Open AccessProceedings Article
Using social media to enhance emergency situation awareness
Jie Yin,Sarvnaz Karimi,Andrew Lampert,Mark Cameron,Bella Robinson,Robert Power +5 more
- pp 4234-4238
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TLDR
The described system uses natural language processing and data mining techniques to extract situation awareness information from Twitter messages generated during various disasters and crises.Abstract:
Social media platforms, such as Twitter, offer a rich source of real-time information about real-world events, particularly during mass emergencies. Sifting valuable information from social media provides useful insight into time-critical situations for emergency officers to understand the impact of hazards and act on emergency responses in a timely manner. This work focuses on analyzing Twitter messages generated during natural disasters, and shows how natural language processing and data mining techniques can be utilized to extract situation awareness information from Twitter. We present key relevant approaches that we have investigated including burst detection, tweet filtering and classification, online clustering, and geotagging.read more
Citations
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Journal ArticleDOI
Social network analysis
Jooho Kim,Makarand Hastak +1 more
TL;DR: The study explores patterns created by the aggregated interactions of online users on Facebook during disaster responses and provides insights to understand the critical role of social media use for emergency information propagation.
Journal ArticleDOI
Twitter as a tool for the management and analysis of emergency situations: A systematic literature review
TL;DR: A systematic literature review is conducted that provides an overview of the current state of research concerning the use of Twitter to emergencies management, as well as presents the challenges and future research directions.
Journal ArticleDOI
Emergency information diffusion on online social media during storm Cindy in U.S.
TL;DR: Certain types of Twitter users (news and weather agencies) were dominant as information sources and information diffusers (the public and organizations) however, the information flow in the network was controlled by numerous types of users including news, agency, weather agencies and the public.
Journal ArticleDOI
Can We Predict a Riot? Disruptive Event Detection Using Twitter
TL;DR: An end-to-end integrated event detection framework that comprises five main components: data collection, pre-processing, classification, online clustering, and summarization is presented and an evaluation of the effectiveness of detecting events using a variety of features derived from Twitter posts is presented.
Journal ArticleDOI
Descriptive and visual summaries of disaster events using artificial intelligence techniques: case studies of Hurricanes Harvey, Irma, and Maria
TL;DR: This work shows that textual and imagery content on social media provide complementary information useful to improve situational awareness and proposes a methodological approach that combines several computational techniques effectively in a unified framework to help humanitarian organisations in their relief efforts.
References
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Proceedings ArticleDOI
An interactive interface for visualizing events on Twitter
Andrew James McMinn,Daniel Tsvetkov,Tsvetan Yordanov,Andrew Patterson,Rrobi Szk,Jesus A. Rodriguez Perez,Joemon M. Jose +6 more
TL;DR: This work has developed an interactive interface for visualizing events, backed by a state-of-the-art event detection approach, which is able to detect, track and summarize events in real-time.
Proceedings Article
Clustering Microtext Streams for Event Identification
TL;DR: This paper proposes a novel two-phase approach for clustering streaming microtext, in particular Twitter messages, into event-based clusters and demonstrates that the proposed approach can achieve better clustering accuracy than state-ofthe-art methods.
Journal ArticleDOI
Evaluation methods for statistically dependent text
Sarvnaz Karimi,Jie Yin,Jiri Baum +2 more
TL;DR: By ignoring the statistical dependence of the text messages published in social media, standard cross-validation can result in misleading conclusions in a machine learning task, and this work explores alternative evaluation methods that explicitly deal with statistical dependence in text.