Using BERT to Extract Topic-Independent Sentiment Features for Social Media Bot Detection

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

Algorithms, bots, and political communication in the US 2016 election: The challenge of automated political communication for election law and administration

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

Using sentiment to detect bots on Twitter: Are humans more opinionated than bots?

Dickerson, J. P., Kagan, V., & Subrahmanian, V. S. (2014) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2014.6921650 · Cited by 288

The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race

Cresci, S., Pietro, R. D., Petrocchi, M., Spognardi, A., & Tesconi, M. (2017) · Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) · doi:10.48550/arxiv.1701.03017 · Cited by 237

A generic statistical approach for spam detection in Online Social Networks

Ahmed, F., & Abulaish, M. (2013) · Computer Communications · doi:10.1016/j.comcom.2013.04.004 · Cited by 199

DNA-Inspired Online Behavioral Modeling and Its Application to Spambot Detection

Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2016) · IEEE Intelligent Systems · doi:10.1109/mis.2016.29 · Cited by 194 · cited 1×

Political Bots and the Manipulation of Public Opinion in VenezuelaPreprint

Forelle, M., Howard, P. N., Monroy‐Hernández, A., & Savage, S. (2015) · SSRN Electronic Journal · doi:10.2139/ssrn.2635800 · Cited by 116

BotOrNot: A System to Evaluate Social BotsPreprint

Davis, C. A., Varol, O., Ferrara, E., Flammini, A., & Menczer, F. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.00975 · Cited by 105

Deep Contextualized Word Embedding for Text-based Online User Profiling to Detect Social Bots on Twitter

Heidari, M., Jones, J. H., & Uzuner, Ö. (2020) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw51313.2020.00071 · Cited by 99

Social Network Analysis with Content and Graphs

Campbell, W. M., Dagli, C. K., & Weinstein, C. J. (2013) · Cited by 68

Using Transfer Learning Approach to Implement Convolutional Neural Network model to Recommend Airline Tickets by Using Online Reviews

Heidari, M., & Rafatirad, S. (2020) · International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP) · doi:10.1109/smap49528.2020.9248443 · Cited by 67

An Unsupervised Approach to Detect Spam Campaigns that Use Botnets on TwitterPreprint

Chen, Z., & Subramanian, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.05232 · Cited by 53

Polarization in social media assists influencers to become more influential: analysis and two inoculation strategies

Garibay, I. I., Mantzaris, A. V., Rajabi, A., & Taylor, C. E. (2019) · Scientific Reports · doi:10.1038/s41598-019-55178-8 · Cited by 51

Deep Reinforcement Learning-based Text Anonymization against Private-Attribute Inference

Mosallanezhad, A., Beigi, G., & Liu, H. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1240 · Cited by 46

A Comprehensive Survey on Machine Learning Techniques and User Authentication Approaches for Credit Card Fraud DetectionPreprint

Yousefi, N., Alaghband, M., & Garibay, I. I. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.02629 · Cited by 34

Mental Workload Classification From Spatial Representation of FNIRS Recordings Using Convolutional Neural Networks

Saadati, M., Nelson, J. K., & Ayaz, H. (2019) · IEEE International Workshop on Machine Learning for Signal Processing (MLSP) · doi:10.1109/mlsp.2019.8918861 · Cited by 33

Application of the Benford’s law to Social bots and Information Operations activities

Madahali, L., & Hall, M. (2020) · International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA) · doi:10.1109/cybersa49311.2020.9139709 · Cited by 26

Scraping Social Media Photos Posted in Kenya and Elsewhere to Detect and Analyze Food Types

Jalal, M., Wang, K., Jefferson, S., Zheng, Y., Nsoesie, E. O., & Betke, M. (2019) · International Workshop on Multimedia Assisted Dietary Management (MADiMa) · doi:10.1145/3347448.3357170 · Cited by 26

TOT-Net: An Endeavor Toward Optimizing Ternary Neural Networks

Nazari, N., Loni, M., Salehi, M. E., Daneshtalab, M., & Sjödin, M. (2019) · Euromicro Conference on Digital System Design (DSD) · doi:10.1109/dsd.2019.00052 · Cited by 24

User Behavior Modelling for Fake Information Mitigation on Social Web

Rajabi, Z., Shehu, A., & Purohit, H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-21741-9_24 · Cited by 21

Code-Bridged Classifier (CBC): A Low or Negative Overhead Defense for Making a CNN Classifier Robust Against Adversarial Attacks

Behnia, F., Mirzaeian, A., Sabokrou, M., Manoj, S., Mohsenin, T., Khasawneh, K. N., Zhao, L., Homayoun, H., & Sasan, A. (2020) · International Symposium on Quality Electronic Design (ISQED) · doi:10.1109/isqed48828.2020.9136987 · Cited by 17

Probabilistic Model of Narratives Over Topical Trends in Social Media

Oghaz, T. A., Mutlu, E. Ç., Jasser, J., Yousefi, N., & Garibay, I. I. (2020) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/3372923.3404790 · Cited by 14

Personalized recommender system based on social relations

Ebrahimi, F., & Golpayegani, A. H. (2016) · Iranian Conference on Electrical Engineering · doi:10.1109/iraniancee.2016.7585521 · Cited by 12

Curtailing Fake News Propagation with PsychographicsPreprint

Safadi, H., Li, W., Rahmati, P., & Soleymani, S. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3558236 · Cited by 10

On Countering Disinformation with Caution: Effective Inoculation Strategies and Others that Backfire into Community Hyper-Polarization

Rajabi, A., Gunaratne, C., Mantzaris, A. V., & Garibay, I. I. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61255-9_13 · Cited by 8

Maximal Sequence Mining approach for topic detection from microblog streams

Jafariakinabad, F., & Hua, K. A. (2016) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2016.7849940 · Cited by 5

Modeling Disinformation and the Effort to Counter It: A Cautionary Tale of When the Treatment Can Be Worse Than the Disease

Rajabi, A., Gunaratne, C., Mantzaris, A. V., & Garibay, I. I. (2020) · Adaptive Agents and Multi-Agents Systems · doi:10.65109/yjde7402 · Cited by 4

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

Deep Contextualized Word Embedding for Text-based Online User Profiling to Detect Social Bots on Twitter

Heidari, M., Jones, J. H., & Uzuner, Ö. (2020) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw51313.2020.00071 · Cited by 99

A 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

Unsupervised 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

Bert Model for Social Media Bot Detection

Heidari, M., & Jones, J. H. (2022) · doi:10.13021/mars/3048 · Cited by 6

Are You Human? Detecting Bots on Twitter Using BERT

Dukić, D., Keča, D., & Stipić, D. (2020) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa49011.2020.00089 · Cited by 58

BotBuster: Multi-Platform Bot Detection Using a Mixture of Experts

Ng, L. H. X., & Carley, K. M. (2023) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v17i1.22179 · Cited by 43

Social Media Bot Detection Using Machine Learning Approach

Bhongale, P., Sali, O., & Mehetre, S. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-28183-9_14

CLASSIFYING TWITTER USER AS A BOT OR NOT AND COMPARING DIFFERENT CLASSIFICATION ALGORITHMS.

Haidermota, M. (2018) · International Journal of Advanced Research in Computer Science · doi:10.26483/ijarcs.v9i3.5949 · Cited by 7