Catching Lies in the Act: A Framework for Early Misinformation Detection on Social Media
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.- doi
- 10.1145/3603163.3609057
- isbn
- 979-8-4007-0232-7
- name
- Catching Lies in the Act: A Framework for Early Misinformation Detection on Social Media
- source
- acm_url
- https://dl.acm.org/doi/10.1145/3603163.3609057
- authors
- Shreya Ghosh, Prasenjit Mitra
- doi_url
- https://doi.org/10.1145/3603163.3609057
- license
- © Copyright held by the owner/author(s). Publication rights licensed to ACM.
- summary
- 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.
- published
- 2023-09-04
- conference
- HT '23: 34th ACM Conference on Hypertext and Social Media, Rome, Italy, September 4-8, 2023
- open_access
- false
- acm_html_url
- https://dl.acm.org/doi/full/10.1145/3603163.3609057
- displayAuthor
- Shreya Ghosh, Prasenjit Mitra
- source_sha256
- 6d2012e2bd6f414502acfabe1056dfc3b3cc913175896168bf95ea5d481af069
- displayPublishTime
- 2023-09-04
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