This paper describes our proposed solution for the Profiling Fake News Spreaders on Twitter shared task at PAN 2020 [23]. The task consists in determining whether a given author a set of Twitter posts is a fake news spreader or not, both for the English and Spanish languages. The proposed approach is based on modeling both types of users according to four main types of characteristics, i.e. stylometry, personality, emotions and feed embeddings. Our system achieved an accuracy of 60% for the English dataset, while 72% for the Spanish one.
Fersini, E., Armanini, J., D'Intorni, M. (2020). Profiling fake news spreaders: stylometry, personality, emotions and embeddings. In Working Notes of CLEF 2020 - Conference and Labs of the Evaluation Forum. CEUR-WS.
Profiling fake news spreaders: stylometry, personality, emotions and embeddings
Fersini, E
;
2020
Abstract
This paper describes our proposed solution for the Profiling Fake News Spreaders on Twitter shared task at PAN 2020 [23]. The task consists in determining whether a given author a set of Twitter posts is a fake news spreader or not, both for the English and Spanish languages. The proposed approach is based on modeling both types of users according to four main types of characteristics, i.e. stylometry, personality, emotions and feed embeddings. Our system achieved an accuracy of 60% for the English dataset, while 72% for the Spanish one.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
