Event detection from social media: 5W1H analysis on big data

Karaman, Cagla Cig; Yaliman, Serkan; Oto, Salih Atilay

doi:10.1109/siu.2017.7960211

Cited by 3 publications

(1 citation statement)

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“…Event detection models can assist in analyzing massive data and detecting events on social media. The traditional supervised learning method of event extraction is to label the 5W1H (What, who, where, when, why, and how) of each event, which can describe the main event from the article [23][24][25]. In [26], a framework based on the naive Bayes classifier to detect civil unrest events on Twitter is built to overcome text mining challenges.…”

Section: Related Workmentioning

confidence: 99%

Entity-Based Integration Framework on Social Unrest Event Detection in Social Media

Shen

Chow

2022

Electronics

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Social unrest events have been an issue of concern to people in various countries. In the past few years, mass unrest events appeared in many countries. Meanwhile, social media has become a distinctive method of spreading event information. It is necessary to construct an effective method to analyze the unrest events through social media platforms. Existing methods mainly target well-labeled data and take relatively little account of the event development. This paper proposes an entity-based integration event detection framework for event extraction and analysis in social media. The framework integrates two modules. The first module utilizes named entity recognition technology based on the bidirectional encoder representation from transformers (BERT) algorithm to extract the event-related entities and topics of social unrest events during social media communication. The second module suggests the K-means clustering method and dynamic topic model (DTM) for dynamic analysis of these entities and topics. As an experimental scenario, the effectiveness of the framework is demonstrated using the Lihkg discussion forum and Twitter from 1 August 2019 to 31 August 2020. In addition, the comparative experiment is performed to reveal the differences between Chinese users on Lihkg and Twitter for comparative social media studies. The experiment results somehow indicate the characteristic of social unrest events that can be found in social media.

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