The DOI matches its Crossref record; 35 of the 37 references it cites were found in a registry or an index.
Using BERT to Extract Topic-Independent Sentiment Features for Social Media Bot Detection
Abstract
Millions of online posts about different topics and products are shared on popular social media platforms. One use of this content is to provide crowd-sourced information about a specific topic, event, or product. However, this use raises an important question: what percentage of the information available through these services is trustworthy? In particular, might some of this information be generated by a machine, i.e., a "bot" instead of a human? Bots can be, and often are, purposely designed to generate enough volume to skew an apparent trend or position on a topic, yet the consumer of such content cannot easily distinguish a bot post from a human post. This paper introduces a new model that uses Bidirectional Encoder Representations from Transformers (Google Bert) for sentiment classification of tweets to identify topic-independent features for the social media bot detection model. Using a Natural Language Processing approach to derive topic-independent features for the new bot detection model distinguishes this work from previous bot detection models. We achieve 94% accuracy classifying the contents of Cresci data set [1] as generated by a bot or a human, where the most accurate prior work achieved an accuracy of 92%.
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“Content posted by bots also provides essential clues (Kudugunta and Ferrara, 2018;Heidari and Jones, 2020).”
From Anatomy of an AI-powered malicious social botnet (2024) · cites this paper as Heidari and Jones, 2020
Citations
Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071
Deep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., & Potts, C. (2013) · Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d13-1170 · Cited by 6,878
Fake News Detection on Social Media: A Data Mining Perspective
Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3137597.3137600 · Cited by 3,304
BotOrNot: A System to Evaluate Social Bots
Davis, C. A., Varol, O., Ferrara, E., Flammini, A., & Menczer, F. (2016) · Proceedings of the 25th International Conference Companion on World Wide Web - WWW 16 Companion · doi:10.1145/2872518.2889302 · Cited by 909
Howard, P. N., Woolley, S., & Calo, R. (2018) · Journal of Information Technology & Politics · doi:10.1080/19331681.2018.1448735 · Cited by 398
Empirical evaluation and new design for fighting evolving Twitter spammers
Yang, C., Harkreader, R., & Gu, G. (2013) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2013.2267732 · Cited by 346
Twitter spammer detection using data stream clustering
Miller, Z., Dickinson, B., Deitrick, W., Hu, W., & Wang, A. H. (2014) · Information Sciences · doi:10.1016/j.ins.2013.11.016 · Cited by 332
Research works printed but not linked: 1
- 0, IMDB Movie Reviews Dataset
Cited by
Sorted by citations
Emotion Detection of Textual Data: An Interdisciplinary Survey
Zad, S., Heidari, M., Jones, J. H., & Uzuner, Ö. (2021) · IEEE World AI IoT Congress (AIIoT) · doi:10.1109/aiiot52608.2021.9454192 · Cited by 115
Self-citationHeidari, M., Jones, J. H., & Uzuner, Ö. (2020) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw51313.2020.00071 · Cited by 99
Self-citationA Survey on Concept-Level Sentiment Analysis Techniques of Textual Data
Zad, S., Heidari, M., Jones, J. H., & Uzuner, Ö. (2021) · IEEE World AI IoT Congress (AIIoT) · doi:10.1109/aiiot52608.2021.9454169 · Cited by 92
Self-citationUnsupervised Social Bot Detection via Structural Information Theory
Peng, H. M., Zhang, J., Huang, X., Hao, Z., Li, A., Yu, Z., & Yu, P. S. (2024) · ACM Transactions on Information Systems · doi:10.1145/3660522 · Cited by 82
Anatomy of an AI-powered malicious social botnet
Yang, K., & Menczer, F. (2024) · Journal of Quantitative Description: Digital Media · doi:10.51685/jqd.2024.icwsm.7 · Cited by 71
Metadata
- Authors
- Heidari, M., & Jones, J. H.
- Affiliations
- George Mason University (US)
- ORCID
- Jones, James H
- Venue
- 2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)
- Publisher
- IEEE
- Published
- 2020-10-28
- Pages
- 0542-0547
- OpenAlex
- W3116492894
- Cited by
- 102
- References
- 37
- Citations by year
- 2026: 4 · 2025: 13 · 2024: 12 · 2023: 18 · 2022: 21 · 2021: 27 · 2020: 7
- FWCI
- 13.84
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- BERT, sentiment classification, tweets, machine-generated content
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