Combining artificial intelligence and expert content analysis to explore radical views on twitter: Case study on far-right discourse

Public opinion is among the critical types of information that often inform policy changes and political strategies. Extreme public opinion is of particular importance due to the potential it has in leading to radical behaviors and violent actions. The monitoring and analysis of extreme public opinion are therefore of great interest to government officials seeking to maintain public order and preempt violent actions. Radicalization is the process employed to influence individuals to develop extreme views and behaviors. Due to their global reach and popularity, social media platforms are used tools for recruitment and radicalization. Recently, data mining and natural language processing techniques have been employed for the detection of extremism and radicalization online. However, the existing approaches focus on the classification of individuals as extremists or non-extremists, thus simplifying the concept of radicalization to a binary problem. Such approaches fail to capture the nuances and the complexity of the radicalization process that is influenced by many aspects such as peer pressure, social interactions, and the impact of prominent figures. Aiming at addressing those limitations, this work proposed a sophisticated approach for the analysis of extreme views expressed on social media. The proposed approach combines the power of artificial intelligence and natural language processing techniques with expert content analysis to achieve a fine-grained and detailed analysis of Twitter extremist content related to the Far-right ideology as a case study. A dataset of over 259,000 tweets collected over five years was used to test our approach, leading to sophisticated analytics and insights about Far-right extremism. The proposed approach can serve as a powerful decision support tool for governments for the analysis of extreme public opinion that is expressed online, and open the door for more effective and responsive decision making.