Exploring deep learning for the analysis of emotional reactions to terrorist events on Twitter - G. D. Harb, Jonathas; Ebeling, Régis; Becker, Karin (Journal of Information and Data Management, 2019)
Terrorist events have a substantial emotional impact on the population, and understanding these effects is very important to design effective assistance programs. However, investigating community-wide traumas is a complex and costly task, where most challenges are related to the data collection process. Social media has been used as a relevant source of data to investigate people’s sentiments and ideas. In this article, we study the emotional reactions of Twitter users regarding two terrorist events that occurred in the United Kingdom. The contributions are twofold: a) we experiment two deep learning architectures to develop an emotion classifier, and b) we develop an analysis on tweets related to terrorist events to underst and whether there is an emotional shift due to a terrorist attack andwhether the emotional reactions are dependent on the event, or on the demographics of the users. Both models, based on convolutional and recurrent neural architectures, presented very similar performances. The analyses revealed an emotion shift due to the events and a difference in the reactions to each specific event, where gender is the most significant factor.