Predicting Controversial News Using Facebook Reactions
Angelo Basile, Tommaso Caselli, Malvina Nissim · Accademia University Press eBooks · 2017
Different events and their reception in different reader communities may give rise to controversy. We propose a distant supervised entropy-based model that uses Facebook reactions as proxies for predicting news controversy. We prove the validity of this approach by running within- and across-source experiments, where different news sources are conceived to approximately correspond to different reader communities. Contextually, we also present and share an automatically generated corpus for controversy prediction in Italian.