Hate Speech and Topic Shift in the Covid-19 Public Discourse on Social Media in Italy
Authors: Komal Florio, Valerio Basile, Viviana Patti (Università di Torino, 2021)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
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 but also arose the challenge of temporal robustness of such detection and monitoring systems. We used as case study a dataset of tweets in Italian related to the COVID-19 induced lockdown in Italy to measure how quickly the most debated topic online shifted in time. We concluded that it is a promising approach but dedicated corpora and fine tuning of algorithms are crucial for more insightful results.

Introduction
The task of abusive message detection is a very challenging one and from multiple perspective. From the computational point of view, despite the increasing interest and effort of the community on developing automatic systems abusive language detection and related tasks for different languages <cit.>, the robustness of detection and monitoring systems emerges as a crucial factor to be addressed, where one of the main limitations observed is to consider the Natural Language Processing (NLP) task of detecting abusive language in isolation, without taking into account the intersection with the contextual or social dimensions, that could contribute to a more holistic comprehension of the abusive phenomena in language. In fact, it is becoming increasingly evident that the goodness of hate speech prediction systems, and of NLP algorithms in general, is rooted in how well they capture and model all the relevant characteristics of language in the context of a specific phenomenon and its evolution over time <cit.>. This brought us to intersect our NLP research with the field of Computational Social Science. The recent availability of long-term and large-scale digital corpora and the effectiveness of methods for representing words over time can play a crucial role in the recent advances in this field. In particular, social media have recently become one of the predominant sources of linguistic data, being the venue for noticeable phenomena in the domain of NLP tasks. They represent the ideal communication context to address the challenges we have outlined. This paper aims to characterize how the online conversation on the Italian Twitter around the first Covid-19 lockdown, imposed in Italy in 2020, shifted very quickly from one heated debate to another one, following the quick succession of news reports on both news cases and institutional advice and rules on how to navigate everyday life as the crisis was unfolding in the entirety of the world. At first we tried to identify the most polarizing conversation by analyzing the presence of hate speech using AlBERTo <cit.> but we found that this BERT <cit.> based algorithm, trained on Italian Social Media language, seemed to under-perform, in comparison with similar case studies <cit.>. We hence performed the same task using an abusive language computational lexicon, Hurtlex <cit.>. We discovered the most recurrent types of abusive language, their distribution over time and correlation with real life events regarding the ongoing pandemic. To identify the most debated topics we resorted to topic modeling and in particular the Dynamic Topic Modeling allowed us to describe how the most frequent topics evolved over time and shed lights on the interplay with the governmental measures that sparked the most debated conversations.

Conclusion
In this work we tried to tackle the challenge of measuring and quantifying the topic shift in the public discourse on Social Media, using as a case study the online debate on Twitter following the Covid-19 related lockdown in Italy in 2020, by means of a dedicated dataset.  By combining multiple classification methods we gathered insights into which governmental measures generated the most debated online conversation but we also concluded for the need of deeper investigation on how to build ad hoc corpora and methods to investigate specific linguistic phenomena as online conversation with rapid topic shift following the flow of news coming from both online and traditional media outlets. We also tried to inform AlBERTo with information extracted from topic modeling but the results were far from satisfying. This is a promising way to enhance the accuracy of hate speech prediction, but we concluded that a further investigation on size and characteristics of datasets is essential to gain better results.