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Article

Time of Your Hate: The Challenge of Time in Hate Speech Detection on Social Media

1
Department of Computer Science, University of Turin, 10149 Turin, Italy
2
Department of Computer Science, University of Bari “Aldo Moro”, 70126 Bari, Italy
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(12), 4180; https://doi.org/10.3390/app10124180
Received: 21 April 2020 / Revised: 5 June 2020 / Accepted: 9 June 2020 / Published: 18 June 2020
(This article belongs to the Special Issue Sentiment Analysis for Social Media Ⅱ)
The availability of large annotated corpora from social media and the development of powerful classification approaches have contributed in an unprecedented way to tackle the challenge of monitoring users’ opinions and sentiments in online social platforms across time. Such linguistic data are strongly affected by events and topic discourse, and this aspect is crucial when detecting phenomena such as hate speech, especially from a diachronic perspective. We address this challenge by focusing on a real case study: the “Contro l’odio” platform for monitoring hate speech against immigrants in the Italian Twittersphere. We explored the temporal robustness of a BERT model for Italian (AlBERTo), the current benchmark on non-diachronic detection settings. We tested different training strategies to evaluate how the classification performance is affected by adding more data temporally distant from the test set and hence potentially different in terms of topic and language use. Our analysis points out the limits that a supervised classification model encounters on data that are heavily influenced by events. Our results show how AlBERTo is highly sensitive to the temporal distance of the fine-tuning set. However, with an adequate time window, the performance increases, while requiring less annotated data than a traditional classifier. View Full-Text
Keywords: hate speech monitoring; diachronic analysis; microblogging data; supervised machine learning hate speech monitoring; diachronic analysis; microblogging data; supervised machine learning
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MDPI and ACS Style

Florio, K.; Basile, V.; Polignano, M.; Basile, P.; Patti, V. Time of Your Hate: The Challenge of Time in Hate Speech Detection on Social Media. Appl. Sci. 2020, 10, 4180. https://doi.org/10.3390/app10124180

AMA Style

Florio K, Basile V, Polignano M, Basile P, Patti V. Time of Your Hate: The Challenge of Time in Hate Speech Detection on Social Media. Applied Sciences. 2020; 10(12):4180. https://doi.org/10.3390/app10124180

Chicago/Turabian Style

Florio, Komal, Valerio Basile, Marco Polignano, Pierpaolo Basile, and Viviana Patti. 2020. "Time of Your Hate: The Challenge of Time in Hate Speech Detection on Social Media" Applied Sciences 10, no. 12: 4180. https://doi.org/10.3390/app10124180

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