|
Kartikeya Bajpai, & Anuj Jaiswal. (2011). A framework for analyzing collective action events on Twitter. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Recent years have witnessed multiple international protest movements which have purportedly been greatly affected by the use of Twitter, a micro-blogging platform. Social movement actors in Iran, Moldova, Kyrgyzstan and Thailand are thought to have utilized Twitter to spread information, co-ordinate protest activities, evade government censorship and, in some cases, to spread misinformation. We propose a framework for conceptualizing and analyzing Twitter data related to contentious collective action crises. Our primary research goal is to delineate a framework informed with a social movements lens and to demonstrate the framework by means of Twitter usage data related to the Thailand protests of 2010. Our proposed framework concerns itself with two aspects of protest activities and Twitter usage, namely, analyzing the content and structure of messages and our construct of Twitter protest waves.
|
|
|
Soudip Roy Chowdhury, Muhammad Imran, Muhammad Rizwan Asghar, Amer-Yahia, S., & Carlos Castillo. (2013). Tweet4act: Using incident-specific profiles for classifying crisis-related messages. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 834–839). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: We present Tweet4act, a system to detect and classify crisis-related messages communicated over a microblogging platform. Our system relies on extracting content features from each message. These features and the use of an incident-specific dictionary allow us to determine the period type of an incident that each message belongs to. The period types are: Pre-incident (messages talking about prevention, mitigation, and preparedness), during-incident (messages sent while the incident is taking place), and post-incident (messages related to the response, recovery, and reconstruction). We show that our detection method can effectively identify incident-related messages with high precision and recall, and that our incident-period classification method outperforms standard machine learning classification methods.
|
|
|
André Dittrich, & Christian Lucas. (2013). A step towards real-time analysis of major disaster events based on tweets. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 868–874). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: The most popular micro blogging platform Twitter has been the topic of a variety of research papers related to disaster and crisis management. As an essential first step and basis for a real-time methodology to exploit Twitter for event detection, localization and ultimately semantic content analysis, a functional model to describe the amount of tweets during a day has been developed. It was derived from a corpus of messages in an exemplary area of investigation. To satisfy the different daily behavior on particular days, two types of days are distinguished in this paper. Moreover, keyword-adjusted data is used to point out the potential of semantic tweet analysis in following steps. The consideration of spatial event descriptions in relevant tweets could significantly improve and accelerate the perception of a disaster. The results from the conducted tests demonstrate the capability of the functional model to detect events with significant social impact in Twitter data.
|
|
|
Amanda L. Hughes, & Leysia Palen. (2009). Twitter adoption and use in mass convergence and emergency events. In S. J. J. Landgren (Ed.), ISCRAM 2009 – 6th International Conference on Information Systems for Crisis Response and Management: Boundary Spanning Initiatives and New Perspectives. Gothenburg: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: This paper offers a descriptive account of Twitter (a micro-blogging service) across four high profile, mass convergence events-two emergency and two national security. We statistically examine how Twitter is being used surrounding these events, and compare and contrast how that behavior is different from more general Twitter use. Our findings suggest that Twitter messages sent during these types of events contain more displays of information broadcasting and brokerage, and we observe that general Twitter use seems to have evolved over time to offer more of an information-sharing purpose. We also provide preliminary evidence that Twitter users who join during and in apparent relation to a mass convergence or emergency event are more likely to become long-term adopters of the technology.
|
|
|
Jidi Zhao, & Linlin Wang. (2016). Research on Public Opinion Propagation in Micro-Blogging Based on Epidemic Models. In A. Tapia, P. Antunes, V.A. Bañuls, K. Moore, & J. Porto (Eds.), ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management. Rio de Janeiro, Brasil: Federal University of Rio de Janeiro.
Abstract: Micro-blogging has become an important communication channel for public opinion topics with its own characteristics such as openness, timeliness and interactive and so on. Studying the propagation rules for public opinion topics in micro-blogging is important to monitor and understand Micro-blogging public opinion. In this paper, we study the spreading process of public opinion in micro-blogging, identify key elements in the process and propose an Mb-RP (Micro-blogging Read-Post) propagation model based on the traditional SIR (Susceptible- Infective-Recovered) epidemic model. Through statistical analysis of a case on Sina Weibo, we assign values to parameters in the model and conduct simulations. Simulation results show that the model established in this paper can well fit real data. Further study of the model indicates that, compared with the attention cycle and the average amount of readings per post, the forwarding rate has the most influence on Micro-blogging information propagation.
|
|
|
Evan A. Sultanik, & Clayton Fink. (2012). Rapid geotagging and disambiguation of social media text via an indexed gazetteer. In Z.Franco J. R. L. Rothkrantz (Ed.), ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management. Vancouver, BC: Simon Fraser University.
Abstract: Microblogging services like Twitter afford opportunities for real time determination of situation awareness during crises as people report, via their statuses, information about events on the ground. An important component of the information included in a tweet are mentions of place names that may be sites of damage, injuries, or relief efforts. Methods for extracting these place names and determining the actual location being referenced are an essential part of the suite of tools required for automated extraction of situation awareness from tweets. Extracting and disambiguating place name mentions from text have been areas of extensive research. Twitter, however, presents challenges given the 140 character restriction on status and the informal, abbreviated language that are a norm in this communication channel. Named entity recognizers, which are dependent on labeled training data, may not be useful in this medium for extracting location mentions because the typical training domains for these taggers are absent the noise found in Twitter statuses. Additionally, the contextual information that is necessary for disambiguating place names is not always present. In this paper, we demonstrate a new technique, RapidGeo, for extracting and disambiguating place names from a location specific Twitter feed using an unsupervised technique for tagging location mentions and relying on the known geographic context of the feed for disambiguation. Our location tagging technique performs much better than an off-the-shelf named entity recognizer and we achieve reasonable precision in disambiguating extracted place names. We argue that such fast, high precision, unsupervised approaches are needed when important, actionable information is required from noisy data sources such as Twitter. © 2012 ISCRAM.
|
|
|
Jeannette N. Sutton. (2010). Twittering Tennessee: Distributed networks and collaboration following a technological disaster. In C. Zobel B. T. S. French (Ed.), ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings. Seattle, WA: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Informal communication channels are often the primary means by which time-sensitive hazard information first reaches members of the public. The capacity for informal communications has been recently transformed by the widespread adoption of social media technologies, such as the micro-blogging service Twitter, which allows individuals to interact with a broad audience over great distances. During a disaster or crisis event, this networked communication mechanism provides a means to communicate information and facilitate collaboration both locally and among distributed networks. This paper examines the use of Twitter following a technological disaster, showing how geographically dispersed individuals broadcast information about the impact of the disaster and its long-term effects, in contrast with the dearth of participation among public officials and industry representatives. Non-local users challenged authoritative accounts of the disaster and corrected misinformation. Conclusions are provided for policy makers and suggestions are offered for further research.
|
|
|
Jeannette N. Sutton, Emma S. Spiro, Britta Johnson, Sean M. Fitzhugh, Mathew Greczek, & Carter T. Butts. (2012). Connected communications: Network structures of official communications in a technological disaster. In Z.Franco J. R. L. Rothkrantz (Ed.), ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management. Vancouver, BC: Simon Fraser University.
Abstract: Informal online communication channels are being utilized for official communications in disaster contexts. Channels such as networked microblogging enable public officials to broadcast messages as well as engage in direct communication exchange with individuals. Here we investigate online information exchange behaviors of a set of state and federal organizations during the Deepwater Horizon 2010 oil spill disaster. Using data from the popular microblogging service Twitter, we analyze the roles individual organizations play in the dissemination of information to the general public online, and the conversational microstructure of official posts. We discuss characteristics and features of following networks, centrality, and conversational dynamics that may affect information exchange in disaster. This research provides insight into the use of networked communications during an event of heightened public concern, describes implications of conversational features, and suggests directions for future research. © 2012 ISCRAM.
|
|