Enhancing Disaster Management Through Social Media Analytics To Develop Situation Awareness: What Can Be Learned From Twitter Messages About Hurricane Sandy?

Twitter became an important channel to contribute and consume all kinds of information, especially in times of disasters, when people feel the need for fast, real-time flows of information. Given the wealth of information Twitter provides, that information can be used by practitioners and researchers alike to study what people affected by a disaster talk about, e.g., to develop a situation awareness and to coordinate disaster management accordingly. In our research, we analyze 11 million tweets that deal with hurricane Sandy, one of the strongest hurricanes that ever hit the US east coast in 2012. First, we extract the tweets by narrowing down the hurricane affected path along the US east coast, based on geo-spatial information. Further, drawing on the situation awareness literature and previous coding schemes, we analyze the nature and characteristics of the tweets. Our research reveals that there are significantly more tweets from original sources than from secondary sources and that individuals tend to share valuable personal experiences and observations at the time of disasters. In analyzing those individual level perceptions, we illustrate how one can generate situation awareness at the collective level. This situation awareness will enhance the decision-making of disaster management agencies at the time of uncertain and volatile situations

How Emotions Unfold in Online Discussions After a Terror Attack

In the wake of a terror attack, social media is used for sharing thoughts and emotions, accessing and distributing information, and memorializing victims. Emotions are a big part of this, but there is a gap in our understanding on how those emotions evolve and what kinds of social media uses they are related to. Better understanding of the emotional and topical developments of online discussions can serve not only to fill the aforementioned gap, but also assist in developing better collective coping strategies for recovering from terror attacks. We examine what types of conversations unfolded online after the Boston Marathon Bombing and what kinds of emotions were associated with them, accounting for regional differences, and present a process model covering the general trends of such conversations. Although the phases apply to reactions to terror attacks on a general level, there are proximity-based differences to the location of the terror attack

Fear and loathing in Boston: The roles of different emotions in information sharing on social media following a terror attack

Emotions are essential to how we communicate, and online discussions are no exception. As most of the analysis on emotion so far has looked at polarity rather than specific emotions, we do not yet have a full understanding of how different emotions spark different behaviours. This study examines how five different emotions are associated with information sharing in the context of a terror attack both on a large scale and when including geolocation information in the analysis. Contrary to what previous findings suggest, increased fear and contempt levels have a negative relation with increased levels of retweeting. Positive emotion in tweets meant a decrease in retweet rates in the geolocation specific data, but an increase when all tweets were considered

The role of social media for collective behaviour development in response to natural disasters

With the emergence of social media, user-generated content from people affected by disasters has gained significant importance. Thus far, research has focused on identifying categories and taxonomies of the types of information being shared among users during times of disasters. However, there is a lack of theorizing with the dynamics of and relationships between the identified concepts. In our current research, we applied probabilistic topic modelling approach to identify topics from Chennai disaster Twitter data. We manually interpreted and further clustered the topics into generic categories and themes, and traced their development over the days of the disaster. Finally, we build a process model to explore an emerging phenomenon on social media during a disaster. We argue that the conditions/activities such as collective awareness, collective concern, collective empathy and support are necessary conditions for people to feel, respond, and act as forms of collective behaviour

Development of a national-scale real-time Twitter data mining pipeline for social geodata on the potential impacts of flooding on communities

International audienceSocial media, particularly Twitter, is increasingly used to improve resilience during extreme weather events/emergency management situations, including floods: by communicating potential risks and their impacts, and informing agencies and responders. In this paper, we developed a prototype national-scale Twitter data mining pipeline for improved stakeholder situational awareness during flooding events across Great Britain, by retrieving relevant social geodata, grounded in environmental data sources (flood warnings and river levels). With potential users we identified and addressed three research questions to develop this application, whose components constitute a modular architecture for real-time dashboards. First, polling national flood warning and river level Web data sources to obtain at-risk locations. Secondly, real-time retrieval of geotagged tweets, proximate to at-risk areas. Thirdly, filtering flood-relevant tweets with natural language processing and machine learning libraries, using word embeddings of tweets. We demonstrated the national-scale social geodata pipeline using over 420,000 georeferenced tweets obtained between 20-29th June 2016. Highlights • Prototype real-time social geodata pipeline for flood events and demonstration dataset • National-scale flood warnings/river levels set 'at-risk areas' in Twitter API queries • Monitoring multiple locations (without keywords) retrieved current, geotagged tweets • Novel application of word embeddings in flooding context identified relevant tweets • Pipeline extracts tweets to visualise using open-source libraries (SciKit Learn/Gensim) Keywords Flood management; Twitter; volunteered geographic information; natural language processing; word embeddings; social geodata. Hardware required: Intel i3 or mid-performance PC with multicore processor and SSD main drive, 8Gb memory recommended. Software required: Python and library dependencies specified in Appendix A1.2.1, (viii) environment.yml Software availability: All source code can be found at GitHub public repositorie

Social media and knowledge integration based emergency response performance model

Emergency Response (ER) during the flood is increasingly being characterized as a complex phase in disaster management as it involves multi-organizational settings. This scenario causes miscommunication, lack of coordination and difficulty in making life-saving decisions, which decreases organisational performance. Accordingly, Knowledge Integration (KI) can reduce and resolve problems of coordination and communications which lead to decisions being made at a proper time, thereby increasing the task of Non- Government Organisations (NGOs)’ capabilities to achieve better performance. Moreover, use of Social Media (SM) provides many advantages that may assist in eliminating KI’s challenges and enhancing its dissemination at low cost, particularly for NGOs that work in disparate places. Despite this, current research into the improvement of task performance using KI through SM in the emergency response context is, unfortunately, limited. Most of the studies are not empirical and there is a lack of theoretical foundation for improving task performance using KI, in addition to using SM to facilitate KI in the flood disaster ER. Hence, it is important to address these issues. The main objective of this study is to identify the factors that influence the Emergency Response Task Performance (ERTP). In this research, the factors which affect the performance of ER tasks were elicited through a review of the literature to identify the essential factors influential NGOs’ emergency response. Then, this study developed an ERTP model by combining Knowledge-Based Theory (KBT) of the firm and the Task-Technology Fit (TTF) theory, used to utilise technology. This study applied a quantitative approach to examine these factors. Based on purposive sampling, questionnaires were distributed to over 700 staff and volunteers working for 12 NGOs in Sudan. Smart PLS 2.0 M3 and IBM SPSS Statistics version 24 were used to analyse the data. The results revealed that KI is a significant factor related to ERTP. In addition, it was found that the SM usage factor was significantly related to KI. Furthermore, this study discovered significant differences among the various experiences of volunteers and staff when it comes to utilising SM for knowledge integration in the context of ER response. The results of the study contribute to the body of knowledge by providing a model for ER managers, team members in NGOs and decision-makers to use it as a guideline for successfully assessing and validating ERTP. Additionally, it sets guidelines that may be useful for NGOs in the effective use of social media as a platform for integrating knowledge. Finally, this study provides recommendations to flood decision-makers who are considering enhancing the performance of the tasks within their organisations

Assessing the social impacts of extreme weather events using social media

The frequency and severity of extreme weather events such as flooding, hurricanes/storms and heatwaves are increasing as a result of climate change. There is a need for information to better understand when, where and how these events are impacting people. However, there are currently limited sources of impact information beyond traditional meteorological observations. Social sensing, which is the use of unsolicited social media data to better understand real world events, is one method that may provide such information. Social sensing has successfully been used to detect earthquakes, floods, hurricanes, wildfires, heatwaves and other weather hazards. Here social sensing methods are adapted to explore potential for collecting impact information for meteorologists and decision makers concerned with extreme weather events. After a review of the literature, three experimental studies are presented. Social sensing is shown to be effective for detection of impacts of named storms in the UK and Ireland. Topics of discussion and sentiment are explored in the period before, during and after a storm event. Social sensing is also shown able to detect high-impact rainfall events worldwide, validating results against a manually curated database. Additional events which were not known to this database were found by social sensing. Finally, social sensing was applied to heatwaves in three European cities. Building on previous work on heatwaves in the UK, USA and Australia, the methods were extended to include impact phrases alongside hazard-related phrases, in three different languages (English, Dutch and Greek). Overall, social sensing is found to be a good source of impact information for organisations that need to better understand the impacts of extreme weather. The research described in this project has been commercialised for operational use by meteorological agencies in the UK, including the Met Office, Environment Agency and Natural Resources Wales.Engineering and Physical Sciences Research Council (EPSRC