TY - GEN
T1 - Proactive Discovery of Fake News Domains from Real-Time Social Media Feeds
AU - Chen, Zhouhan
AU - Freire, Juliana
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/4/20
Y1 - 2020/4/20
N2 - The proliferation of web sites that disseminate fake news is a growing problem in our society. Not surprisingly, the problem of identifying whether a web page contains fake news has attracted substantial attention. However, the problem of discovering new sources of fake news has been largely unexplored. Timely discovery of such sources is critical to combat misinformation and minimize its potential harm. In this paper, we present an automatic discovery system that proactively surfaces fake news domains before they are flagged by humans. Our system operates in two-steps: first, it uses Twitter feeds to uncover user co-sharing structures to discover political websites; then it uses a topic-agnostic classifier to score and rank newly discovered domains. To demonstrate the effectiveness of our system, we conduct an experimental evaluation in which we collect tweets related to the 2020 presidential impeachment process in the United States, and show that not only our system is able to discover new sites, but that a large percentage of these sites are indeed publishing fake news. We also design an integrated user interface to support fact-checkers and leverage their knowledge. Through this interface, fact-checkers can visualize domain interaction networks, query domain fakeness score, and tag incorrectly predicted results. Our proactive discovery system will expedite fact-checking process and can be a powerful weapon in the toolbox to combat misinformation.
AB - The proliferation of web sites that disseminate fake news is a growing problem in our society. Not surprisingly, the problem of identifying whether a web page contains fake news has attracted substantial attention. However, the problem of discovering new sources of fake news has been largely unexplored. Timely discovery of such sources is critical to combat misinformation and minimize its potential harm. In this paper, we present an automatic discovery system that proactively surfaces fake news domains before they are flagged by humans. Our system operates in two-steps: first, it uses Twitter feeds to uncover user co-sharing structures to discover political websites; then it uses a topic-agnostic classifier to score and rank newly discovered domains. To demonstrate the effectiveness of our system, we conduct an experimental evaluation in which we collect tweets related to the 2020 presidential impeachment process in the United States, and show that not only our system is able to discover new sites, but that a large percentage of these sites are indeed publishing fake news. We also design an integrated user interface to support fact-checkers and leverage their knowledge. Through this interface, fact-checkers can visualize domain interaction networks, query domain fakeness score, and tag incorrectly predicted results. Our proactive discovery system will expedite fact-checking process and can be a powerful weapon in the toolbox to combat misinformation.
KW - fake news discovery
KW - misinformation
KW - social network analysis
UR - http://www.scopus.com/inward/record.url?scp=85091704672&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85091704672&partnerID=8YFLogxK
U2 - 10.1145/3366424.3385772
DO - 10.1145/3366424.3385772
M3 - Conference contribution
AN - SCOPUS:85091704672
T3 - The Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020
SP - 584
EP - 592
BT - The Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020
PB - Association for Computing Machinery
T2 - 29th International World Wide Web Conference, WWW 2020
Y2 - 20 April 2020 through 24 April 2020
ER -