ABSTRACT
This chapter focuses on the phenomenon of fear of terrorist attacks in cyberspace through the analysis of linguistically encoded emotional responses of online social media (tweets). The analysis sample is tweets related to the attacks on Charlie Hebdo, Nice, and Barcelona between 2015 and 2017. The main objective is to analyze the evolution of emotivity in tweets during the first 24 hours after each of the attacks through automatized sentiment analysis. A method to quantify the fear of terrorist attacks in this environment through linguistic sentiment analysis is proposed. The main results indicate that the emotional profile of tweets after terrorist events changes as a function of elapsed time after the attack, the hashtag, and the event in question. This study follows the recent critical trend of traditional methodologies for approaching the phenomenon of fear based on self-reported measures, offering an alternative that combines Big Data, real-time data collection, and linguistic sentiment analysis in text-mediated communication.
