How Early Can We Detect? Detecting Misinformation on Social Media Using User Profiling and Network Characteristics

Shreya Ghosh; Prasenjit Mitra

doi:10.1007/978-3-031-43427-3_11

How Early Can We Detect? Detecting Misinformation on Social Media Using User Profiling and Network Characteristics

Shreya Ghosh, Prasenjit Mitra

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English (US)
Title of host publication	Machine Learning and Knowledge Discovery in Databases
Subtitle of host publication	Applied Data Science and Demo Track - European Conference, ECML PKDD 2023, Proceedings
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 (Print)	9783031434266
DOIs	https://doi.org/10.1007/978-3-031-43427-3_11
State	Published - 2023
Event	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023 - Turin, Italy Duration: Sep 18 2023 → Sep 22 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

Conference

Conference	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023
Country/Territory	Italy
City	Turin
Period	9/18/23 → 9/22/23

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-43427-3_11

Cite this

Ghosh, S., & Mitra, P. (2023). How Early Can We Detect? Detecting Misinformation on Social Media Using User Profiling and Network Characteristics. In G. De Francisci Morales, F. Bonchi, C. Perlich, N. Ruchansky, N. Kourtellis, & E. Baralis (Eds.), Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track - European Conference, ECML PKDD 2023, Proceedings (pp. 174-189). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14174 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-43427-3_11

Ghosh, Shreya ; Mitra, Prasenjit. / How Early Can We Detect? Detecting Misinformation on Social Media Using User Profiling and Network Characteristics. Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track - European Conference, ECML PKDD 2023, Proceedings. editor / Gianmarco De Francisci Morales ; Francesco Bonchi ; Claudia Perlich ; Natali Ruchansky ; Nicolas Kourtellis ; Elena Baralis. Springer Science and Business Media Deutschland GmbH, 2023. pp. 174-189 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

author = "Shreya Ghosh and Prasenjit Mitra",

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Ghosh, S & Mitra, P 2023, How Early Can We Detect? Detecting Misinformation on Social Media Using User Profiling and Network Characteristics. in G De Francisci Morales, F Bonchi, C Perlich, N Ruchansky, N Kourtellis & E Baralis (eds), Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track - European Conference, ECML PKDD 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14174 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 174-189, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023, Turin, Italy, 9/18/23. https://doi.org/10.1007/978-3-031-43427-3_11

How Early Can We Detect? Detecting Misinformation on Social Media Using User Profiling and Network Characteristics. / Ghosh, Shreya; Mitra, Prasenjit.
Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track - European Conference, ECML PKDD 2023, Proceedings. 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

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Ghosh S, Mitra P. How Early Can We Detect? Detecting Misinformation on Social Media Using User Profiling and Network Characteristics. In De Francisci Morales G, Bonchi F, Perlich C, Ruchansky N, Kourtellis N, Baralis E, editors, Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track - European Conference, ECML PKDD 2023, Proceedings. 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)). doi: 10.1007/978-3-031-43427-3_11