Monitoring Users’ Behavior: Anti-Immigration Speech Detection on Twitter

Pitropakis, Nikolaos; Kokot, Kamil; Gkatzia, Dimitra; Ludwiniak, Robert; Mylonas, Alexios; Kandias, Miltiadis

Abstract

The proliferation of social media platforms changed the way people interact online. However, engagement with social media comes with a price, the users’ privacy. Breaches of users’ privacy, such as the Cambridge Analytica scandal, can reveal how the users’ data can be weaponized in political campaigns, which many times trigger hate speech and anti-immigration views. Hate speech detection is a challenging task due to the different sources of hate that can have an impact on the language used, as well as the lack of relevant annotated data. To tackle this, we collected and manually annotated an immigration-related dataset of publicly available Tweets in UK, US, and Canadian English. In an empirical study, we explored anti-immigration speech detection utilizing various language features (word n-grams, character n-grams) and measured their impact on a number of trained classifiers. Our work demonstrates that using word n-grams results in higher precision, recall, and f-score as compared to character n-grams. Finally, we discuss the implications of these results for future work on hate-speech detection and social media data analysis in general.

Citation

Pitropakis, N., Kokot, K., Gkatzia, D., Ludwiniak, R., Mylonas, A., & Kandias, M. (2020). Monitoring Users’ Behavior: Anti-Immigration Speech Detection on Twitter. Machine Learning and Knowledge Extraction, 2(3), 192-215. https://doi.org/10.3390/make2030011

• Journal Article Type: Article
• Acceptance Date: Jul 30, 2020
• Online Publication Date: Aug 3, 2020
• Publication Date: Aug 3, 2020
• Deposit Date: Aug 10, 2020
• Publicly Available Date: Aug 10, 2020
• Journal: Machine Learning and Knowledge Extraction
• Print ISSN: 2504-4990
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 2
• Issue: 3
• Pages: 192-215
• DOI: https://doi.org/10.3390/make2030011
• Keywords: social media; twitter; privacy; behavior tracking; NLP
• Public URL: http://researchrepository.napier.ac.uk/Output/2680458