Details
| Original language | English |
|---|---|
| Title of host publication | HT`23 |
| Subtitle of host publication | Proceedings of the 34th ACM Conference on Hypertext and Social Media |
| ISBN (electronic) | 9798400702327 |
| Publication status | Published - 5 Sept 2023 |
| Event | 34th ACM Conference on Hypertext and Social Media, HT 2023 - Rome, Italy Duration: 4 Sept 2023 → 8 Sept 2023 |
Abstract
The proliferation of social media has intensified the necessity for automated misinformation detection. Existing methods often struggle with early detection, as key information is not readily available during the initial dissemination stages. In this paper, we introduce a novel model for early misinformation detection on social media by classifying information propagation paths and leveraging linguistic patterns. Our model incorporates a causal user attribute inference model to label users as potential misinformation propagators or believers. Designed for early detection, the model includes two auxiliary tasks: forecasting the scope of misinformation dissemination and clustering similar nodes (users) based on their attributes outperforming the current state-of-the-art benchmarks.
Keywords
- discourse analysis, Misinformation, social network
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Computer Graphics and Computer-Aided Design
- Computer Science(all)
- Human-Computer Interaction
- Computer Science(all)
- Software
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HT`23: Proceedings of the 34th ACM Conference on Hypertext and Social Media. 2023. 36.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Catching Lies in the Act
T2 - 34th ACM Conference on Hypertext and Social Media, HT 2023
AU - Ghosh, Shreya
AU - Mitra, Prasenjit
N1 - Funding Information: This research was partially funded by the Federal Ministry of Education and Research (BMBF), Germany under the project LeibnizK-ILabor with grant No. 01DD20003.
PY - 2023/9/5
Y1 - 2023/9/5
N2 - The proliferation of social media has intensified the necessity for automated misinformation detection. Existing methods often struggle with early detection, as key information is not readily available during the initial dissemination stages. In this paper, we introduce a novel model for early misinformation detection on social media by classifying information propagation paths and leveraging linguistic patterns. Our model incorporates a causal user attribute inference model to label users as potential misinformation propagators or believers. Designed for early detection, the model includes two auxiliary tasks: forecasting the scope of misinformation dissemination and clustering similar nodes (users) based on their attributes outperforming the current state-of-the-art benchmarks.
AB - The proliferation of social media has intensified the necessity for automated misinformation detection. Existing methods often struggle with early detection, as key information is not readily available during the initial dissemination stages. In this paper, we introduce a novel model for early misinformation detection on social media by classifying information propagation paths and leveraging linguistic patterns. Our model incorporates a causal user attribute inference model to label users as potential misinformation propagators or believers. Designed for early detection, the model includes two auxiliary tasks: forecasting the scope of misinformation dissemination and clustering similar nodes (users) based on their attributes outperforming the current state-of-the-art benchmarks.
KW - discourse analysis
KW - Misinformation
KW - social network
UR - http://www.scopus.com/inward/record.url?scp=85174226835&partnerID=8YFLogxK
U2 - 10.1145/3603163.3609057
DO - 10.1145/3603163.3609057
M3 - Conference contribution
AN - SCOPUS:85174226835
BT - HT`23
Y2 - 4 September 2023 through 8 September 2023
ER -