Abusive Language Detection in Online Conversations by Combining Content- and Graph-Based Features.

ABSTRACT: In recent years, online social networks have allowed world-wide users to meet and discuss. As guarantors of these communities, the administrators of these platforms must prevent users from adopting inappropriate behaviors. This verification task, mainly done by humans, is more and more difficult due to the ever growing amount of messages to check. Methods have been proposed to automatize this moderation process, mainly by providing approaches based on the textual content of the exchanged messages. Recent work has also shown that characteristics derived from the structure of conversations, in the form of conversational graphs, can help detecting these abusive messages. In this paper, we propose to take advantage of both sources of information by proposing fusion methods integrating content-

SUBMITTER: Cecillon N 

PROVIDER: S-EPMC7931951 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature