1183489
doi
10.1007/s13735-017-0143-x
oai:zenodo.org:1183489
user-invid-h2020
user-eu
Papadopoulos, Symeon
CERTH-ITI, Thessaloniki, Greece
Zampoglou, Markos
CERTH-ITI, Thessaloniki, Greece
Apostolidis, Lazaros
CERTH-ITI, Thessaloniki, Greece
Papadopoulou, Olga
CERTH-ITI, Thessaloniki, Greece
Kompatsiaris, Yiannis
CERTH-ITI, Thessaloniki, Greece
Detection and Visualization of Misleading Content on Twitter
Boididou, Christina
Urban Big Data Centre Glasgow, UK
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
social media
verication
fake detection
information credibility
<p>The problems of online misinformation and fake news have gained increasing prominence in an age where user-generated content and social media platforms are key forces in the shaping and diffusion of news stories. Unreliable information and misleading content are often posted and widely disseminated through popular social media platforms such as Twitter and Facebook. As a result, journalists and editors are in need of new tools that can help them speed up the verication process for content that is sourced from social media. Motivated by this need, in this paper we present a system that supports the automatic classication of multimedia<br>
Twitter posts into credible or misleading. The system leverages credibility-oriented features extracted from the tweet and the user who published it, and trains a two-step classication model based on a novel semisupervised learning scheme. The latter uses the agreement between two independent pre-trained models on new posts as guiding signals for retraining the classication model.We analyze a large labeled dataset of tweets that shared debunked fake and conrmed real images and videos, and show that integrating the newly proposed features, and making use of bagging in the initial classiers and of the semi-supervised learning scheme, signicantly improves classication accuracy. Moreover, we present a web-based application for visualizing and communicating the classication results to end users.</p>
Zenodo
2017-12-04
info:eu-repo/semantics/article
1183488
user-invid-h2020
user-eu
award_title=In Video Veritas – Verification of Social Media Video Content for the News Industry; award_number=687786; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/687786; funder_id=00k4n6c32; funder_name=European Commission;
award_title=REVEALing hidden concepts in Social Media; award_number=610928; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/610928; funder_id=00k4n6c32; funder_name=European Commission;
1579539931.67513
2874336
md5:1e4e62cc194eb81fb3d85a9146fb3bee
https://zenodo.org/records/1183489/files/detection-visualization-misleading.pdf
public
International Journal of Multimedia Information Retrieval
7
1
71–86
2017-12-04