tweets requesting information and those referring to the news and press from the tweets offering first-hand information, to test our first and fourth hypotheses. During the Paris attacks (fig. 2), top keywords first refer to the ongoing events, and second to their location. We aggregated the singular and plural forms of shooting(s) ("fusillade(s)") and terror attack(s) ("attentat(s)"). The terms refer to the attacks at the concert hall ("Bataclan", starting at 21:40), in the streets ("Voltaire"), plazas ("République") and subway stations ("Charonne") in Paris 11 th district ("Paris11") where the mass shootings started at 21:20. The explosion at the national stadium ("Stade de France", north of Paris) occurred at 21:17, twenty minutes after the start of the game. We also aggregated diminutives ("Répu") and variants ("ParisXI"). "Porte(s)Ouverte(s)" ("open doors" singular and plural) refers to the keywords used by Parisians to provide shelter to those afraid to travel back home after the attacks.

Results

Keywords

Fig.3 Top 10 Twitter keywords during the Brussels attacks in March 2016

During the Brussels attacks (fig. 3), the main keyword refers to the international airport ("Zaventem") bombed at 7:58. "Maelbeek" is the subway station where the second explosion occurred at 9:11. We aggregated the singular and plural forms of several variants of terror attacks in English, Flemish and French, with or without the location ("BrusselsAttack(s)", "attentat(s)", "aanslag(en) +Brussel", "attentat(s) +Bruxelles"). The other keywords are "victims" ("victimes") and "wounded" ("blessés") in English and French when associated to Brussels. During the attack in Nice, France (fig. 4), the city's name was difficult to use, as "nice" is a popular English word. It is commonly associated to the number of the French Riviera department as a hashtag (#Nice06). The only other location name in the top 10 keywords was Promenade des Anglais on the waterfront, where the attack started around 22:30. We aggregated the singular and plural forms of several variants of terror attacks, with or without the location ("attentat(s)", "NiceAttack(s)", "attentat(s) +Nice", "attaque(s) +Nice"). The other keywords are "victims" ("victimes") and "wounded" ("blessés") in French when associated to Nice, the variants of "open doors" ("Porte(s)Ouverte(s)"), as well as the keyword used by people seeking news of their relatives ("RechercheNice").

Geotagged tweets

Fig.5 Geotagged tweets distribution during the Paris attack

In Paris, geotweets represented 5.5% of the Paris sample, but only 0.5% (1,826 tweets) were located in the Paris area in the three hours that followed the attacks (fig. 5). Tweets geotagged within Paris represented 18% of the French geotweets sample, while the Parisian population represented 4% of the total French population. There were irregularities regarding the geotweets to population ratio as some medium-sized cities (Reims, Saint Etienne, Grenoble, Dijon) did not well, while cities like Villejuif (51 geotweets for 56,661 inhabitants) and Creutzwald (46 geotweets for 13,255 inhabitants) were well represented.

Fig.6 Geotagged tweets distribution during the Brussels attack

In Brussels, geotweets represented 5.3% of the Brussels sample, but only 0.3% (227 tweets in Brussels and 24 tweets in Zaventem) were located in the area in the three hours that followed the attacks (fig. 6). Geotweets within the Brussels area represented 36% of the Belgium events geotweets while Brussels population accounted for 10% of the total Belgium population, which reveals the spatial effect of the event. There was a major contrast between the French-speaking regions in the South (only 12% of the geotweets) and the Flemish-speaking regions in the North (52% of all Belgian geotweets).

Fig.7 Geotagged tweets distribution during the Nice attack

In Nice, geotagged tweets represented 6.1% of the Nice sample, but only 0.3% (291 tweets) were located in Nice during the three hours following the attack (fig. 7). Geotweets within Nice represented 7% of the Nice events geotweets sample while Nice population accounted for 1.4% of the total French population. Paris municipality is still the first geotagged city (405 geotweets), almost at a tie with the entire Nice area (438 geotweets) but with twice as many inhabitants (2.2 million inhabitants in Paris vs. 1.1 million inhabitants in the Nice area). Southern cities in the Nice and Marseille areas were well represented.

Timelines

For every event in all three case studies, warnings from official accounts were sent one to two hours after the attacks had started (fig. 8). The press systematically had a much quicker reaction time.

Overall, the peak of tweets related to the event occurred before the official alert. Delays were shorter for the events that followed the first attack, and they were generally shorter in Belgium than in France. In Paris, the first tweet from an official account occurred at 23:15 and was made by the Municipality of Paris (@Paris), urging people to stay indoors and to wait for further official instructions (fig. 9A). The only tweet from first-responders (@PompiersParis) was made later, at 23:29, to warn that their emergency hotlines were flooded by distress calls. The only tweet from the police (@PoliceNationale) was to ask people to stop spreading rumors, at 23:58. It was later translated in English and German, but still they did not raise the alarm. The 24-hours news TV (@BFMTV) did raise the alarm at 21:50, informing of several shootings in the streets of Paris, then of the explosion at the stadium at 22:10, and later of the attack on the Bataclan concert hall at 22:42.

The tweeting pattern remained the same before and after the attacks, making it difficult to spot the dramatic events without content analysis. The local press (@LeParisien) did not tweet until after midnight. Many official accounts, such as the one of the French Ministry of Internal Affairsknown by the name of its address in Paris @Place_Beauvau -did not tweet that night.

The same is true for bottom-up tweets (fig. 9B): users were much more engaging with the press than first-responders, local authorities or the police. Overwhelmingly, these tweets requested more information, some shared substance with official accounts, and a minority were conversational (replies, retweets, etc.). However, almost all questions and attempts to interact remained unanswered. Only after the alarming tweets from the press -first around 22:00 and then after 22:40 -did the tweeting pattern began to change, making an unusual situation visible.

Overall, locations (e.g. "Stade de France") were mentioned both before and after the events (fig. 9C), making it difficult to detect an irregularity before their occurrence quickly soared, especially as regards to the Bataclan after 22:40 -i.e. after it was explicitly mentioned in the press.

Similarly, the steady increase in the occurrence of the term "shootings" ("fusillade(s)") started once it had been mentioned in the press. The peak of tweets mentioning the top 10 keywords occurred around 23:10, almost two hours after the start of the attacks but before the first official tweets. As the evening progressed, the bulk of the tweet volume moved from generic keywords (shootings, attacks, etc.) to specific locations (Voltaire, Charonne, etc.), from mostly French to a growing use of English (#ParisAttack(s)), and from a description of events to offers of help ("open doors", #PortesOuvertes).

Extracting only geotagged tweets (5.5%) did not alter the sequence and pattern of the timeline (fig. 9D). Because it dealt with smaller volumes, it was easier to see the "noise" before the events and the very few tweets mentioning shootings in Paris before the press raised the alarm at 21:50.

Fig.10 Timeline of tweets during the Brussels attacks in March 2016

In Brussels, the first tweet from an official account to raise the alarm was sent by the municipality at 9:08 (@VilleBruxelles), and at 9:09 by the national crisis center (@CrisisCenterBe) (fig. 10A).

After the 9:11 explosion at the Maelbeek subway station, @CrisisCenterBe announced the interruption of all public transport services at 10:05 in different languages, but still in reference to the Zaventem events. At 10:21, it informed of the saturation of the emergency hotlines in three languages: "Don't call. Use texting or social media. Network is getting saturated #Brussels". Only after 11:36 did the official accounts start mentioning the surroundings, but the subway station and the explosion were not officially specified until the afternoon. The national TV (@RTBFinfo) tweeted the first explosion at Zaventem at 8:18 and located the second one at the Maelbeek station at 9:28. The local press (@LaCapitale_be) was slower -at 8:35 and 9:48 respectively. The media tweeting pattern slightly changed around 8:30, with a sudden greater volume of publication, making it easier to detect the dramatic situation.

At first, users shared information, testimonies and information requests substantially more with the press than with first-responders (fig. 10B). Interactions with the local authorities and the police started to appear around 9:00, and the national crisis center quickly took over afterwards. After 10:00, the majority of tweets were inquiries, requests for more detailed information, replies, etc., and the various accounts failed to publish enough tweets to cope with all these interactions.

The name of Zaventem airport started to emerge moments before the location of the explosion was mentioned in the press (fig. 10C). The name of Maelbeek station was also introduced right before the press disclosed it. There remained a 15 to 20 additional minutes delay before the volume was large enough to detect an unusual event. The qualification of the events only arose after further delay and in much smaller volumes. Some of them were already used before the events for unrelated comments, making it challenging at first to separate signal from noise. The peak of tweets occurred before the first official accounts raised the alarm.

Extracting only geotagged tweets (5.3%) did not alter the timeline (fig. 10D). Reaction delays, however, seemed slightly longer during the first half hour after each event. In Nice, the first tweet from an official account was published at 23:49 by the local police (@PMdeNice) (fig. 11A). The message in French was to stay inside because of ongoing police operations. Only after 00:37 did official accounts begin to mention the term "attack" and the location, mostly to counter rumors of a hostage situation. The local press (@NiceMatin) disclosed the attack and location at 23:00, and the TV (@BFMTV) tweeted breaking news at 23:43. The tweeting pattern remained the same before and after the attacks. Many official Tweeter accounts, such as that of local first-responders (@sdis_06) or the regional crisis center (@pref13), did not tweet that night.

Here again, users first interacted much more with the press than with first-responders (fig. 11B). The first peak followed the first tweet from the local press, asking what was going on in Nice, and the second stemmed from outrage over media coverage and/or police's request not to disseminate images or information. Though initially very low, interactions with first responders increased throughout the night and official accounts failed to publish enough tweets to even cope with genuine questions, information feedbacks or expressions of support.

The reaction time of users during the Nice, France attacks (fig. 11C) placed them right before the local press (23:00), although the increase became considerable an additional 15 minutes afterwards.

The timeline first swelled with the qualification of the events; location keywords appeared later ("Promenade des Anglais"). Tweets were increasingly published in reaction to the press breaking news and coverage rather than to share first-hand information. The peak of tweets occurred before the official accounts raised the alarm. Only later in the night did tweets deal with offering help ("Porte(s)Ouverte(s)") or inquiring about relatives ("RechercheNice") rose.

Extracting only geotagged tweets (6%) did not alter the timeline (fig. 11D). It facilitated the spotting of the first mentions of the events, even if the volumes remained extremely low before the first tweet from the press at 23:00. The time lag for first-hand information is below 10 minutes but only returned consequences of the attack, which implies that there was no direct visual testimony (Fig. 12). To the exception of Zaventem and Nice, first-hand information represents less than 2% of the whole information. Unless it was published by a journalist, first hand-information was not easily accessible because users did not necessarily use hashtags or understood what was happening. This was different in the case of multi-events, as information from previous events increased the noise. In the Stade de France attack, there was some confusion between people tweeting in-situ and people tweeting from their home, the game being broadcasted live, which explains why there were very few tweets about the on-site situation. Moreover, the explosions occurred just outside the stadium, which explains why it was not easy for people in the stands to apprehend the situation. During the shootings that took place in the 10 th and 11 th arrondissements, locals published the first tweets three minutes after the events had begun. The rare pictures taken after the shooting that took place at Le Carillon came from the user @pierre75010 who took photos of the restaurant from his window, or from the user @VinBerth, a journalist from BFM TV6 (fig. 13). Police already appeared on the second photograph. The Bataclan shooting happened inside a building. The only tweets we have regarding the Bataclan were made by people who were hearing gunshots from the outside and by people who were escaping the building. Concerning the Zaventem bombing, the firsts tweets were published three minutes after the explosion from passengers explaining that there had been an explosion in the airport (fig. 14) and users immediately tagged the media (#vtmnieuws, @rtlinfo, @BFMTV, @RTBFinfo). In this case, the approach was different, as users were not trying to gather news but rather to alert others. The first picture of the event was taken from inside the airport train station, three minutes after the blast, but the user did not have any information about what was happening (fig. 15). Moreover, this picture is embedded in a tweet that does not have any of the keywords from our many-to-many datasets. We only discovered it because it was subsequently shared by another user who had used the keywords in our queries. A journalist in the parking lot outside the airport took the second picture. The information is qualified, referenced with fact, hashtags and highlighted with the "BREAKING" announcement (fig. 15). The Maelbeek dataset is filled with information about Zaventem because the two events took place in a short period of time. The first tweet came from a subway passenger who did not understand what was happening, the traffic having just been interrupted. Figure 16 shows that the journalist arrived on the scene eight minutes after the detonation, using the hashtag "Brussels". The second tweet shows a police patrol arriving on site ten minutes after the explosion. With the Nice attacks, the first tweet was written 13 minutes after the attack, asking the local media @Nice_Matin if they had any information regarding an attack. We did not find a tweet that provided evidence of the attack in the first fifteen minutes, but we detected a video published in that time frame on Twitter that did not appear in our dataset (fig. 17). The video shows people running from the attack area. We checked and found that our keyword research had not uncovered this video because the text message did not match any of our keywords, in fact the user seems to have a difficult time understanding the seriousness of the situation.

Finding needles in haystack

Discussion

We have found that only 5% to 6% of tweets are geotagged, depending on the event. This represents however some of the highest rate of geotagged tweets reported in the literature [START_REF] Graham | Where in the World Are You? Geolocation and Language Identification in Twitter[END_REF][START_REF] Takhteyev | Geography of Twitter networks[END_REF][START_REF] Tsou | Visualization of social media: seeing a mirage or a message? Cartogr[END_REF]. The strong correlation between geotweets and population density previously reported in the literature [START_REF] Li | Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr[END_REF] does not fully explain the spatial distribution of geotweets. A social analysis of social media users might help understand such a distribution and the differences in proportions of relevant tweets to noise [START_REF] Crutcher | Placemarks and waterlines: Racialized cyberscapes in post-Katrina Google Earth. Geoforum, Themed Issue: The 'view from nowhere'? Spatial politics and cultural significance of high-resolution satellite imagery[END_REF]. Such an approach would however stumble on the various biases previously highlighted. Mixing quantitative and qualitative approaches provided better insight on what was happening on the ground, corroborating previous studies [START_REF] Shelton | Mapping the data shadows of Hurricane Sandy: Uncovering the sociospatial dimensions of 'big data[END_REF][START_REF] Tsou | Visualization of social media: seeing a mirage or a message? Cartogr[END_REF]. Choosing Twint over the Twitter API enabled to gather vast tweets datasets for each three-hour time slot, as could have been done in real-time during the events. As such, Twitter's real time monitoring tool, the Streaming API v1, only returned a free 1% sample [START_REF] González-Bailón | Assessing the bias in samples of large online networks[END_REF][START_REF] Hiriart | Le citoyen, l'expert et le politique : une rationalité complexe pour une régulation excessive du risque[END_REF]. We were then able to better grasp the social media situation during the response phase. No previous studies have conducted real-time analyses to the best of our knowledge, and most have been relying on samples through the APIs. Some studies produced recommendations for managing social media data during the recovery phase [START_REF] Pereira | Transportation in Social Media: An Automatic Classifier for Travel-Related Tweets[END_REF][START_REF] Yin | Using Social Media to Enhance Emergency Situation Awareness: Extended Abstract 5[END_REF][START_REF] Zhenlong | A novel approach to leveraging social media for rapid flood mapping: a case study of the 2015 South Carolina floods[END_REF][START_REF] Zou | Mining Twitter Data for Improved Understanding of Disaster Resilience[END_REF], yet they usually fail to considered the application for actual emergency management during the response phase [START_REF] Crawford | The limits of crisis data: analytical and ethical challenges of using social and mobile data to understand disasters[END_REF]. Other studies did develop crisis maps based on near-real-time geotweets [START_REF] Chen | Implementing a real-time Twitter-based system for resource dispatch in disaster management[END_REF][START_REF] Middleton | Real-Time Crisis Mapping of Natural Disasters Using Social Media[END_REF] but failed to discuss Twitter's API bias. Our results confirm most of the previously discussed biases of SMEM, however emergency-managers and decision-makers are consistently overlooking them in the name of speed, dissemination, multimedia information and citizen engagement.

The findings from the three case studies do not fully support such too often unchallenged expectations. Firstly, social media do not enable a faster dissemination of alerts and early warnings, as we have identified systematic delays of one to two hours. The peak of tweets related to the attacks consistently occurred before official accounts issued an alert, and there was no shortage of press alerts in the meantime. Secondly, although social media did foster bottom-up processes, users appealed to the press first, even the few who shared first-hand information and multimedia content; this contributed to eclipsing first-responders and officials accounts. Users reacted to the press in a first phase. Past the first hour, social media boiled down to amplifying political controversies or outrage concerning news coverage, further drowning any information of relevance to emergency management. Thirdly, although extracting only geotagged tweets did not alter the sequence and pattern of the timeline, on-site geotagged information only represented less than 0.5% of the collected tweets for each event. Fourthly, for each case study, only 3 to 15 Twitter accounts did indeed provide multimedia, context-rich, crowdsourced feedbacks from the scene of the events: first-hand information was very scarce and hard to find amongst the background noise in a timely manner. Furthermore, users were not always able to make sense of the events or images they were reporting. Also, users were not aware of the hashtags, keywords or accounts that could help make their information easier to detect or to localize. Last but not least, first-responders are frequently already at the scene in the case of shared multimedia content, defeating the whole purpose of collecting data on social media for emergency management.

While previous studies did not systematically address the four SMEM biases (fig 1A), some limitations arise from the particular scope and timing of the three European attacks. First, their local extend limited to a couple of neighborhoods over hours (fig 1B) directly affected a relatively limited number of people. The international status of these cities prompted many more people to comment in several languages though, and this might explain our finding of a low in-site geotagged rate. While it could be different in the case of disasters with a wider scope and duration, this has not been examined in the literature yet. Second, the number of affected people (fig 1C) might also impact the signal to noise ratio. During a disaster affecting a larger population, such as major floods or earthquakes, a larger share of social media users might have access to first-hand information which could possibly improve to signal ratio. Again, we have to leave it to conjecture as this has not been thoroughly investigated in previous studies. Third, the security dimension of terror attacks might slow down the dissemination of official information and warnings, enhancing the relevance of SMEM bias 3. It might also generate stronger feelings as we saw with the development of #prayfor hashtags and attract more political comments [START_REF] Starbird | Rumors, False Flags, and Digital Vigilantes: Misinformation on Twitter after the 2013 Boston Marathon Bombing[END_REF], lowering the signal to noise ratio as a result. On the other hand, we expect the data repressiveness issue (bias 1) to remain consistent across disaster types and case studies because it might be inherent to social media. We recommend future research to more systematically address these four biases in a variety of case studies to cover the full extent of our proposed SMEM framework while comparing different local governance and geographical context.

Contrasting Twitter 'cultures' in France and Belgium might explain some differences in our results. Unfortunately, to the best of our knowledge, there is no literature on Twitter 'cultures' in Belgium or France, nor a comparison between the two countries, or between the various linguistic communities. However, the police requested in each country the general public to stop sharing information on social media during two major antiterrorist operations in Paris and Brussels consecutive of the 2015 attacks, allowing for a direct comparison: the November 18 th assault of a cell in the Saint-Denis suburbs of Paris, and the Brussels' lockdown of November 22 nd to carry out arrests of accomplices.

In Paris, official Twitter communication kept repeating not to cross the security perimeter and citizens to stop obstructing the operation or sharing pictures and location of police forces. In contrast, in Brussels, the communication was more citizen-inclusive, and the public response more social media savvy. Most Brussels related topics on Twitter were flooded by users with cat pictures and jokes that day, in the attempt of drowning out any possible unintentional information leakage. On the next day, Belgian official Twitter accounts followed-up on such humorous means of coping with a crisis situation by thanking the general public with pictures of cat food [START_REF] Jensen | Brussels will land on its feet like a cat': motivations for memefying #Brusselslockdown[END_REF].

By comparing the three case studies, we found that multi-events are different because they are intertwined with information concerning previous events, but with shorter communication delays. Delays in Belgium were always shorter, the crisis center had been working with SMEM since 2013.

Regarding the first events to occur, with the exception of the Stade de France failed attack, the highest ratios of users discussing the events were in Zaventem and the city of Nice. The Zaventem case is probably due to circumstances, because of the fortuitous presence of journalists at the airport. Otherwise, in-situ witnesses have, at first, great difficulty interpreting the events, which complicates the detection and crowdsourcing of information, even when images or videos are being shared. The time difference for the press to react is partially explained by the fact that French users tag the press to seek for information, while Belgian users tag the press to prompt it to spread firsthand information. The more comprehensive approach of SMEM in Belgium could explain a halving of the alert times, but warnings were published an hour after the events nonetheless. There is no difference regarding the rate of geotagged tweets in the three case studies. However, analyzing the location of an event using geotweets is not relevant for all cases, as the Paris area was more tagged than the Nice area during the city of Nice attack.

Conclusion

During the past decade, social media have sparked increasing appeal for improving emergency management. The current focus on social media data collection during days or weeks following disasters is, however, poorly suited to assess real-time SMEM or iVGI usability. Our analysis of top-down, bottom-up and many-to-many Twitter uses during the first hours before and after several recent attacks in France and Belgium raises questions regarding emergency-managers and decisionmakers expectations. While our results confirm most of the biases of SMEM, they also challenge the very reasons why such biases are usually overlooked in the literature and on the ground. Contrary to expectations, first-hand information is easily drowned in the background noise and too often submerged by comments on previous events in the case of multi-event emergencies. In addition, on-site geotagged information only represented an infinitesimal portion of the tweets, even with the most targeted collection. Moreover, the context-rich multimedia shared by users almost systematically depicts first-responders already at the scene and/or users are not always able to make sense of the events they are witnessing in due time. Furthermore, we found a delay of one to two hours between the events and tweets from official accounts, the press consistently being first to alert the public. Users also called upon the press first, overshadowing first-responders, while official accounts lacked the ability to respond to all direct requests. While terror threats have specific time and space settings, it is likely that delays at least equivalent will be found in other emergencies and disasters, in particular those with longer timeframes, such as floods, wildfires, earthquakes or storms. This will require more studies, in different places, with variation among hazards, threats, temporal and analysis scale. We also recommend future research to more systematically address the SMEM four biases to better disentangle what derives from the disaster or case study and what derives from social media and users. We suggest a method to collect Twitter datasets circumventing the API limitations. However, these limitations have recently been reduced with the new Twitter API v2. While it may be possible to reduce warning times by distributing the decision-making or using ready-made messages, more inclusive approaches such as the cell broadcast technology may seem more appropriate for early warning [START_REF] Aloudat | The application of location-based services in national emergency warning systems: SMS, cell broadcast services and beyond[END_REF]. As for the bottom-up flow of information, one approach could be to define an official emergency hashtag and to train users; another solution would be to rely on the press to filter out relevant information, since users call upon the press first in any case. In all case studies, emergency managers have been asking the public to switch to social media when their call centers were overloaded. We advise caution as resources might be better spent than looking for weak, scattered, and not necessarily speedier firsthand information on social media.

Fig. 2

 2 Fig.2 Top 10 Twitter keywords during the Paris attacks in November 2015

Fig. 4

 4 Fig.4 Top 10 Twitter keywords during the Nice, France attacks in July 2016

Fig. 8 Fig. 9

 89 Fig.8 Delays between the attacks and tweets mentioning them for each event

Fig. 11

 11 Fig.11 Timeline of tweets during the Nice, France attacks in July 2016

Fig. 12

 12 Fig.12 Overall reaction time during the first 15 minutes.

Fig. 13

 13 Fig. 13 Early pictures after the Paris shooting on Twitter 7

Fig. 14 Fig. 15

 1415 Fig. 14 During the Zaventem attack, the media were made aware of the explosion 3 minutes after it occurred 8

Fig. 16

 16 Fig. 16 Early pictures and video of the Maelbeek event 10

Fig. 17

 17 Fig. 17 People fleeing in an early video during the Nice attack 11

Event Year Disaster type Duration Scope Affected people Prior Warning Literature Bias 1 Bias 2 Bias 3 Bias 4

	Affected
	Earthquake Haiti Warning people	Earthquake 10s	Days	100s	National		1 000 000 1000s	No		Yates and Paquette, 2011 10 000s	Not addressed 100 000s applicable Not	Not applicable Millions addressed Not
	South Carolina Wildfires	Fire 2012 Canyon Fire (Kent and Capello,	Months 2013 Boston Regional Bombing		10 000 2014 Riot	Yes		Sutton et al., 2008	Not addressed	Yes	Not applicable	Not addressed
	Riot Venezuela Boston Marathon No prior warning bombing	Riot Attack 2013) 2015 Gunsman in the newsroom (Jong and Dückers, 2016)	Months Minutes (Starbird et al., 2014) National Local 2014 Fergusson unrest (Bunting and Stamatel, 2019)	1 000 100 Venezuela (Debos and Lancini, 2017) 2016 Nice Attack	No No	Debos and Lancini, 2017 Starbird et al., 2014 2015 Paris Attacks 2016 Brussels Attacks 2007 South Carolina	Not addressed Not addressed 2013 & 2014 Floods Not addressed Yes in Jakarta (Kusumo et al., 2017) 2011 Tohoku	Yes Yes 2010 Earthquake Haiti Not addressed (Yates and Paquette, 2011) Not addressed
	Flash flood France Gunman in the Newsroom Prior warning	Flash flood Hostage	Hours Hours		Local Local	100 2010 Flash Flood 10 2017) France (Douvinet et al.,	Yes No floods (Zhenlong et al., Douvinet et al., 2017 wildfires (Sutton et al., 2008) Jong and Dückers 2016 2015 South Carolina 2011 European floods (Rizza et Pareira, 2014)	Not addressed Earthquake and Not addressed Tsunami Not addressed Yes Katrina (Crutcher and 2005 Hurricane (Acar and Muraki, 2011)	Yes Yes 2012 Hurricane Sandy Not addressed addressed Not (Wang et al., 2019)
	Hurricane Sandy	Hurricane	Days	International 1 000 000	Yes		Wang et al., 2019 2017)	Yes	Not addressed Zook, 2009	Not addressed	Yes
	Tohoku Earthquake & Tsunami	Earthquake	Minutes International 100 000	Yes		Acar and Muraki, 2011	Yes	Yes	Not addressed	Not addressed
	Flood Jakarta	Flood	Days		Regional		100 000	No		Kusumo et al., 2017	Yes	Yes	Not addressed	Yes
	Hurricane Katrina	Hurricane	Days		Regional		100 000	Yes		Crutcher and Zook, 2009	Yes	Not addressed	Not applicable	Yes
	Flash flood South Carolina	Flash flood	Days		Regional		10 000	Yes		Zhenlong et al., 2017	Yes	Not addressed	Not addressed	Not addressed
	European floods	Flood	Days	International	10 000	Yes		Rizza et Pareira, 2014	Yes	Not addressed	Not addressed	Not addressed
	Ferguson unrest	Riot	Months	Local		100	No		Bunting and Stamatel, 2019	Yes	Not addressed	Not addressed	Yes
	Horsethief Canyon fire		Fire	Days		Local		10	No		Kent and Capello, 2013	Yes	Yes	Not addressed	Not addressed
	Scope	Duration		Minutes				Hours			Days	Months
		Local	2013 Boston Bombing (Starbird et al., 2014) 2016 Nice Attack	2010 Flash flood France (Douvinet et al., 2017) 2015 Gunsman in the newsroom (Jong and Dückers, 2016) 2016 Brussels Attacks 2015 Paris Attacks	2012 Canyon Fire (Kent and Capello, 2013)	2014 Fergusson unrest (Bunting and Stamatel, 2019)
	Regional				2013 & 2014 Floods in Jakarta (Kusumo et al., 2017)	2005 Hurricane Katrina (Crutcher and Zook, 2009) 2015 South Carolina floods (Zhenlong et al., 2017)	2007 South Carolina wildfires (Sutton et al., 2008)
	National									2010 Earthquake Haiti (Yates and Paquette, 2011)	2014 Riot Venezuela (Debos and Lancini, 2017)
	International				2011 Tohoku Earthquake and Tsunami (Acar and Muraki, 2011)	2012 Hurricane Sandy (Wang et al., 2019) 2011 European floods (Rizza et Pareira, 2014)

  The Paris attacks occurred on November 13 th 2015, with the Stade de France bombing at 21:17, the first street mass shootings at 21:20, and the Bataclan shootings at 21:40. The Brussels attacks took place on March 22 nd 2016, with bombings at the Zaventem airport at 7:58 and at Maelbeek metro station at 9:11. The attack in Nice occurred on July 14 th 2016, as a truck started to run over spectators of the National Day fireworks on the "Promenade des Anglais" at 22:30. In our framework (fig.

The French Ministry of Interior partners with major actors to warn populations in case of an emergency, op. cit.

3The use of social media for communications by Belgian's crisis center. Practical guidance (November 2013) https://centredecrise.be/fr/publication/les-medias-sociaux-en-communication-de-crise

Very recently, Twitter has released a Search API v2. With an academic account, it is now possible to overcome these constraints and theoretically to search the full archive. We have compared both approaches, Twint and Twitter API v2. While the two methods return confirmatory results both in volume and timing -validating our scraping approach -Twint still collects up to 10% more tweets at any moment than the recently updated API.

French Fire and Emergency Departmental service, 2018 official stats https://www.interieur.gouv.fr/Publications/Statistiques/Securite-civile/2018

BFM TV is a French 24-hour rolling news channel.

1 st tweet: "Shooting at the Carillon" 2 nd tweet: "Kalashnikov fire in the Petit Cambodge in the 10 th in #Paris many dead. Paramedics and Police on-site #shot

1 st tweet: "Two #explosions #airvents #Zaventem #vtmniews #vrt" 2 nd tweet: "@rtlinfo @BFMTV @RTBFinfo #zaventem Brussels airport attack #belgium attack"

1 st tweet: "Huge explosion in the @BrusselsAirport train station. We don't know yet what's going on"

1 st tweet: "Explosion in Schuman. The under the bridge entrance exploded! Police just arrived #attack #zaventem" 2 nd tweet: "@Dominique_Rizet Explosion in Arts-loi, Maelbeek (Belgium government) and Shuman (EU) stations"

"I'm in Nice there are mutants crowd movements, we don't know why"


Jesús M Avilés

Elena Solís

Juliana Valencia

Carlos De

La Cruz

Gabriele Sorci

Jesus M Aviles
email: javiles@eeza.csic.es.

Elena Solis

Carlos De La Cruz

J M Avilis

Female and male plumage brightness correlate with nesting failure in azure-winged magpies Cyanopica cyanus

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Bird coloration is produced by chemical pigments (carote noids and/or melanins), or by the physical interaction of the light with feather microstructure (i.e. structural colours; [START_REF] Hill | Preface[END_REF]. Structural colours are broadly distributed throughout Aves [START_REF] Prum | Anatomy, physics, and evolution of structural colors[END_REF], and there is growing evidence that their expression might be condition dependent (reviewed in [START_REF] Hill | Environmental regulation of ornamental coloration[END_REF]. Most studies, however, were carried out on male structural coloration while neglecting female coloration (see [START_REF] Siefferman | Evidence for sexual selection on structural plumage coloration of female eastern bluebirds[END_REF].

The azure-winged magpie is a small colonial corvid in which males appear to invest a considerable effort in reproduction by feeding incubating females and chicks (Cramp and Perrins 1994). Males are only slightly larger than females although both sexes are highly coloured with blue and chestnut as main colours (Cramp and Perrins 1994). To the human eye structural blue is the predomi nant colour on wing and tail which is displayed during the whole year in both sexes.

The first aim of this study was to identify relationships between structural blue coloration and sex and age in the azure-winged magpie. Second, we investigated whether coloration may signal individual quality in both sexes under the assumption that mate quality assessment will be equally advantageous for females and males (see above). In the azure-winged magpie only a small fraction of the nests in a population escape to predation, since about 80% of the nests are depredated [START_REF] Cruz | Rabilargo ( Cyanopica cyanus)[END_REF]. Therefore, coloured traits might provide conspecifics with information on the quality of potential partner in terms of predator avoidance.

Methods

Study site and data collection

The study was carried out in a colony of azure-winged magpies during four consecutive years (2002)(2003)(2004)(2005), at Valdesequera, in the province ofBadajoz, Spain (39° 03'N, 6° 48'W) (see details in [START_REF] Valencia | Parents increase their parental effort when aided by helpers in a cooperative breeding bird. -A nim[END_REF].

Every year we performed an intensive capture using a specially designed trap before reproduction [START_REF] Valencia | Parents increase their parental effort when aided by helpers in a cooperative breeding bird. -A nim[END_REF]. A total of 143 birds were captured and marked. Sex of breeders was always assigned according to their beha viour, being scored as females those individuals that incubated the eggs and brooded the young [START_REF] Valencia | Flexible helping behaviour in the azure-winged magpie[END_REF]. Age was assigned using moult extension, following [START_REF] Cruz | Postjuvenile moult in the azure-winged magpie Cyanopica cyanea cooki. -Ring[END_REF] as: juveniles, birds born in the previous year, and adults, birds older that one year.

We searched for nests at the beginning of the breeding season by inspecting every tree in the study area. Nests were marked and checked at least once every two days to record laying date, dutch size and number of fledglings. Second dutches can occur in our population [START_REF] Cruz | Rabilargo ( Cyanopica cyanus)[END_REF], and thus be confounded with replacement dutches after a predation event, therefore only first dutches were considered in the analyses. Early monitoring of every nest allowed us to identify the members of the breeding pair before the chick hatched. About 50% of nests in our population present a helper [START_REF] Valencia | Flexible helping behaviour in the azure-winged magpie[END_REF]. Helpers usually join the breeding group after the chicks hatched and only occasionally feed the incubating female [START_REF] Valencia | Parents increase their parental effort when aided by helpers in a cooperative breeding bird. -A nim[END_REF]), thus it is always possible to differentiate them from the male breeder. Observations for every nest were made from a hidden position with a telescope for at least lh every two days which allowed us to identify 41 females and 45 males as breeders. Only in 9 nests we were able to capture the two members of the breeding pair. Thus, we performed separately our analyses on males and females when looking at the relationships between blue plumage coloration and reproductive attributes. Although some birds were recap tured between years, we only used data for the first capture in the analyses.

Body size and condition

We used tarsus length as a measure of body size. We also measured body mass using a Pesola balance (± 0.5 g) and used the residuals of a regression of body mass on tarsus length as an index of body condition.

Plumage colour

Upon capture, we collected 2 secondary wing covert feathers from each individual. Feather samples were plucked from the same location on all birds. At the time of data colour collection, feathers of each individual were placed on black paper in a fashion that mimicked the way the feathers lay on the bird. We quantified plumage reflectance in the range 300-700 nm using a spectrometer (DH 2000, Ocean Optics Europe) using a bi-furcated micron fiber optic probe at a 45° angle from the feather surface and illuminating an area of 1 mm 2 . Using OOIBase, a spectra acquisition software package, we recorded 10 spectra relative to a standard white reference (WS-2) sequentially and then averaged the spectra to reduce electrical noise. This process was repeated three times; the probe lifted and replaced on the feather sample each scan. We then averaged the three spectra for each individual. We summarized reflectance data by calculating three different standard descriptors of reflectance spectra: brightness, hue and chroma. Brightness is the summed reflectance from 300-700 nm. UV-blue hue corresponds to the wavelength at which the maximum peak of reflectance is reached within the ultraviolet-blue wave band (300-500 nm). UV-blue chroma was the ratio between the spectral reflectance in the ultraviolet-blue waveband (300-500 nm) and the reflectance of the entire spectrum (300-700 nm).

Recent findings suggest that structural plumage colora tion is not a static trait and that changes in feather coloration may occur after moulting [START_REF] Ornborg | Seasonal changes in a ultraviolet structural colour signal in blue tits, Parus caeruleus[END_REF][START_REF] Delhey | Seasonal changes in blue tit crown color: do they signal individual quality[END_REF]. To account for this potential source of bias, we classified feathers in relation to the period in which they were collected as "freshly" moulted feathers, when collected from birds trapped from October to February, and "late" moulted feathers when collected from birds trapped from March to the reproduction. We subsequently introduced period as a fixed term in all our models to control for possible seasonal changes in plumage coloration.

Statistical analysis

The influence of age and sex on structural blue coloration was tested by running generalized linear mixed models (Mixed procedure in SAS, SAS Institute 1996) in which sex, age and period were entered as fixed factors and year was entered as a random effect. The associations between body size and condition with colour variables were tested by running general linear models in which sex, period and year were entered as fixed effects. Analyses on nests were performed with general linear models (laying date and dutch size) and logistic regressions (nest failure probability, Genmod procedure in SAS) including period and year as fixed effects. Past research demonstrated that the number of helpers at the nest influence reproductive success [START_REF] Valencia | Flexible helping behaviour in the azure-winged magpie[END_REF]. Therefore, we control for effects of helpers on nesting failure probability by including the number of helpers as a covariable. Only some individuals were recorded in the colony during the reproduction in the same year in which the feathers were collected which reduced our sample size for testing relationships between magpie coloration and nest traits (i.e. laying time, clutch size and nest failure probability). Model selection was carried out by removing, one by one, the effects that were the furthest to statistical significance. Due to low sample size we disregarded testing interactive effects between independent variables.

Results

Plumage coloration

The wing coverts of azure-winged magpies reflect most strongly in the violet and the blue regions of the bird-visible spectrum (Fig. 1). The average hue for the sampled feathers was 427 ± 29 .0 nm, well within the blue range (400-475 nm; Fig. 1). Mean values of secondary wing covert colour predictors and of morphological traits are reported in Table 1. Variability of colour predictors was similar for female and male azure-winged magpies (Table 1).

Plumage coloration in relation to age, sex and period

We did not find any sexual or age related variation in brightness and UV-blue hue (Table 2). UV/blue chroma, however, was significantly related to sex (Table 2): males have more saturated UV/blue coloration than females (Table 1).

Blueness varied seasonally (Table 2). Feathers collected from birds captured from October to February had more brilliant and saturated blue coloration and showed a more violet hue than those collected afterwards (brightness (mean±SD), freshly moulted 2571.1 ±47.0 vs late moulted: 2289.5±26.3; UV-blue chroma (mean±SD), freshly moulted 0.55 ± 0.0 vs late moulted: 0.54 ± 0.0; UV-blue hue (mean±SD), freshly moulted 416.8 ±6.61 vs late moulted: 430.7 ±2.36).

Plumage coloration and pairing

We did not find evidence for assortative mating with respect to blue coloration (brightness: F1,7 =0.00, P =0.95; UV blue chroma: F1,7 =2.55, P =0.15; UV-blue hue: F1,7 = 1.87, P = 0.21). However, there was assortative mating with respect to tarsus length (r =0.72, F1,6 =6.40, P =0.04), and body condition (r =0.87, F1,6=18.93, P =0.004). These results could not be driven by age-assortative pairing since yearlings were similar in size (F1,139 =3.07, P =0.08), and had similar condition than adults (F1,134 =0.006, p =0.93).

Plumage coloration and body size and condition

Neither colour variable was retained in final models predicting body size and condition (colour variables excluded with 0.31 < P < 0.86.

Plumage coloration and breeding outcome

Female and male blue coloration was unrelated to laying date and/or clutch size (laying date females: colour variables excluded with 0.16 <P <0.77; laying date males: colour variables excluded with 0.63 <P <0.73; clutch size females: colour variables excluded with 0.07 <P <0.90; clutch size males: colour variables excluded with 0.26 <P <0.71). However, after deleting non significant terms, nests of fem . ales with brighter f lumages were less likel � predated durmg the season (Xi, 2 6 =6.15, P =0.01, Fig. 2). In addition, nests attended by brighter males were less likely predated (xi,36 =4.81, P =0.03, Fig. 2).

Discussion

Our results provide support for the existence of sex-related expression of blue coloration in the azure-winged magpie since males tended to have more saturated UV-blue coloration than females. Among individuals, the expression of blue plumage colour was not correlated with body condition or size, and based on a reduced number of nests we found no support for assortative mating in relation to blueness. Female and male blueness did not predict laying date or clutch size. However, nests of brighter females were those more likely escaping to predation. Furthermore, nests attended by brighter males were less predated than those attended by duller males.

We have found that the feather colour of the azure winged magpie changed seasonally. Wing coverts became less UV-blue reflectant with time, while overall brightness decreased and feathers tended to become greener. The pattern of variation in plumage coloration reported in this study was qualitatively similar to that reported by [START_REF] Ornborg | Seasonal changes in a ultraviolet structural colour signal in blue tits, Parus caeruleus[END_REF] for the crown feathers in blue tits Parus caeruleus (see also [START_REF] Delhey | Seasonal changes in blue tit crown color: do they signal individual quality[END_REF]. A further practical consideration of this result concerns the need of statistical control of seasonal changes in coloration [START_REF] Delhey | Seasonal changes in blue tit crown color: do they signal individual quality[END_REF], such as we have done, to avoid biasing other statistical comparisons in studies of plumage signalling.

Based on only 9 pairs we did not find evidence that pairing patterns in azure-winged magpies were based on blue plumage coloration. However, azure-winged magpies did not mate randomly since individuals in better condition tended to mate with individuals of similar quality. Although low sample size makes unlikely any firm conclusion, perhaps, azure-winged magpies rely in other elaborate traits and or behaviours not measured in this study. Alternatively, it could be possible that there was a positive correlation between female and male blueness onl y at the time of pair formation. Indeed, in another study we have found that differences in blueness between breeders and helpers are maximal in winter, when pairing presumably occurs [START_REF] Solis | Winter male plumage coloration correlates with breeding status in a cooperative breeding species[END_REF].

Plumage brightness in female and male azure-winged magpies serves as a predictor of nestling failure probability. have failed to find such a link. Only [START_REF] Siefferman | Evidence for sexual selection on structural plumage coloration of female eastern bluebirds[END_REF] have previously found evidence for a positive relationship between breeding success and structural colora tion in females of the eastern blue bird. Our results could be due to a higher female investment in predator avoidance when maced with a highly ornamented male (Burley 1986).

However, there was no evidence of assorcative mating with respect to blueness in our population (but only nine pairs). Alternatively, plumage brightness may signal capacity of nest defence against predators irrespective of sex. Finally , chis result was unaffected for helper presence which has previously been demonstrated to influence breeding success in azure-winged magpies [START_REF] Valencia | Flexible helping behaviour in the azure-winged magpie[END_REF].

In conclusion, we have found support for the view that structural blue coloration could be favoured by sexu al selection and may have a signalling function in both sexes of the azure-winged magpie. In addition, our results suggest a link between the ability of avoiding predators and plumage coloration in chis species. Experimental studies are required to understand how chis blue plumage coloration is selected and maintained in the azure-winged magpies.

Fig. 1 .

 1 Fig. 1. Average reflectance spectra for the azure-winged magpie secondary wing cover feathers (n =45 individuals). Values are means of the means for each individual (i.e. three measures for each individual).

Fig. 2 .

 2 Fig. 2. Female (a) and, male (b) plumage brightness (mean± SE) in relation to nesting failure.

Table 1 .

 1 Structural plumage coloration descriptors and morphological traits (mean ±SD) of adult male and female azure-winged magpies.

	Trait		Males			Females
		Mean	SD	n	Mean	SD	N
	Brightness	2368.4	320.9	84	2339.4	265.8	59
	UV-blue chroma	0.6	0.02	84	0.5	0.03	59
	UV-blue hue (nm)	426.6	29.5	84	428.7	28.6	59
	Tarsus length (mm)	34.9	0.9	82	34.3	1.1	59
	Bill length (mm)	54.0	1.0	27	52.3	1.4	29
	Wing length (mm)	132.6	3.5	27	127.5	3.1	29

Table 2 .

 2 The influence of age, sex and period on structural blue coloration of the azure-winged magpie.Model selection was carried out by removing, one by one, the effects that were the furthest to statistical significance, starting with the highest order interactions down to the main effects. Fixed terms included in the final model are in bold.

	Effect	Brightness	UV-blue chroma	UV-blue hue
	Sex Age Age xsex Period Year	F,,137 =1.53, P =0.22 F1.136=0.17, P=0.67 F1.13s =0.12, P =0.73 f1,l38 =19.93, p <0.001 z =0.84, p =0.19	f1,u1=4.72, P=0.03 F1 , 136=0.03, P=0.87 F1 13s =0.03, P =0.87 f1:137 =3.42, p =0.06 z =0.17, p =0.43	F1,136 =0.20, P =0.66 F1.137 =0.89, P =0.35 F1. m =0.02, P =0.88 f1,l38 =6.22, p =0.013 z =0.27, p =0.84

Acknowledgements -J. J. Cuervo, A. Roulin and two anonymous referees made useful suggestions to previous drafts. This scudy was partially supported by FEDER and Junta de Excremadura (Project IPR99A020) co C.C., E.S. and G.S.


Imene Bousahba

Jérémie David

Yves Guerin

Benoit Cohade

Didier Rémond

Nathalie Poupin

Sergio Polakof

Metabolic adaptations in insulin resistant and dyslipidemic minipigs ranging from lean

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Metabolic adaptations in insulin resistant and dyslipidemic minipigs ranging from lean to morbid obesity.

Authors: Imene Bousahba 1,2* , Jérémie David 1 , Yves Guerin 1 , Benoit Cohade 1 , Didier Rémond 1 , Nathalie Poupin 2 and Sergio Polakof 1 .

1 Université Clermont-Auvergne,INRAE,UMR1019,Unité Nutrition Humaine,Clermont-Ferrand, France.

2 UMR1331 Toxalim,Université de Toulouse,INRAE,INP-Purpan,Toulouse,France.*imene.bousahba@inrae.fr Introduction: Type 2 diabetes (T2D) is a silent and slowly progressive metabolic disease to which obesity is a major contributor and whose prevention could be significantly improved if individuals at risk could be identified earlier (1) . Our aim is therefore to identify phenotypes that anticipate the onset of T2D in minipigs (2) for the determination of early metabolomics-based biomarkers of T2D. Methods: We fed four groups of minipigs (n=5-10) either on normal-fat or on a high-fat high-sugar (HFHS) diet during 2, 4 or 6 months. Body weight (BW), lean mass, fat mass (FM) and morphometric features were recorded.

Biochemical, hormonal and inflammatory parameters were assessed on fasting plasma samples. Results and discussion: Based on multivariate statistical analysis, we observed that 4 different groups were differentiated, with 30% of the variance explained by both morphological (BW, FM) and clinical (total and HDL cholesterol, insulin, HOMA-IR and incretins) components, allowing to identify 4 distinct phenotypes: lean (LP), overweight (OWP), obese (ObP) and morbidly obese (MObP). Compared to the LP, we observed a BW increase of 2, 2.5, and 3-fold in the OWP, ObP and MobP respectively, mostly due to the FM accretion (4, 7 and 11-fold respectively). Concerning the clinical phenotype, insulin-resistance (IR) was already present in the OWP, which was further accompanied by a significant hyperinsulinemia (p = 0.03) that increases by 3.5-fold for the OWP, ObP, and MobP. Interestingly, IR affected rather lipid (2-fold increased free fatty acids) than glucose (steady glucose levels) metabolism. We also observed mild dyslipidemia with increased total cholesterol (1.3-fold) and HDL (2-fold) for all 3 phenotypes, as well as the incretin hormones (GLP-1, GIP) which actions are jointly responsible for 60% of insulin-secretion (GLP-1 p=0.02), preventing hyperglycemia. These variations are surprisingly maintained in the ObP and MObP despites of the strong weight and FM gain. The clinical phenotype was further completed by a rather proinflammatory pattern (slightly higher TNF-a, TGF-B) offset by a significant increase (3.7-fold) of IL-4 in the ObP, yet not confirmed in the MoBP. Conclusion: Our results show that in minipigs ranging from lean to morbid obesity, IR was rapidly installed and accompanied by dysregulated lipid metabolism, while hyperglycemia was prevented by improved incretin adjustments. This ability to regulate the metabolism in the presence of progressive concomitant BW gain and adiposity with a background of IR and mild dyslipidemia makes of this model an interesting tool for the ongoing early T2D metabolomics-based biomarker's discovery. References: 1. Chatterjee et al, The Lancet, 2017. 2. Koopmans et al, European Journal of Pharmacology, 2015.