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

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

A Distant Supervision Approach to Semantic Role Labeling

Semanticrolelabelinghasbecomeakeymodule for many language processing applications such as question answering, information extraction, sentiment analysis, and machine translation. To build an unrestricted semantic role labeler, the first step is to develop a comprehensive proposition bank. However, creating such a bank is a costly enterprise, which has only been achieved for a handful of languages. In this paper, we describe a technique to build proposition banks for new languages using distant supervision. Starting from PropBank inEnglishandlooselyparallelcorporasuchas versions of Wikipedia in different languages, we carried out a mapping of semantic propositions we extracted from English to syntactic structures in Swedish using named entities. We trained a semantic parser on the generated Swedishpropositionsandwereporttheresults we obtained. Using the CoNLL 2009 evaluation script, we could reach the scores of 52.25 for labeled propositions and 62.44 for the unlabeled ones. We believe our approach can be appliedtotrainsemanticrolelabelersforother resource-scarce languages