In this paper we present HateChecker, a tool for the automatic detection of hater users in online social networks which has been developed within the activities of”Contro L’Odio” research project. In a nutshell, our tool implements a methodology based on three steps: (i) all the Tweets posted by a target user are gathered and processed. (ii) sentiment analysis techniques are exploited to automatically label intolerant Tweets as hate speeches. (iii) a lexicon is used to classify hate speeches against a set of specific categories that can describe the target user (e.g., racist, homophobic, antisemitic, etc.). Finally, the output of the tool, that is to say, a set of labels describing (if any) the intolerant traits of the target user, are shown through an interactive user interface and exposed through a REST web service for the integration in third-party applications. In the experimental evaluation we crawled and annotated a set of 200 Twitter profiles and we investigated to what extent our tool is able to correctly identify hater users. The results confirmed the validity of our methodology and paved the way for several future research directions.

HateChecker: A tool to automatically detect hater users in online social networks

Musto C.
;
Polignano M.;Semeraro G.;
2019

Abstract

In this paper we present HateChecker, a tool for the automatic detection of hater users in online social networks which has been developed within the activities of”Contro L’Odio” research project. In a nutshell, our tool implements a methodology based on three steps: (i) all the Tweets posted by a target user are gathered and processed. (ii) sentiment analysis techniques are exploited to automatically label intolerant Tweets as hate speeches. (iii) a lexicon is used to classify hate speeches against a set of specific categories that can describe the target user (e.g., racist, homophobic, antisemitic, etc.). Finally, the output of the tool, that is to say, a set of labels describing (if any) the intolerant traits of the target user, are shown through an interactive user interface and exposed through a REST web service for the integration in third-party applications. In the experimental evaluation we crawled and annotated a set of 200 Twitter profiles and we investigated to what extent our tool is able to correctly identify hater users. The results confirmed the validity of our methodology and paved the way for several future research directions.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11586/272182
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact