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

• URL: http://arxiv.org/abs/2306.16495v1
• Date: Wed, 28 Jun 2023 18:40:03 GMT
• Title: Event Detection from Social Media Stream: Methods, Datasets and Opportunities
• Authors: Quanzhi Li, Yang Chao, Dong Li, Yao Lu, Chi Zhang
• Abstract summary: 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.
• Score: 20.42206536532482
• License: http://creativecommons.org/licenses/by/4.0/
• 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

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