Details
| Original language | English |
|---|---|
| Title of host publication | Machine Learning and Knowledge Discovery in Databases |
| Subtitle of host publication | Applied Data Science and Demo Track |
| Editors | Gianmarco De Francisci Morales, Francesco Bonchi, Claudia Perlich, Natali Ruchansky, Nicolas Kourtellis, Elena Baralis |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 174-189 |
| Number of pages | 16 |
| ISBN (electronic) | 978-3-031-43427-3 |
| ISBN (print) | 9783031434266 |
| Publication status | Published - 17 Sept 2023 |
| Event | European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023 - Turin, Italy Duration: 18 Sept 2023 → 22 Sept 2023 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 14174 LNAI |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
The rise of social media has amplified the need for automated detection of misinformation. Current methods face limitations in early detection because crucial information that they rely on is unavailable during the initial phases of information dissemination. This paper presents an innovative model for the early detection of misinformation on social media through the classification of information propagation paths and using linguistic patterns. We have developed and incorporated a causal user attribute inference model to label users as potential misinformation propagators or believers. Our model is designed for early detection of false information and includes two auxiliary tasks: predicting the extent of misinformation dissemination and clustering similar nodes (or users) based on their attributes. We demonstrate that our proposed model can identify fake news on real-world datasets with 86.5% accuracy within 30 min of its initial distribution and before it reaches 50 retweets, outperforming existing state-of-the-art benchmarks.
Keywords
- discourse analysis, Misinformation, social network
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
Cite this
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Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track . ed. / Gianmarco De Francisci Morales; Francesco Bonchi; Claudia Perlich; Natali Ruchansky; Nicolas Kourtellis; Elena Baralis. Springer Science and Business Media Deutschland GmbH, 2023. p. 174-189 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14174 LNAI).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - How Early Can We Detect?
T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023
AU - Ghosh, Shreya
AU - Mitra, Prasenjit
N1 - Funding Information: This research was funded by the Federal Ministry of Education and Research (BMBF), Germany under the project LeibnizKILabor with grant No. 01DD20003.
PY - 2023/9/17
Y1 - 2023/9/17
N2 - The rise of social media has amplified the need for automated detection of misinformation. Current methods face limitations in early detection because crucial information that they rely on is unavailable during the initial phases of information dissemination. This paper presents an innovative model for the early detection of misinformation on social media through the classification of information propagation paths and using linguistic patterns. We have developed and incorporated a causal user attribute inference model to label users as potential misinformation propagators or believers. Our model is designed for early detection of false information and includes two auxiliary tasks: predicting the extent of misinformation dissemination and clustering similar nodes (or users) based on their attributes. We demonstrate that our proposed model can identify fake news on real-world datasets with 86.5% accuracy within 30 min of its initial distribution and before it reaches 50 retweets, outperforming existing state-of-the-art benchmarks.
AB - The rise of social media has amplified the need for automated detection of misinformation. Current methods face limitations in early detection because crucial information that they rely on is unavailable during the initial phases of information dissemination. This paper presents an innovative model for the early detection of misinformation on social media through the classification of information propagation paths and using linguistic patterns. We have developed and incorporated a causal user attribute inference model to label users as potential misinformation propagators or believers. Our model is designed for early detection of false information and includes two auxiliary tasks: predicting the extent of misinformation dissemination and clustering similar nodes (or users) based on their attributes. We demonstrate that our proposed model can identify fake news on real-world datasets with 86.5% accuracy within 30 min of its initial distribution and before it reaches 50 retweets, outperforming existing state-of-the-art benchmarks.
KW - discourse analysis
KW - Misinformation
KW - social network
UR - http://www.scopus.com/inward/record.url?scp=85174437359&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-43427-3_11
DO - 10.1007/978-3-031-43427-3_11
M3 - Conference contribution
AN - SCOPUS:85174437359
SN - 9783031434266
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 174
EP - 189
BT - Machine Learning and Knowledge Discovery in Databases
A2 - De Francisci Morales, Gianmarco
A2 - Bonchi, Francesco
A2 - Perlich, Claudia
A2 - Ruchansky, Natali
A2 - Kourtellis, Nicolas
A2 - Baralis, Elena
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 18 September 2023 through 22 September 2023
ER -