Hate Speech Patterns in Social Media: A Methodological Framework and Fat Stigma Investigation Incorporating Sentiment Analysis, Topic Modelling and Discourse Analysis
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Date
8/02/2023
Open Access Location
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Publisher
Australasian Association for Information Systems and Australian Computer Society
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Abstract
Social media offers users an online platform to freely express themselves; however, when users post opinionated and offensive comments that target certain individuals or communities, this could instigate animosity towards them. Widespread condemnation of obesity (fatness) has led to much fat stigmatizing content being posted online. A methodological framework that uses a novel mixed-method approach for unearthing hate speech patterns from large text-based corpora gathered from social media is proposed. We explain the use of computer-mediated quantitative methods comprising natural language processing techniques such as sentiment analysis, emotion analysis and topic modelling, along with qualitative discourse analysis. Next, we have applied the framework to a corpus of texts on gendered and weight-based data that have been extracted from Twitter and Reddit. This assisted in the detection of different emotions being expressed, the composition of word frequency patterns and the broader fat-based themes underpinning the hateful content posted online. The framework has provided a synthesis of quantitative and qualitative methods that draw on social science and data mining techniques to build real-world knowledge in hate speech detection. Current information systems research is limited in its use of mixed analytic approaches for studying hate speech in social media. Our study therefore contributes to future research by establishing a roadmap for conducting mixed-method analyses for better comprehension and understanding of hate speech patterns.
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© 2023 The Author/s
Keywords
social media, hate speech, sentiment analysis, topic modelling, discourse analysis, fat stigma
Citation
Australasian Journal of Information Systems, 2023, 27 pp. 1 - 29 (29)