Analysis and Early Detection of Rumors in a Post Disaster Scenario

Mondal, Tamal; Pramanik, Prithviraj; Bhattacharya, Indrajit; Boral, Naiwrita; Ghosh, Saptarshi

doi:10.1007/s10796-018-9837-8

Cited by 52 publications

(31 citation statements)

References 27 publications

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“…There has been a lot of recent work in utilizing Online Social Media (OSM) to facilitate post-disaster relief operations -see [1,8,9] for some recent surveys on this topic. For instance, there have been works on classifying situational and non-situational information [3,10], location inferencing from social media posts during disasters [11,12,13], early detection of rumours from social media posts [14], emergency information diffusion on social media during crises [15], event detection [16], extraction of event-specific informative tweets during disaster [17] and so on. Recent works have exemplified social media's ability to disseminate disaster information between institutional and non-institutional volunteers [18] and their use to reinforce the role of different stakeholders [19].…”

Section: Related Workmentioning

confidence: 99%

Utilizing microblogs for assisting post-disaster relief operations via matching resource needs and availabilities

Dutt

Basu

Ghosh

et al. 2019

Information Processing & Management

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During a disaster event, two types of information that are especially useful for coordinating relief operations are needs and availabilities of resources (e.g., food, water, medicines) in the affected region. Information posted on microblogging sites is increasingly being used for assisting post-disaster relief operations. In this context, two practical challenges are (i) to identify tweets that inform about resource needs and availabilities (termed as need-tweets and availability-tweets respectively), and (ii) to automatically match needs with appropriate availabilities. While several works have addressed the first problem, there has been little work on automatically matching needs with availabilities. The few prior works that attempted matching only considered the resources, and no attempt has been made to understand other aspects of needs/availabilities that are essential for matching in practice. In this work, we develop a methodology for understanding five important aspects of need-tweets and availability-tweets, including what resource and what quantity is needed/available, the geographical location of the need/availability, and who needs / is providing the resource. Understanding these aspects helps us to address the need-availability matching problem considering not only the resources, but also other factors such as the geographical proximity between the need and the availability. To the best of our knowledge, this study is the first attempt to develop methods for understanding the semantics of need-tweets and availability-tweets. We also develop a novel methodology for matching needtweets with availability-tweets, considering both resource similarity and geographical proximity. Experiments on two datasets corresponding to two disaster events, demonstrate that our proposed methods perform substantially better matching than those in prior works. Additionally, our proposed methodologies are reusable across different types of disaster events.

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Section: Related Workmentioning

confidence: 99%

Utilizing microblogs for assisting post-disaster relief operations via matching resource needs and availabilities

Dutt

Basu

Ghosh

et al. 2019

Information Processing & Management

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“…During times of disaster, there is widespread panic and tension amongst the people. Not only the victims, but also the volunteers remain in a state of stress, due to which misinformation and rumours are able to seep into the network (Mondal et al 2018). It is a challenge to detect such misinformation and rumours, since at such times, even genuinely renowned people can also unwittingly post rumours.…”

Section: Rc5: Guarding Against Misinformation and Other Types Of Harmmentioning

confidence: 99%

“…They also present an online algorithm that learns and automatically adjusts weights of the initial word model. Mondal et al (2018) attempt rumordetection on the tweets at early stage in the aftermath of a disaster situation. To this end, they present a probabilistic model on the important features of rumor propagation using which they obtain better rumor detection performance on tweets collected during a disaster event over relevant baselines.…”

Section: Papers In the Special Issuementioning

confidence: 99%

Exploitation of Social Media for Emergency Relief and Preparedness: Recent Research and Trends

Ghosh

Ganguly

et al. 2018

Inf Syst Front

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Online Social Media, such as Twitter, Facebook and WhatsApp, are important sources of real-time information related to emergency events, including both natural calamities, man-made disasters, epidemics, and so on. There has been lot of recent work on designing information systems that would be useful for aiding post-disaster relief operations, as well as for pre-disaster preparedness. A special issue on BExploitation of Social Media for Emergency Relief and Preparedness^was conducted for the journal Information Systems Frontiers. The objective of this special issue was to present a platform for dissemination of the empirical results of various technologies for extracting vital and actionable information from social media content in disaster situations. The papers included in this issue are expected to be the stepping stones for future explorations and technical innovations towards technologies meant for utilizing various online and offline information sources for enhancing pre-disaster preparedness and post-disaster relief operations.

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“…Majumdar et al [4] proposed a method for rumor detection in financial scope using a high volume of data. Mondal et al [5] focused on the fast and timely detection of rumor, which is so important in disaster and crisis.…”

Section: Information Verificationmentioning

confidence: 99%

Information verification improvement by textual entailment methods

2019

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Information verification is significant because the rate of information generation is high and growing every day, generally in social networks. This also causes social networks to be invoked as a news agency for most of the people. Accordingly, information verification in social networks becomes more significant. Therefore, in this paper, a method for information verification on Twitter is proposed. The proposed method employs textual entailment methods for enhancement of verification methods on Twitter. Aggregating the results of entailment methods in addition to the state-of-the-art methods can enhance the outcomes of tweet verification. In addition, as writing style of tweets is not perfect and formal enough for textual entailment, we used the language model to supplement tweets with more formal and proper texts for textual entailment. Although singly utilizing entailment methods for information verification may result in acceptable results, it is not possible to provide relevant and valid sources for all of the tweets, especially in early times by posting tweets. Therefore, we utilized other sources as a user conversational tree (UCT) besides utilizing entailment methods for tweet information verification. The analysis of UCT is based on the pattern extraction from the UCT. Experimental results indicate that using entailment methods enhances tweet verification.

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Analysis and Early Detection of Rumors in a Post Disaster Scenario

Cited by 52 publications

References 27 publications

Utilizing microblogs for assisting post-disaster relief operations via matching resource needs and availabilities

Utilizing microblogs for assisting post-disaster relief operations via matching resource needs and availabilities

Exploitation of Social Media for Emergency Relief and Preparedness: Recent Research and Trends

Information verification improvement by textual entailment methods

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