Mining Factors Influencing Emotion Polarization in the Context of Disastrous Emergency Based on Machine Learning
Pages 39 - 44
Abstract
The world has seen a substantial number of disastrous emergencies, which lead to heated discussion on the Internet and then chaos without proper interference. Motivated by others’ emotions through online discussion, netizens actively interact with each other and flock together, eventually reaching to the same emotional state. That's what emotion polarization means. To guide the netizens, influencing factors accounting for emotion polarization must be identified. In this paper, we take the China Eastern Airlines MU5735 crash as an example. All the hot topics,comments and retweets concerning the air crash on Sina Weibo are collected by a web crawler from March 21 to April 1, 2022. The reciprocal of sentiment entropy is first used to measure emotion polarization. 7 main factors affecting emotion polarization are identified based on Hovland's Persuasion Model. In order to mine key influencing factors, four machine learning models are established and tuned, among which Adaptive Boosting regression model (AdaBoost) has the best performance. Then three influencing factors are mined by feature importance ranking of AdaBoost. By SHapley Additive exPlanations (SHAP), a more concise outcome of feature importance ranking is represented. Finallly, according to the figures of feature importance ranking, decision-making suggestions on facilitating emotion polarization is proposed.
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- Mining Factors Influencing Emotion Polarization in the Context of Disastrous Emergency Based on Machine Learning
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Published In
May 2024
73 pages
ISBN:9798400718205
DOI:10.1145/3695220
Copyright © 2024 ACM.
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Published: 03 October 2024
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ICBDC 2024
ICBDC 2024: 2024 9th International Conference on Big Data and Computing
May 24 - 26, 2024
Bangkok, Thailand
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