Exploring Deep Learning for the Analysis of Emotional Reactions to Terrorist Events on Twitter


  • Karin Becker UFRGS
  • Jonathas Gabriel Harb UFRGS
  • Regis Ebeling UFRGS


Terrorist events have a huge 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 Tweeter users regarding two terrorist events that occurred in 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 understand whether there is an emotional shift due to a terrorist attack, and whether 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 on the reactions to each specific event, where gender is the most significant factor.


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