[2306.16495] Event Detection from Social Media Stream: Methods, Datasets and Opportunities
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Computer Science > Social and Information Networks

arXiv:2306.16495 (cs)
[Submitted on 28 Jun 2023]

Title:Event Detection from Social Media Stream: Methods, Datasets and Opportunities

Authors:Quanzhi Li, Yang Chao, Dong Li, Yao Lu, Chi Zhang
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Abstract:Social media streams contain large and diverse amount of information, ranging from daily-life stories to the latest global and local events and news. Twitter, especially, allows a fast spread of events happening real time, and enables individuals and organizations to stay informed of the events happening now. Event detection from social media data poses different challenges from traditional text and is a research area that has attracted much attention in recent years. In this paper, we survey a wide range of event detection methods for Twitter data stream, helping readers understand the recent development in this area. We present the datasets available to the public. Furthermore, a few research opportunities
Comments: 8 pages
Subjects: Social and Information Networks (cs.SI); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
Cite as: arXiv:2306.16495 [cs.SI]
  (or arXiv:2306.16495v1 [cs.SI] for this version)
  https://doi.org/10.48550/arXiv.2306.16495

Submission history

From: Quanzhi Li [view email]
[v1] Wed, 28 Jun 2023 18:40:03 UTC (456 KB)
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