TY - GEN
T1 - Debunking rumors in social networks
T2 - 11th ACM Conference on Web Science, WebSci 2019
AU - Wu, Liang
AU - Liu, Huan
N1 - Funding Information: National Science Foundation (NSF) grant 1614576 Publisher Copyright: © 2019 Association for Computing Machinery.
PY - 2019/6/26
Y1 - 2019/6/26
N2 - Social networks have been instrumental in spreading rumor such as fake news and false rumors. Research in rumor intervention to date has concentrated on launching an intervening campaign to limit the number of infectees. However, many emerging and important tasks focus more on early intervention. Social and psychological studies have revealed that rumors might evolve 70% of its original content after 6 transmissions. Therefore, ignoring earliness of intervention makes the intervening campaign downgrade rapidly due to the evolved content. In real social networks, the number of social actors is usually large, while the budget for an intervening campaign is relatively small. The limited budget makes early intervention particularly challenging. Nonetheless, we present an eicient containment method that promptly terminates the difusion with least cost. To our knowledge, this work is the irst to study the earliness of rumor intervention in a large real-world social network. Evaluations on a network of 3 million users show that the key social actors who earliest terminate the spread are not necessarily the most inluential users or friends of rumor initiators, and the proposed method efectively reduces the life span of rumors.
AB - Social networks have been instrumental in spreading rumor such as fake news and false rumors. Research in rumor intervention to date has concentrated on launching an intervening campaign to limit the number of infectees. However, many emerging and important tasks focus more on early intervention. Social and psychological studies have revealed that rumors might evolve 70% of its original content after 6 transmissions. Therefore, ignoring earliness of intervention makes the intervening campaign downgrade rapidly due to the evolved content. In real social networks, the number of social actors is usually large, while the budget for an intervening campaign is relatively small. The limited budget makes early intervention particularly challenging. Nonetheless, we present an eicient containment method that promptly terminates the difusion with least cost. To our knowledge, this work is the irst to study the earliness of rumor intervention in a large real-world social network. Evaluations on a network of 3 million users show that the key social actors who earliest terminate the spread are not necessarily the most inluential users or friends of rumor initiators, and the proposed method efectively reduces the life span of rumors.
KW - Classiication
KW - Graph Mining
KW - Social Media Mining
KW - Social Network Analysis
UR - http://www.scopus.com/inward/record.url?scp=85069524153&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85069524153&partnerID=8YFLogxK
U2 - 10.1145/3292522.3326025
DO - 10.1145/3292522.3326025
M3 - Conference contribution
T3 - WebSci 2019 - Proceedings of the 11th ACM Conference on Web Science
SP - 323
EP - 331
BT - WebSci 2019 - Proceedings of the 11th ACM Conference on Web Science
PB - Association for Computing Machinery, Inc
Y2 - 30 June 2019 through 3 July 2019
ER -