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Detecting Expressional Anomie in Social Media via Fine-grained Content Mining

Detecting Expressional Anomie in Social Media via Fine-grained Content Mining
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Author(s): Qingqing Zhou (Nanjing Normal University, Nanjing, Jiangsu Province, China) and Ming Jing (Nanjing Normal University, Jiangsu Province, China)
Copyright: 2020
Volume: 31
Issue: 1
Pages: 19
Source title: Journal of Database Management (JDM)
Editor(s)-in-Chief: Keng Siau (Missouri University of Science and Technology, USA)
DOI: 10.4018/JDM.2020010101

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Abstract

Expression plays an important role in language inheritance, interpersonal communication, and social stability. With the rapid development of the Internet, people are becoming frequently interested in expressing themselves on social media. Meanwhile, massive anomic expressions are generated, which pollute network environments and even hinder social development. Hence, the purpose of this article is detecting anomic expressions in social media automatically, so as to reveal fine-grained status of online expressional anomie. Specifically, the authors used machine learning to detect anomic expressions and identify anomic types. Then, impacts of different factors (e.g. gender, region, time) on expressional anomie were analyzed. Finally, distributions and characteristics of expressional anomie about online contents were obtained. Empirical results indicate that the current situation of expressional anomie is severe, and scientific and effective treatments for anomic expression are necessary and urgently. Meanwhile, gender, region, and time should be taken into consideration in the formulation of treatments.

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