Abstract
In this research, we hypothesize that some social users are more gullible to fake news than others, and accordingly investigate on the susceptibility of users to fake news-i.e., how to identify susceptible users, what are their characteristics, and if one can build a prediction model.Building on the crowdsourced annotations of 5 types of susceptible users in Twitter, we found out that: (1) susceptible users are correlated with a combination of user, network, and content features; (2) one can build a reasonably accurate prediction model with 0.82 in AUC-ROC for the multinomial classification task; and (3) there exists a correlation between the dominant susceptibility level of center nodes and that of the entire network.
| Original language | English (US) |
|---|---|
| Title of host publication | WebSci 2019 - Proceedings of the 11th ACM Conference on Web Science |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 287-288 |
| Number of pages | 2 |
| ISBN (Electronic) | 9781450362023 |
| DOIs | |
| State | Published - Jun 26 2019 |
| Event | 11th ACM Conference on Web Science, WebSci 2019 - Boston, United States Duration: Jun 30 2019 → Jul 3 2019 |
Publication series
| Name | WebSci 2019 - Proceedings of the 11th ACM Conference on Web Science |
|---|
Conference
| Conference | 11th ACM Conference on Web Science, WebSci 2019 |
|---|---|
| Country | United States |
| City | Boston |
| Period | 6/30/19 → 7/3/19 |
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
Cite this
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How gullible are you? predicting susceptibility to fake news. / Shen, Tracy Jia; Cowell, Robert; Gupta, Aditi; Le, Thai; Yadav, Amulya; Lee, Dongwon.
WebSci 2019 - Proceedings of the 11th ACM Conference on Web Science. Association for Computing Machinery, Inc, 2019. p. 287-288 (WebSci 2019 - Proceedings of the 11th ACM Conference on Web Science).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - How gullible are you? predicting susceptibility to fake news
AU - Shen, Tracy Jia
AU - Cowell, Robert
AU - Gupta, Aditi
AU - Le, Thai
AU - Yadav, Amulya
AU - Lee, Dongwon
PY - 2019/6/26
Y1 - 2019/6/26
N2 - In this research, we hypothesize that some social users are more gullible to fake news than others, and accordingly investigate on the susceptibility of users to fake news-i.e., how to identify susceptible users, what are their characteristics, and if one can build a prediction model.Building on the crowdsourced annotations of 5 types of susceptible users in Twitter, we found out that: (1) susceptible users are correlated with a combination of user, network, and content features; (2) one can build a reasonably accurate prediction model with 0.82 in AUC-ROC for the multinomial classification task; and (3) there exists a correlation between the dominant susceptibility level of center nodes and that of the entire network.
AB - In this research, we hypothesize that some social users are more gullible to fake news than others, and accordingly investigate on the susceptibility of users to fake news-i.e., how to identify susceptible users, what are their characteristics, and if one can build a prediction model.Building on the crowdsourced annotations of 5 types of susceptible users in Twitter, we found out that: (1) susceptible users are correlated with a combination of user, network, and content features; (2) one can build a reasonably accurate prediction model with 0.82 in AUC-ROC for the multinomial classification task; and (3) there exists a correlation between the dominant susceptibility level of center nodes and that of the entire network.
UR - http://www.scopus.com/inward/record.url?scp=85069465485&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85069465485&partnerID=8YFLogxK
U2 - 10.1145/3292522.3326055
DO - 10.1145/3292522.3326055
M3 - Conference contribution
AN - SCOPUS:85069465485
T3 - WebSci 2019 - Proceedings of the 11th ACM Conference on Web Science
SP - 287
EP - 288
BT - WebSci 2019 - Proceedings of the 11th ACM Conference on Web Science
PB - Association for Computing Machinery, Inc
ER -