Emotion-Aware Monitoring of Users’ Reaction With a Multi-Perspective Analysis of Long- and Short-Term Topics on Twitter

Abstract

Social networks, such as Twitter, play like a disinformation spread booster giving the chance to individuals and organizations to influence users’ beliefs on purpose through tweets causing destabilization effects to the community. As a consequence, there is a need for solutions to analyse users’ reactions to topics debated in the community. To this purpose, state-of-the-art methods focus on selecting the most debated topics over time, ignoring less-frequent-discussed topics. In this paper, a framework for users’ reaction and topic analysis is introduced. First the method extracts topics as frequent itemsets of named entities from tweets collected, hence the support over time and RoBERTa-based sentiment analysis are applied to assess the current topic spread and the emotional impact, then a time-grid-based approach allows a granule-level analysis of the collected features that can be exploited for predicting future users’ reactions towards topics. Finally, a three-perspective score function is introduced to build comparative ranked lists of the most relevant topics according to topic sentiment, importance and spread. Experiences demonstrate the potential of the framework on IEEE COVID-19 Tweets Dataset.