TY - GEN
T1 - Lambretta
T2 - 44th IEEE Symposium on Security and Privacy, SP 2023
AU - Paudel, Pujan
AU - Blackburn, Jeremy
AU - De Cristofaro, Emiliano
AU - Zannettou, Savvas
AU - Stringhini, Gianluca
N1 - Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
PY - 2023
Y1 - 2023
N2 - To curb the problem of false information, social media platforms like Twitter started adding warning labels to content discussing debunked narratives, with the goal of providing more context to their audiences. Unfortunately, these labels are not applied uniformly and leave large amounts of false content unmoderated. This paper presents LAMBRETTA, a system that automatically identifies tweets that are candidates for soft moderation using Learning To Rank (LTR). We run Lambretta on Twitter data to moderate false claims related to the 2020 US Election and find that it flags over 20 times more tweets than Twitter, with only 3.93% false positives and 18.81% false negatives, outperforming alternative state-of-the-art methods based on keyword extraction and semantic search. Overall, LAMBRETTA assists human moderators in identifying and flagging false information on social media.
AB - To curb the problem of false information, social media platforms like Twitter started adding warning labels to content discussing debunked narratives, with the goal of providing more context to their audiences. Unfortunately, these labels are not applied uniformly and leave large amounts of false content unmoderated. This paper presents LAMBRETTA, a system that automatically identifies tweets that are candidates for soft moderation using Learning To Rank (LTR). We run Lambretta on Twitter data to moderate false claims related to the 2020 US Election and find that it flags over 20 times more tweets than Twitter, with only 3.93% false positives and 18.81% false negatives, outperforming alternative state-of-the-art methods based on keyword extraction and semantic search. Overall, LAMBRETTA assists human moderators in identifying and flagging false information on social media.
UR - http://www.scopus.com/inward/record.url?scp=85166479335&partnerID=8YFLogxK
U2 - 10.1109/SP46215.2023.10179392
DO - 10.1109/SP46215.2023.10179392
M3 - Conference contribution
AN - SCOPUS:85166479335
T3 - Proceedings - IEEE Symposium on Security and Privacy
SP - 311
EP - 326
BT - Proceedings - 44th IEEE Symposium on Security and Privacy, SP 2023
PB - Institute of Electrical and Electronics Engineers (IEEE)
Y2 - 22 May 2023 through 25 May 2023
ER -