$1.00 per RT #BostonMarathon #PrayForBoston: analyzing fake content on Twitter

This study found that 29% of the most viral content on Twitter during the Boston bombing crisis were rumors and fake content.AbstractOnline social media has emerged as one of the prominent channels for dissemination of information during real world events. Malicious content is posted online during events, which can result in damage, chaos and monetary losses in the real world. We analyzed one such media i.e. Twitter, for content generated during the event of Boston Marathon Blasts, that occurred on April, 15th, 2013. A lot of fake content and malicious profiles originated on Twitter network during this event. The aim of this work is to perform in-depth characterization of what factors influenced in malicious content and profiles becoming viral. Our results showed that 29% of the most viral content on Twitter, during the Boston crisis were rumors and fake content; while 51% was generic opinions and comments; and rest was true information. We found that large number of users with high social reputation and verified accounts were responsible for spreading the fake content. Next, we used regression prediction model, to verify that, overall impact of all users who propagate the fake content at a given time, can be used to estimate the growth of that content in future. Many malicious accounts were created on Twitter during the Boston event, that were later suspended by Twitter. We identified over six thousand such user profiles, we observed that the creation of such profiles surged considerably right after the blasts occurred. We identified closed community structure and star formation in the interaction network of these suspended profiles amongst themselves

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

Social Media Data in an Augmented Reality System for Situation Awareness Support in Emergency Control Rooms

During crisis situations, emergency operators require fast information access to achieve situation awareness and make the best possible decisions. Augmented reality could be used to visualize the wealth of user-generated content available on social media and enable context-adaptive functions for emergency operators. Although emergency operators agree that social media analytics will be important for their future work, it poses a challenge to filter and visualize large amounts of social media data. We conducted a goal-directed task analysis to identify the situation awareness requirements of emergency operators. By collecting tweets during two storms in Germany we evaluated the usefulness of Twitter data for achieving situation awareness and conducted interviews with emergency operators to derive filter strategies for social media data. We synthesized the results by discussing how the unique interface of augmented reality can be used to integrate social media data into emergency control rooms for situation awareness support.publishedVersio

A Neural Network-Based Situational Awareness Approach for Emergency Response

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Organised crime and social media; a system for detecting, corroborating and visualising weak signals of organised crime online

This paper describes an approach for detecting the presence or emergence of Organised Crime (OC) signals on Social Media. It shows how words and phrases, used by members of the public in Social Media posts, can be treated as weak signals of OC, enabling information to be classi�ed according to a taxonomy. Formal Concept Analysis (FCA) is used to group information sources, according to Crime-type and Location, thus providing a means of corroboration and creating OC Concepts that can be used to alert police analysts to the possible presence of OC. The analyst is able to `drill down' into an OC Concept of interest, discovering additional information that may be pertinent to the crime. The paper describes the implementation of this approach into a fully-functional prototype software system, incorporating a Social Media scanning system and a map-based user interface. The approach and system are illustrated using Human Tra�cking and Modern Slavery as an example. Real data is used to obtain results that show that weak signals of OC have been detected and corroborated, thus alerting to the possible presence of OC

A Process Evaluation of Intelligence Gathering Using Social Media for Emergency Management Organizations in California

When responding to an emergency, correct and timely information is often the difference between a successful response and a potential disaster. The information that emergency managers in California receive from the public often dictates how agencies respond to emergencies. The emergence of social media has presented several benefits to emergency managers regarding intelligence gathering during the emergency response process. Simultaneously, the emergence of social media has raised several concerns for the stakeholders involved. One major issue involves inaccurate information circulating on social media platforms during ongoing disasters. If emergency managers cannot discern incorrect information from correct information, disaster response may be less effective. Rumors and misinformation tend to circulate before, during, and after emergencies. Although incorrect information circulating on social media cannot be stopped in totality, emergency managers can use cutting-edge technology and strategies to discern and counteract false information. New technologies and intelligence gathering tools can be used as a source of intelligence to relay lifesaving information to the public. Past negative examples of inaccurate information on social media influencing stakeholder decision-making raise the focus of this research: How can emergency management agencies in California leverage the flow of valid information on social media during crisis conditions