Processing Social Media Messages in Mass Emergency

By Muhammad Imran, Carlos Castillo, Fernando Diaz, Sarah Vieweg. ACM Computing Surveys, 2014

877 citations recorded by the source corpus.

Social media platforms provide active communication channels during mass convergence and emergency events such as disasters caused by natural hazards. As a result, first responders, decision makers, and the public can use this information to gain insight into the situation as it unfolds. In particular, many social media messages communicated during emergencies convey timely, actionable information. Processing social media messages to obtain such information, however, involves solving multiple challenges including: parsing brief and informal messages, handling information overload, and prioritizing different types of information found in messages. These challenges can be mapped to classical information processing operations such as filtering, classifying, ranking, aggregating, extracting, and summarizing. We survey the state of the art regarding computational methods to process social media messages and highlight both their contributions and shortcomings. In addition, we examine their particularities, and methodically examine a series of key subproblems ranging from the detection of events to the creation of actionable and useful summaries. Research thus far has, to a large extent, produced methods to extract situational awareness information from social media. In this survey, we cover these various approaches, and highlight their benefits and shortcomings. We conclude with research challenges that go beyond situational awareness, and begin to look at supporting decision making and coordinating emergency-response actions.

Subfields: Artificial Intelligence, Communication, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/cd853833424c07ac9a4726f59171803dc217bc90

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Citations in the corpus

Twitter as a Lifeline: Human-annotated Twitter Corpora for NLP of Crisis-related Messages (2016, background): “Many Natural-Language-Processing (NLP) techniques such as automatic summarization, information classiﬁcation, named-entity recognition, information extraction can be used to process such social media messages (Bontcheva et al., 2013; Imran et al., 2015).”
Tackling Climate Change with Machine Learning (2019, background): “ies – which can inform relief efforts. ML can help properly surface these insights, compressing large volumes of social media data into the key takeaways, which can be acted upon by disaster managers [624, 639, 640]. 8.5 Discussion Climate change will have profound effects on the planet, and the ML ”
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding (2020, methodology): “Natural disaster dataset can be initially classiﬁed into two classes: A) Non-imaging dataset (text, tweets, social media post) [35, 58] and B) Imaging datasets [60, 29, 12].”
RealTime QA: What's the Answer Right Now? (2022, background): “For example, a QA system with timely updates can improve emergency management of natural disasters (Imran et al., 2013, 2015, 2016; Nguyen et al., 2016) or pandemics (e.g., COVID-19; Wang et al., 2020; Lee et al., 2020; Möller et al., 2020; Alzubi et al., 2021).”
Online and Social Media Data As an Imperfect Continuous Panel Survey (2016, background): “Much research has demonstrated the promise of online and social media for use in grass roots political mobilization [13], astroturfing [14], predicting outbreaks of influenza [15], reproducing economic indices [16], crisis response [17], and measuring people ’ s happiness and mood [18,19].”
CrisisMMD: Multimodal Twitter Datasets from Natural Disasters (2018, background): “The use of social media such as Twitter, Facebook, and Youtube, has been explored in numerous studies (Imran et al. 2014; Vieweg et al. 2010; Imran et al. 2015; Terpstra et al. 2012; Tsou et al. 2017) for curating, analyzing and summarizing crisis-related information in order to make some decisions…”