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Adam Flizikowski, M. P., Anna Stachowicz, Tomasz Olejniczak, & Rafael Renk. (2015). Text Analysis Tool TWeet lOcator ? TAT2. In L. Palen, M. Buscher, T. Comes, & A. Hughes (Eds.), ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management. Kristiansand, Norway: University of Agder (UiA).
Abstract: Information about location and geographical coordinates in particular, may be very important during a crisis event, especially for search and rescue operations ? but currently geo-tagged tweets are extremely rare. Improved capabilities of capturing additional location from Twitter (up to 4 times improvement) are crucial for response efforts given a vast amount of messages exchanged during a crisis event. That is why authors have designed a tool (Text Analysis TWeet lOcator ? TAT2) that relies on existing open source text analysis tools with additional services to provide additional hints about people location. Validation process, complementing experimentation and test results, included involvement of end-users (i.e. Public Protection and Disaster Relief services and citizens during a realistic crisis exercise showcase. In addition, the integration of TAT2 with external tools has also been validated.
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Adam Flizikowski, Witold Holubowicz, Anna Stachowicz, Laura Hokkanen, Taina Kurki, Nina Päivinen, et al. (2014). Social media in crisis management – The iSAR+ project survey. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 707–711). University Park, PA: The Pennsylvania State University.
Abstract: Social media together with still growing social media communities has become a powerful and promising solution in crisis and emergency management. Previous crisis events have proved that social media and mobile technologies used by citizens (widely) and public services (to some extent) have contributed to the post-crisis relief efforts. The iSAR+ EU FP7 project aims at providing solutions empowering citizens and PPDR (Public Protection and Disaster Relief) organizations in online and mobile communications for the purpose of crisis management especially in search and rescue operations. This paper presents the results of survey aiming at identification of preliminary end-user requirements in the close interworking with end-users across Europe.
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Ahmed Alnuhayt, Suvodeep Mazumdar, Vitaveska Lanfranchi, & Frank Hopfgartner. (2022). Understanding Reactions to Misinformation – A Covid-19 Perspective. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 687–700). Tarbes, France.
Abstract: The increasing use of social media as an information source brings further challenges – social media platforms can be an excellent medium for disseminating public awareness and critical information, that can be shared across large populations. However, misinformation in social media can have immense implications on public health, risking the effectiveness of health interventions as well as lives. This has been particularly true in the case of COVID-19 pandemic, with a range of misinformation, conspiracy theories and propaganda being spread across social channels. In our study, through a questionnaire survey, we set out to understand how members of the public interact with different sources when looking for information on COVID-19. We explored how participants react when they encounter information they believe to be misinformation. Through a set of three behaviour tasks, synthetic misinformation posts were provided to the participants who chose how they would react to them. In this work in progress study, we present initial findings and insights into our analysis of the data collected. We highlight what are the most common reactions to misinformation and also how these reactions are different based on the type of misinformation.
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Ahmed Nagy, & Jeannie Stamberger. (2012). Crowd sentiment detection during disasters and crises. 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: Microblogs are an opportunity for scavenging critical information such as sentiments. This information can be used to detect rapidly the sentiment of the crowd towards crises or disasters. It can be used as an effective tool to inform humanitarian efforts, and improve the ways in which informative messages are crafted for the crowd regarding an event. Unique characteristics of microblogs (lack of context, use of jargon etc) in Tweets expressed by a message-sharing social network during a disaster response require special handling to identify sentiment. We present a systematic evaluation of approaches to accurately and precisely identify sentiment in these Tweets. This paper describes sentiment detection expressed in 3698 Tweets, collected during the September 2010, San Bruno, California gas explosion and resulting fires. The data collected was manually coded to benchmark our techniques. We start by using a library of words with annotated sentiment, SentiWordNet 3.0, to detect the basic sentiment of each Tweet. We complemented that technique by adding a comprehensive list of emoticons, a sentiment based dictionary and a list of out-of-vocabulary words that are popular in brief, online text communications such as lol, wow, etc. Our technique performed 27% better than Bayesian Networks alone, and the combination of Bayesian networks with annotated lists provided marginal improvements in sentiment detection than various combinations of lists. © 2012 ISCRAM.
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Aibek Musaev, De Wang, & Calton Pu. (2014). LITMUS: Landslide detection by integrating multiple sources. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 677–686). University Park, PA: The Pennsylvania State University.
Abstract: Disasters often lead to other kinds of disasters, forming multi-hazards such as landslides, which may be caused by earthquakes, rainfalls, water erosion, among other reasons. Effective detection and management of multihazards cannot rely only on one information source. In this paper, we evaluate a landslide detection system LITMUS, which combines multiple physical sensors and social media to handle the inherent varied origins and composition of multi-hazards. LITMUS integrates near real-time data from USGS seismic network, NASA TRMM rainfall network, Twitter, YouTube, and Instagram. The landslide detection process consists of several stages of social media filtering and integration with physical sensor data, with a final ranking of relevance by integrated signal strength. Applying LITMUS to data collected in October 2013, we analyzed and filtered 34.5k tweets, 2.5k video descriptions and 1.6k image captions containing landslide keywords followed by integration with physical sources based on a Bayesian model strategy. It resulted in detection of all 11 landslides reported by USGS and 31 more landslides unreported by USGS. An illustrative example is provided to demonstrate how LITMUS' functionality can be used to determine landslides related to the recent Typhoon Haiyan.
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Aibek Musaev, Kimberly Stowers, & Jonghun Kam. (2018). Harnessing Data to Create an Effective Drought Management System. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 544–552). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Drought is a complex climate phenomenon with slow emergence and quick vanish, which makes it hard for stakeholders to respond to drought timely. To reduce the vulnerability of our society to future drought, a better understanding of how society responds to drought is critical. Here, we propose a pilot study about social response to a recent California drought through social media. In this study, we identify the most important users using an extension of PageRank algorithm. We investigate the key drivers of the public activity in February, 2014 during the California drought. We also create a word cloud visualization from the most retweeted tweets. Lastly, we specify the information sources from those tweets. The findings of this study inform us that big data can help us to improve the current drought response plans through fundamental understanding of social response to drought, which is applicable to other natural hazards.
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Alan Aipe, Asif Ekbal, Mukuntha NS, & Sadao Kurohashi. (2018). Linguistic Feature Assisted Deep Learning Approach towards Multi-label Classification of Crisis Related Tweets. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 705–717). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Micro-blogging site like Twitter, over the last decade, has evolved into a proactive communication channel during mass convergence and emergency events, especially in crisis stricken scenarios. Extracting multiple levels of information associated with the overwhelming amount of social media data generated during such situations remains a great challenge to disaster-affected communities and professional emergency responders. These valuable data, segregated into different informative categories, can be leveraged by the government agencies, humanitarian communities as well as citizens to bring about faster response in areas of necessity. In this paper, we address the above scenario by developing a deep Convolutional Neural Network (CNN) for multi-label classification of crisis related tweets.We augment deep CNN by several linguistic features extracted from Tweet, and investigate their usage in classification. Evaluation on a benchmark dataset show that our proposed approach attains the state-of-the-art performance.
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Amanda L. Hughes. (2014). Participatory design for the social media needs of emergency public information officers. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 727–736). University Park, PA: The Pennsylvania State University.
Abstract: This paper describes the design, execution, and results of a participatory design workshop with emergency public information officers (PIOs). During the workshop, PIOs and researchers explored ideas and designs for supporting the social media needs of PIO work. Results indicate that PIO perceptions of social media have changed as they have learned to incorporate activities of the public into their work, yet they still struggle with issues of trust and liability. Based on workshop design activities, the paper offers a set of design recommendations for supporting the social media needs of PIO work practice such as the ability to monitor, document, and report social media activity.
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Amanda Langer, Marc-André Kaufhold, Elena Maria Runft, Christian Reuter, Margarita Grinko, & Volkmar Pipek. (2019). Counter Narratives in Social Media: An Empirical Study on Combat and Prevention of Terrorism. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: With the increase of terrorist attacks and spreading extremism worldwide, countermeasures advance as well. Often
social media is used for recruitment and radicalization of susceptible target groups. Counter narratives are trying
to disclose the illusion created by radical and extremist groups through a purposive and educational counter
statement, and to initiate a rethinking in the affected individuals via thought-provoking impulses and advice. This
exploratory study investigates counter narrative campaigns with regard to their fight and prevention against
terrorism in social media. Posts with strong emotions and a personal reference to affected individuals achieved
the highest impact and most reactions from the target group. Furthermore, our results illustrate that the impact of
a counter narrative campaign cannot be measured solely according to the reaction rate to their postings and that
further analysis steps are therefore necessary for the final evaluation of the campaigns.
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Amro Al-Akkad, Christian Raffelsberger, Alexander Boden, Leonardo Ramirez, & Zimmermann, A. (2014). Tweeting 'when online is off'? Opportunistically creating mobile ad-hoc networks in response to disrupted infrastructure. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 662–671). University Park, PA: The Pennsylvania State University.
Abstract: In this paper, we present a system that enables people to post and receive tweets despite disruptions of existing network infrastructure. Our system opportunistically deploys mobile ad hoc networks (MANETs) based on Wi-Fi in which people can communicate with each other in a peer-to-peer fashion. A MANET per se constitutes an isolated island, but as people carry devices around that can join other MANETs, eventually people can transport previously collected data to the online world. Compared to other systems that aim to enable communication in crisis, our system differs in two ways: it does not rely on existing network infrastructure, and it exploits established protocols and standards allowing it to run on off-the-shelf, commercially available smartphones. We evaluated our prototype with a group of students and practitioners. Overall, we received positive feedback on the potential of our technology, but also were pointed to limitations requiring future work.
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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.
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André Sabino, & Armanda Rodrigues. (2011). Understanding the role of cooperation in emergency plan construction. 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: In this paper we describe a proposal for information organization for computer supported cooperative work, while working with spatial information. It is focused on emergency response plan construction, and the requirements extracted from that task. At the centre of our proposal is the analysis of the structure of the cooperative workspace. We argue that the internal information representation should follow a spatial approach, tying the structure used to manage users with the structure used to manage information, suggesting the use of different spaces to represent the information. The gain we expect from this approach is the improved capacity to extract information on how people are cooperating and their relationship with the information they are working with. The ideas are introduced while focusing on real life emergency planning activities, where we discuss the current shortcomings of the cooperation strategies in use and propose a solution.
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Andrea H. Tapia, Kathleen A. Moore, & Nichloas J. Johnson. (2013). Beyond the trustworthy tweet: A deeper understanding of microblogged data use by disaster response and humanitarian relief organizations. 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. 770–779). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: In this paper we present findings from interviews conducted with representatives from large international disaster response organizations concerning their use of social media data in crisis response. We present findings in which the barriers to use by responding organizations have gone beyond simple discussions of trustworthiness to that of more operational issues rather than mere data quality. We argue that the landscape of the use of microblogged data in crisis response is varied, with pockets of use and acceptance among organizations. We found that microblogged data is useful to responders in situations where information is limited, such as at the beginning of an emergency response effort, and when the risks of ignoring an accurate response outweigh the risks of acting on an incorrect one. In some situations, such as search and rescue operations, microblogged data may never meet the standards of quality required. In others, such as resource and supply management, microblogging data could be useful as long as it is appropriately verified and classified.
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Andrea Kavanaugh, Steven D. Sheetz, Riham Hassan, Seungwon Yang, Hicham G. Elmongui, Edward A. Fox, et al. (2012). Between a rock and a cell phone: Communication and information technology use during the 2011 Egyptian uprising. 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: Many observers heralded the use of social media during recent political uprisings in the Middle East even dubbing Iran's post election protests a “Twitter Revolution”. We seek to put into perspective the use of social media in Egypt during the mass political demonstrations in 2011. We draw on innovation diffusion theory to argue that these media could have had an impact beyond their low adoption rates due to other factors related to demographics and social networks. We supplement our social media data analysis with survey data we collected in June 2011 from an opportunity sample of Egyptian youth. We conclude that in addition to the contextual factors noted above, the individuals within Egypt who used Twitter during the uprising have the characteristics of opinion leaders. These findings contribute to knowledge regarding the role of opinion leaders and social media, especially Twitter, during violent political demonstrations. © 2012 ISCRAM.
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Andrea Zielinski, Stuart E. Middleton, Laurissa N. Tokarchuk, & Xinyue Wang. (2013). Social media text mining and network analysis for decision support in natural crisis management. 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. 840–845). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: A core issue in crisis management is to extract from the mass of incoming information what is important for situational awareness during mass emergencies. Based on a case study we develop a prototypical application, TweetComp1, which is integrated into the decision-support component of a Tsunami early warning system and demonstrates the applicability of our approach. This paper describes four novel approaches using focused twitter crawling, trustworthiness analysis, geo-parsing, and multilingual tweet classification in the context of how they could be used for monitoring crises. The validity of our state-of-the art text mining and network analysis technologies will be verified in different experiments based on a human annotated gold standard corpus.
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Andrés Moreno, Philip Garrison, & Karthik Bhat. (2017). WhatsApp for Monitoring and Response during Critical Events: Aggie in the Ghana 2016 Election. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 645–655). Albi, France: Iscram.
Abstract: Mobile Instant Messaging platforms like WhatsApp are becoming increasingly popular. They have expanded access to digital text, audio, picture, and video messaging. Integrating them into existing crisis monitoring and response platforms and workflows can help reach a wider population. This paper describes a first attempt to integrate WhatsApp into Aggie, a social media aggregating and monitoring platform. We report on the deployment of this integration during Ghana's 2016 election, along with Twitter, Facebook, and RSS. The WhatsApp messages collected by Aggie during the election improved the eectiveness of the monitoring eorts. Thanks to these messages, more incidents were found and escalated to the Electoral Commission and security forces. From interviews with people involved in monitoring and response, we found that the WhatsApp integration helped their coordination and monitoring activities.
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Anna Kruspe. (2020). Detecting Novelty in Social Media Messages During Emerging Crisis Events. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 860–871). Blacksburg, VA (USA): Virginia Tech.
Abstract: Social media can be a highly valuable source of information during disasters. A crisis' development over time is of particular interest here, as social media messages can convey unfolding events in near-real time. Previous approaches for the automatic detection of information in such messages have focused on a static analysis, not taking temporal changes and already-known information into account. In this paper, we present a novel method for detecting new topics in incoming Twitter messages (tweets) conditional upon previously found related tweets. We do this by first extracting latent representations of each tweet using pre-trained sentence embedding models. Then, Infinite Mixture modeling is used to dynamically cluster these embeddings anew with each incoming tweet. Once a cluster reaches a minimum number of members, it is considered to be a new topic. We validate our approach on the TREC Incident Streams 2019A data set.
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Anna Kruspe, Jens Kersten, & Friederike Klan. (2019). Detecting event-related tweets by example using few-shot models. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: Social media sources can be helpful in crisis situations, but discovering relevant messages is not trivial. Methods
have so far focused on universal detection models for all kinds of crises or for certain crisis types (e.g. floods).
Event-specific models could implement a more focused search area, but collecting data and training new models for
a crisis that is already in progress is costly and may take too much time for a prompt response. As a compromise,
manually collecting a small amount of example messages is feasible. Few-shot models can generalize to unseen
classes with such a small handful of examples, and do not need be trained anew for each event. We show how
these models can be used to detect crisis-relevant tweets during new events with just 10 to 100 examples and
counterexamples. We also propose a new type of few-shot model that does not require counterexamples.
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Annemijn F. Van Gorp. (2014). Integration of volunteer and technical communities into the humanitarian aid sector: Barriers to collaboration. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 622–631). University Park, PA: The Pennsylvania State University.
Abstract: Volunteer and Technical Communities (V&TCs) with expertise in the collection, analysis and presentation of data and the development of supporting technologies, have potential to inform humanitarian aid organizations and help increase the efficiency of their operations. This study analyzes the role of V&TCs during recent response efforts and identifies a number of challenges of organizational nature that need to be overcome in order for aid organizations to harness the potential of V&TCs. The study finds that V&TCs can broadly be categorized into software platform development communities, mapping collaborations, expert networks and data aggregators. Evidence of collaboration with aid organizations however remains limited, suggesting a number of barriers need to be overcome, including (1) limited resources; (2) the management of volunteers; (3) different levels of engagement; (4) level of commitment by V&TCs; (5) different ways of working; and (6) aid organizations' limited knowledge about V&TCs' expertise.
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Anthony C. Robinson, Alexander Savelyev, Scott Pezanowski, & Alan M. MacEachren. (2013). Understanding the utility of geospatial information in social media. 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. 918–922). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Crisis situations generate tens of millions of social media reports, many of which contain references to geographic features and locations. Contemporary systems are now capable of mining and visualizing these location references in social media reports, but we have yet to develop a deep understanding of what end-users will expect to do with this information when attempting to achieve situational awareness. To explore this problem, we have conducted a utility and usability analysis of SensePlace2, a geovisual analytics tool designed to explore geospatial information found in Tweets. Eight users completed a task analysis and survey study using SensePlace2. Our findings reveal user expectations and key paths for solving usability and utility issues to inform the design of future visual analytics systems that incorporate geographic information from social media.
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Antone Evans Jr., Yingyuan Yang, & Sunshin Lee. (2021). Towards Predicting COVID-19 Trends: Feature Engineering on Social Media Responses. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 792–807). Blacksburg, VA (USA): Virginia Tech.
Abstract: During the course of this pandemic, the use of social media and virtual networks has been at an all-time high. Individuals have used social media to express their thoughts on matters related to this pandemic. It is difficult to predict current trends based on historic case data because trends are more connected to social activities which can lead to the spread of coronavirus. So, it's important for us to derive meaningful information from social media as it is widely used. Therefore, we grouped tweets by common keywords, found correlations between keywords and daily COVID-19 statistics and built predictive modeling. The features correlation analysis was very effective, so trends were predicted very well. A RMSE score of 0.0425504, MAE of 0.03295105 and RSQ of 0.5237014 in relation to daily deaths. In addition, we found a RMSE score of 0.07346836, MAE of 0.0491152 and RSQ 0.374529 in relation to daily cases.
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Antonin Segault, Federico Tajariol, & Ioan Roxin. (2015). #geiger : Radiation Monitoring Twitter Bots for Nuclear Post-Accident Situations. In L. Palen, M. Buscher, T. Comes, & A. Hughes (Eds.), ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management. Kristiansand, Norway: University of Agder (UiA).
Abstract: In the last decade, people have increasingly relied on social media platforms such as Twitter to share information on the response to a natural or a man-made disaster. This paper focuses on the aftermath of the Fukushima Daiichi nuclear disaster. Since the disaster, victims and volunteers have been sharing relevant information about radiation measurements by means of social media. The aim of this research is to explore the diffusion of information produced and shared by Twitter bots, to understand the degree of popularity of these sources and to check if these bots deliver original radiation measurements.
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Antonin Segault, Federico Tajariol, Yang Ishigaki, & Ioan Roxin. (2016). #geiger 2: Developing Guidelines for Radiation Measurements Sharing on Social Media. 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: Radiation measurements are key information in post-nuclear accident situations. Automated Twitter accounts have been used to share the readings, but often in an incomplete way from the perspective of data sharing and risk communication between citizen and radiation experts. In this paper, we investigate the requirements for radiation measurements completeness, by analyzing the perceived usefulness of several metadata items that may go along the measurement itself. We carried out a benchmark of existing uses, and conducted a survey with both experts and lay citizens. We thus produced a set of guidelines regarding the metadata that should be used, and the way to publish it.
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Apoorva Chauhan, & Amanda Hughes. (2021). COVID-19 Named Resources on Facebook, Twitter, and Reddit. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 679–690). Blacksburg, VA (USA): Virginia Tech.
Abstract: Crisis Named Resources (CNRs) are social media accounts and pages named after a crisis event. They are created soon after an event occurs. CNRs share a lot of information around an event and are followed by many. In this study, we identify CNRs created around COVID-19 on Facebook, Twitter, and Reddit. We analyze when these resources were created, why they were created, how they were received by members of the public, and who created them. We conclude by comparing CNRs created around COVID-19 with past crisis events and discuss how CNR owners attempt to manage content and combat misinformation.
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Apoorva Chauhan, & Amanda Lee Hughes. (2018). Social Media Resources Named after a Crisis Event. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 573–583). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Crisis Named Resources (CNRs) are the social media accounts and pages named after a crisis event. CNRs typically appear spontaneously after an event as places for information exchange. They are easy to find when searching for information about the event. Yet in most cases, it is unclear who manages these resources. Thus, it is important to understand what kinds of information they provide and what role they play in a response. This paper describes a study of Facebook and Twitter CNRs around the 2016 Fort McMurray wildfire. We report on CNR lifecycles, and their relevance to the event. Based on the information provided by these resources, we categorize them into 8 categories: donations, fundraisers, prayers, reactions, reports, needs and offers, stories, and unrelated. We also report on the most popular CNR on both Facebook and Twitter. We conclude by discussing the role of CNRs and the need for future investigation.
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