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.
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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.
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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
Beyond Trending Topics: Real-World Event Identification on Twitter
TL;DR: This paper explores approaches for analyzing the stream of Twitter messages to distinguish between messages about real-world events and non-event messages, and relies on a rich family of aggregatestatistics of topically similar message clusters.
Journal ArticleDOI
Using Social Media to Enhance Emergency Situation Awareness
TL;DR: In this paper, a system uses natural language processing and data mining techniques to extract situation awareness information from Twitter messages generated during various disasters and crises, such as hurricanes, floods, and floods.
Journal ArticleDOI
Overview of multidatabase transaction management
TL;DR: It is argued that the multidatabase research will become increasingly important in the coming years and basic research issues in multid atabase transaction management are outlined, followed by a discussion of open problems and practical implications.
Proceedings ArticleDOI
TEDAS: A Twitter-based Event Detection and Analysis System
TL;DR: The overall system architecture is shown, the implementation of the components that crawl, classify, and rank tweets and extract location from tweets are explained, and some interesting results of the system are presented.
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Where Is This Tweet From? Inferring Home Locations of Twitter Users.
TL;DR: A new algorithm for inferring the home locations of Twitter users at different granularities, such as city, state, or time zone, using the content of their tweets and their tweeting behavior is presented.