Year: 2017
Type: conference-paper
Abstract: Fake news has become a hotly debated topic in journalism.In this paper, we present our entry to the 2017 Fake News Challenge which models the detection of fake news as a stance classification task that finished in 11th place on the leader board.Our entry is an ensemble system of classifiers developed by students in the context of their coursework.We show how we used the stacking ensemble method for this purpose and obtained improvements in classification accuracy exceeding each of the individual models' performance on the development data.Finally, we discuss aspects of the experimental setup of the challenge.
DOI: 10.18653/v1/w17-4214
Institution: University of Sheffield
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
Funder: Horizon 2020 Framework Programme
Award: G495187929
OpenAlex ID: W2756851436