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The attack against the Charlie Hebdo weekly in Paris, in the year 2015, was a disruptive event that generated an important public reaction in social networks, creating the opportunity to study the phenomenon of violent communication and hate messages on Twitter. In the days after the attack (between January 7 and January 12), a sample of more than 255,000 tweets with the hashtags #CharlieHebdo, #JeSuisCharlie and #StopIslam was collected. An analysis was made using qualitative and quantitative approaches to contrast the level of agreement between the different methods used. In the first place, messages were classified as tweets that contained violent and hate speech or general messages, following the inclusion criteria that based on experience and the scientific literature were defined by the Principal Investigator. Then, three pairs of judges classified the sample using the excluding criteria previously defined, according to which ten types of violent speech communication were identified, which were reduced to five essential categories. After the qualitative analysis, the methods of Data Mining were used with the purpose of extracting systems of rules for the classification of the type of speech, beginning with 18 variables derived from each tweet, including date, favorites or the type of software used for the tweet, among others. The results show that disruptive events are followed by communications that show spatial temporal and textual patterns clearly identifiable; this allows the authors to propose a methodology to classify in a very precise way, those messages that contain hate or violent speech.

Cyber hate speech on twitter: Analyzing disruptive events from social media to build a violent communication and hate speech taxonomy