TY - GEN
T1 - Unsupervised Post-Time Fake Social Message Detection with Recommendation-aware Representation Learning
AU - Hsiao, Shao Ping
AU - Tsai, Yu Che
AU - Li, Cheng Te
N1 - Funding Information:
ACKNOWLEDGMENTS This work is supported by the Ministry of Science and Technology (MOST) of Taiwan under grants 110-2221-E-006-136-MY3, 110-2221-E-006-001, and 110-2634-F-002-051.
Publisher Copyright:
© 2022 ACM.
PY - 2022/4/25
Y1 - 2022/4/25
N2 - This paper deals with a more realistic scenario of fake message detection on social media, i.e., unsupervised post-time detection. Given a source message, our goal is to determine whether it is fake without using labeled data and without requiring user interacted with the given message. We present a novel learning framework, Recommendation-aware Message Representation (RecMR), to achieve the goal. The key idea is to learn user preferences and have them encoded into the representation of the source message through jointly training the tasks of user recommendation and binary detection. Experiments conducted on two real Twitter datasets exhibit the promising performance of RecMR, and show the effectiveness of recommended users in unsupervised detection.
AB - This paper deals with a more realistic scenario of fake message detection on social media, i.e., unsupervised post-time detection. Given a source message, our goal is to determine whether it is fake without using labeled data and without requiring user interacted with the given message. We present a novel learning framework, Recommendation-aware Message Representation (RecMR), to achieve the goal. The key idea is to learn user preferences and have them encoded into the representation of the source message through jointly training the tasks of user recommendation and binary detection. Experiments conducted on two real Twitter datasets exhibit the promising performance of RecMR, and show the effectiveness of recommended users in unsupervised detection.
UR - http://www.scopus.com/inward/record.url?scp=85137513983&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85137513983&partnerID=8YFLogxK
U2 - 10.1145/3487553.3524259
DO - 10.1145/3487553.3524259
M3 - Conference contribution
AN - SCOPUS:85137513983
T3 - WWW 2022 - Companion Proceedings of the Web Conference 2022
SP - 232
EP - 235
BT - WWW 2022 - Companion Proceedings of the Web Conference 2022
PB - Association for Computing Machinery, Inc
T2 - 31st ACM Web Conference, WWW 2022
Y2 - 25 April 2022
ER -