Facebook Reactions as Controversy Proxies: Predictive Models over Italian News

Discussion on social media over controversial topics can easily escalate to harsh interactions. Being able to predict whether a certain post will be controversial, and what reactions it might give rise to, could help moderators provide a better experience for all users. We develop a battery of distant supervised models that use Facebook reactions as proxies for predicting news controversy, building on the idea that controversy can be modeled via the entropy of the reaction distribution to a post. We create a Facebook-based corpus for the study of controversy in Italian, and test on it the validity of our approach as well as a series of controversy models. Results show that controversy and reactions can be modelled successfully at various degrees of granularity

Source-driven Representations for Hate Speech Detection

Sources, in the form of selected Facebook pages, can be used as indicators of hate-rich content. Polarized distributed representations created over such content prove superior to generic embeddings in the task of hate speech detection. The same content seems to carry a too weak signal to proxy silver labels in a distant supervised setting. However, this signal is stronger than gold labels which come from a different distribution, leading to re-think the process of annotation in the context of highly subjective judgments.La provenienza di ciò che viene condiviso su Facebook costituisce un primo elemento indentificativo di contentuti carichi di odio. La rappresentazione distribuita polarizzata che costruiamo su tali contenuti si dimostra migliore nell’individuazione di argomenti di odio rispetto ad alternative più generiche. Il potere predittivo di tali embedding polarizzati risulta anche più incisivo rispetto a quello di dati gold standard che sono caratterizzati da una distribuzione ed una annotatione diverse

Source-driven Representations for Hate Speech Detection

Sources, in the form of selected Facebook pages, can be used as indicators of hate-rich content. Polarized distributed representations created over such content prove superior to generic embeddings in the task of hate speech detection. The same content seems to carry a too weak signal to proxy silver labels in a distant supervised setting. However, this signal is stronger than gold labels which come from a different distribution, leading to re-think the process of annotation in the context of highly subjective judgments.La provenienza di ciò che viene condiviso su Facebook costituisce un primo elemento indentificativo di contentuti carichi di odio. La rappresentazione distribuita polarizzata che costruiamo su tali contenuti si dimostra migliore nell’individuazione di argomenti di odio rispetto ad alternative più generiche. Il potere predittivo di tali embedding polarizzati risulta anche più incisivo rispetto a quello di dati gold standard che sono caratterizzati da una distribuzione ed una annotatione diverse

Facebook Reactions as Controversy Proxies:Predictive Models over Italian News

Discussion on social media over controversial topics can easily escalate to harsh interactions. Being able to predict whether a certain post will be controversial, and what reactions it might give rise to, could help moderators provide a better experience for all users. We develop a battery of distant supervised models that use Facebook reactions as proxies for predicting news controversy, building on the idea that controversy can be modeled via the entropy of the reaction distribution to a post. We create a Facebook-based corpus for the study of controversy in Italian, and test on it the validity of our approach as well as a series of controversy models. Results show that controversy and reactions can be modelled successfully at various degrees of granularity