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. (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 48,293 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 337 · see citing works

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 · see citing works

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 278 · see citing works

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 · see citing works

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 · see citing works

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× · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Social Network Analysis with Content and Graphs

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

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 66 · see citing works

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 · see citing works

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 45 · see citing works

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 44 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 24 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 12 · see citing works

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 · see citing works

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 9 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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

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 109 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Sentiment Analysis of Twitter Data Using NLP Models: A Comprehensive Review

Albladi, A., Islam, M., & Seals, C. (2025) · IEEE Access · doi:10.1109/access.2025.3541494 · Cited by 89 · see citing works

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 66 · see citing works

A Comparison of Natural Language Processing and Machine Learning Methods for Phishing Email Detection

Bountakas, P., Koutroumpouchos, K., & Xenakis, C. (2021) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1145/3465481.3469205 · Cited by 63 · see citing works

Identifying learners’ topical interests from social media content to enrich their course preferences in MOOCs using topic modeling and NLP techniques

Zankadi, H., Idrissi, A., Daoudi, N., & Hilal, I. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11373-1 · Cited by 62 · see citing works

Social media bot detection with deep learning methods: a systematic review

Hayawi, K., Saha, S., Masud, M. M., Mathew, S. S., & Kaosar, M. G. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08352-z · Cited by 61 · see citing works

Review of Graph Neural Network in Text Classification

Malekzadeh, M., Hajibabaee, P., Heidari, M., Zad, S., Uzuner, Ö., & Jones, J. H. (2021) · IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) · doi:10.1109/uemcon53757.2021.9666633 · Cited by 60 · see citing works

BERT Model for Fake News Detection Based on Social Bot Activities in the COVID-19 Pandemic

Heidari, M., Zad, S., Hajibabaee, P., Malekzadeh, M., HekmatiAthar, S., Uzuner, Ö., & Jones, J. H. (2021) · IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) · doi:10.1109/uemcon53757.2021.9666618 · Cited by 57 · see citing works

Offensive Language Detection on Social Media Based on Text Classification

Hajibabaee, P., Malekzadeh, M., Ahmadi, M., Heidari, M., Esmaeilzadeh, A., Abdolazimi, R., & Jones, J. H. (2022) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc54503.2022.9720804 · Cited by 56 · see citing works

A Survey on Mobile Malware Detection Methods using Machine Learning

Kambar, M. E. Z. N., Esmaeilzadeh, A., Kim, Y., & Taghva, K. (2022) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc54503.2022.9720753 · Cited by 53 · see citing works

Review on Learning and Extracting Graph Features for Link Prediction

Mutlu, E. C., Oghaz, T., Rajabi, A., & Garibay, I. (2020) · Machine Learning and Knowledge Extraction · doi:10.3390/make2040036 · Cited by 51 · see citing works

Recent trends in supply chain management of business-to-business firms: a review and future research directions

Shrivastava, S. (2023) · Journal of Business and Industrial Marketing · doi:10.1108/jbim-02-2023-0122 · Cited by 45 · see citing works

Semantic Convolutional Neural Network model for Safe Business Investment by Using BERT

Heidari, M., & Rafatirad, S. (2020) · International Conference on Social Networks Analysis, Management and Security (SNAMS) · doi:10.1109/snams52053.2020.9336575 · Cited by 44 · see citing works

An Empirical Study of the GraphSAGE and Word2vec Algorithms for Graph Multiclass Classification

Hajibabaee, P., Malekzadeh, M., Heidari, M., Zad, S., Uzuner, Ö., & Jones, J. H. (2021) · IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) · doi:10.1109/iemcon53756.2021.9623238 · Cited by 37 · see citing works

Bidirectional Transformer based on online Text-based information to Implement Convolutional Neural Network Model For Secure Business Investment

Heidari, M., & Rafatirad, S. (2020) · IEEE International Symposium on Technology and Society (ISTAS) · doi:10.1109/istas50296.2020.9462170 · Cited by 33 · see citing works

A Survey of Deep Learning Methods on Semantic Similarity and Sentence Modeling

Zad, S., Heidari, M., Hajibabaee, P., & Malekzadeh, M. (2021) · IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) · doi:10.1109/iemcon53756.2021.9623078 · Cited by 32 · see citing works

Social Engineering Attacks: Recent Advances and Challenges

Mashtalyar, N., Ntaganzwa, U. N., Santos, T., Hakak, S., & Ray, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77392-2_27 · Cited by 29 · see citing works

Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection

Hays, C., Schutzman, Z., Raghavan, M., Walk, E., & Zimmer, P. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583214 · Cited by 29 · see citing works

A Hybrid Deep Learning Architecture for Social Media Bots Detection Based on BiGRU-LSTM and GloVe Word Embedding

Ellaky, Z., Benabbou, F., Matrane, Y., & Qaqa, S. (2024) · IEEE Access · doi:10.1109/access.2024.3430859 · Cited by 26 · see citing works

Fine-Tuned Understanding: Enhancing Social Bot Detection With Transformer-Based Classification

Sallah, A., Alaoui, E. A. A., Agoujil, S., Wani, M. A., Hammad, M. A., Maleh, Y., & El‐Latif, A. A. A. (2024) · IEEE Access · doi:10.1109/access.2024.3440657 · Cited by 27 · see citing works

Systematic Review of Fake News, Propaganda, and Disinformation: Examining Authors, Content, and Social Impact Through Machine Learning

Plikynas, D., Rizgelienė, I., & Korvel, G. (2025) · IEEE Access · doi:10.1109/access.2025.3530688 · Cited by 28 · see citing works

Efficient Large Scale NLP Feature Engineering with Apache Spark

Esmaeilzadeh, A., Heidari, M., Abdolazimi, R., Hajibabaee, P., & Malekzadeh, M. (2022) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc54503.2022.9720765 · Cited by 25 · see citing works

Building an intrusion detection system on UNSW ‐ NB15 : Reducing the margin of error to deal with data overlap and imbalance

Zoghi, Z., & Serpen, G. (2024) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.8242 · Cited by 24 · see citing works

Review of Deep Learning Methods for Automated Sleep Staging

Malekzadeh, M., Hajibabaee, P., Heidari, M., & Berlin, B. (2022) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc54503.2022.9720875 · Cited by 23 · see citing works

Deep Learning for Identification of Alcohol-Related Content on Social Media (Reddit and Twitter): Exploratory Analysis of Alcohol-Related Outcomes

Ricard, B. J., & Hassanpour, S. (2021) · Journal of Medical Internet Research · doi:10.2196/27314 · Cited by 19 · see citing works

Classifying Mobile Applications Using Word Embeddings

Ebrahimi, F., Tushev, M., & Mahmoud, A. (2021) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3474827 · Cited by 15 · see citing works

Automated Identification of Social Media Bots Using Deepfake Text Detection

Saravani, S. M., Ray, I., & Ray, I. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-92571-0_7 · Cited by 18 · see citing works

A Survey on Deep Learning Techniques for Joint Named Entities and Relation Extraction

Kambar, M. E. Z. N., Esmaeilzadeh, A., & Heidari, M. (2022) · IEEE World AI IoT Congress (AIIoT) · doi:10.1109/aiiot54504.2022.9817231 · Cited by 17 · see citing works

MapReduce Preprocess of Big Graphs for Rapid Connected Components Detection

Abdolazimi, R., Heidari, M., Esmaeilzadeh, A., & Naderi, H. (2022) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc54503.2022.9720798 · Cited by 16 · see citing works

Characterizing the Online Discourse in Twitter: Users’ Reaction to Misinformation around COVID-19 in Twitter

Kalantari, N., Liao, D., & Motti, V. G. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671740 · Cited by 14 · see citing works

A Self-Supervised Representation Learning of Sentence Structure for Authorship Attribution

Jafariakinabad, F., & Hua, K. A. (2022) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3491203 · Cited by 14 · see citing works

Building Wikipedia N-grams with Apache Spark

Esmaeilzadeh, A., Cacho, J. R. F., Taghva, K., Kambar, M. E. Z. N., & Hajiali, M. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-10464-0_45 · Cited by 13 · see citing works

Bot or Human? Detection of DeepFake Text with Semantic, Emoji, Sentiment and Linguistic Features

Chong, A. T. Y., Chua, H. N., Jasser, M. B., & Wong, R. T. K. (2023) · IEEE International Conference on System Engineering and Technology (ICSET) · doi:10.1109/icset59111.2023.10295100 · Cited by 13 · see citing works

Dissecting a social bot powered by generative AI: anatomy, new trends and challenges

López-Joya, S., Diaz-Garcia, J. A., Ruiz, M. D., & Martín-Bautista, M. J. (2025) · Social Network Analysis and Mining · doi:10.1007/s13278-025-01410-5 · Cited by 13 · see citing works

Aspect sentiment mining of short bullet screen comments from online TV series

Liu, J., Zhou, Z., Gao, M. Y., Tang, J., & Fan, W. (2023) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24800 · Cited by 12 · see citing works

ASRL: Adaptive Swarm Reinforcement Learning for Enhanced OSN Intrusion Detection

Boahen, E. K., Sosu, R. N. A., Ocansey, S. K., Xu, Q., & Wang, C. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3488506 · Cited by 12 · see citing works

Do Bots Have Moral Judgement? The Difference Between Bots and Humans in Moral Rhetoric

Mutlu, E. Ç., Oghaz, T. A., Tütüncüler, E., & Garibay, I. I. (2020) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam49781.2020.9381386 · Cited by 11 · see citing works

Blockchain technology in circular economy: Unpacking the potential issues and critical echoes through data triangulation and natural language processing

Shrivastav, S. K., & Bag, S. (2024) · Business Strategy and the Environment · doi:10.1002/bse.3763 · Cited by 10 · see citing works

Application of Recurrent Convolutional Neural Networks for Mental Workload Assessment Using Functional Near-Infrared Spectroscopy

Saadati, M., Nelson, J. K., Curtin, A., Wang, L., & Ayaz, H. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-80285-1_13 · Cited by 10 · see citing works

Bot Detection in Twitter: An Overview

López-Joya, S., Diaz-Garcia, J. A., Ruiz, M. D., & Martín-Bautista, M. J. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-42935-4_11 · Cited by 10 · see citing works

A comprehensive review on sentiment analysis of social/web media big data for stock market prediction

Shah, P., Desai, K., Hada, M., Parikh, P., Champaneria, M., Panchal, D., Tanna, M., & Shah, M. (2024) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-023-02214-6 · Cited by 10 · see citing works

Evolution of Malicious Social Bot Detection: From Individual Profiling to Group Analysis and Beyond

Liu, Y., Xu, S., Li, Y., & Yu, S. (2025) · Journal of Social Computing · doi:10.23919/jsc.2025.0017 · Cited by 9 · see citing works

Main Contributing Factors and the Heuristic Approach for Assessing Risk at Mass Gatherings

Torkjazi, M., Farrokhvar, L., & Kamali, B. (2022) · Operations Research Forum · doi:10.1007/s43069-022-00133-z · Cited by 9 · see citing works

Twitter bots, democratic deliberation and social accountability: the case of #OccupyWallStreet

Neu, D., & Saxton, G. D. (2023) · Accounting Auditing & Accountability Journal · doi:10.1108/aaaj-01-2023-6234 · Cited by 8 · see citing works

Hybrid-MELAu: A Hybrid Mixing Engineered Linguistic Features Framework Based on Autoencoder for Social Bot Detection.

Hamida, Z. F., Refouf, A., Drif, A., & Giordano, S. (2022) · Informatica · doi:10.31449/inf.v46i6.4081 · Cited by 7 · see citing works

Exploring the disparity of influence between users in the discussion of Brexit on Twitter

Rajabi, A., Mantzaris, A. V., Atwal, K. S., & Garibay, I. (2021) · Journal of Computational Social Science · doi:10.1007/s42001-021-00112-0

A Comparative Analysis of the Impact of Data Preprocessing and Fine-Tuning Methods on Bert's Performance in Text Classification

Purnomo, R. D., Fatyanosa, T. N., & Data, M. (2025) · doi:10.1109/icnlp65360.2025.11108690 · Cited by 6 · see citing works

An adept approach to ascertain and elude probable social bots attacks on twitter and twitch employing machine learning approach

Sethurajan, M. R., & Natarajan, K. P. (2023) · MethodsX · doi:10.1016/j.mex.2023.102430 · Cited by 6 · see citing works

Classifying the Ideological Orientation of User-Submitted Texts in Social Media

Ravi, K., Vela, A., & Ewetz, R. (2022) · International Conference on Machine Learning and Applications · doi:10.1109/icmla55696.2022.00066 · Cited by 6 · see citing works

CB-MTE: Social Bot Detection via Multi-Source Heterogeneous Feature Fusion

Cheng, M., Xiao, Y., Huang, T., Lei, C., & Zhang, C. (2025) · Sensors · doi:10.3390/s25113549 · Cited by 5 · see citing works

Harmful Tweet Detection using Supervised and Semi-supervised Learning Techniques

Das, T. R., Mahmud, T., Hanip, A., & Hossain, M. S. (2025) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict64420.2025.11005317 · Cited by 5 · see citing works

Decoding Linguistic Ambiguity in Times of Emergency based on Twitter Disaster Datasets

Hembree, A., Beggs, A. D., Marshall, T., & Ceesay, E. N. (2021) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc51732.2021.9375916 · Cited by 5 · see citing works

Security of Social Networks: Lessons Learned on Twitter Bot Analysis in the Literature

Alipour, S. A., Orji, R., & Zincir-Heywood, N. (2022) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1145/3538969.3544450 · Cited by 5 · see citing works

Decoding Social Sentiment in DAO: A Comparative Analysis of Blockchain Governance CommunitiesPreprint

Quan, Y., Wu, X., Deng, W., & Zhang, L. (2023) · doi:10.31219/osf.io/bq6tu · Cited by 5 · see citing works

Utilising Twitter Metadata for Hate Classification

Warke, O., Jose, J. M., & Breitsohl, J. (2023) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-28238-6_59 · Cited by 4 · see citing works

A Review on Sentiment and Emotion Analysis for Computational Literary Studies

Makhdom, N., Verma, H. N., & Yadav, A. K. (2024) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit241029 · Cited by 4 · see citing works

Research on sentiment analysis methods for text-oriented data

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Suicide Rate and Factors Analysis: Pre and Post COVID Pandemic Data Analysis

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HIN-RNN: A Graph Representation Learning Neural Network for Fraudster Group Detection With No Handcrafted Features

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Statistical and Transfer Learning Model to Analyze Endurance Performance with Aging

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Accident analysis and severity prediction of road accidents in United States using machine learning algorithms

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CS/NLP at SemEval-2022 Task 4: Effective Data Augmentation Methods for Patronizing Language Detection and Multi-label Classification with RoBERTa and GPT3

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A Study of Different Approaches for Malware Detection in Smartphones

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Unifying Lexical, Syntactic, and Structural Representations of Written Language for Authorship Attribution

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Transfer Learning Model for Disrupting Misinformation During a COVID-19 Pandemic

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A machine learning based empathy mapping framework for enhancing user experience through app review analysis

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A Visual Interpretation-Based Self-improved Classification System Using Virtual Adversarial Training

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Abnormal user identification in online social networks based on user behavior

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CAT-GPT-4: Context-Aware Phishing Detection with Explainability

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Detecting Camouflaged Social Bots Through Multi-level Aggregation and Information Encoding

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BSG4Bot:Efficient Bot Detection Based on Biased Heterogeneous Subgraphs

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Conditional Classification: A Solution for Computational Energy Reduction

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Economic Inclusion in the United States: Predictive Analysis of COVID-19 pandemic on County Rates of Unbanked Households

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Machine Learning models for Customer Relationship Analysis to Improve Satisfaction Rate in Banking

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CNA-TCC: Campaign Network Attribute Based Thematic Campaign Classification

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A Comprehensive Overview of Consumer Conflicts on Social Media

Warke, O. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-28241-6_55 · Cited by 1 · see citing works

Algorithms and techniques for bot detection in social networks

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Toward Privacy and Utility Preserving Image RepresentationPreprint

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Social Networks Analysis to Retrieve Critical Comments on Online PlatformsPreprint

Bhandari, S., & Raju, R. P. K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.10495

Machine Learning Model to Detect Emergency in the Global Pandemic

Raju, R. P. K. (2021) · doi:10.13021/mars/3039

Information Retrieval Model for Social Media Applications

Bhandari, S. (2021) · doi:10.13021/mars/3092

Bimodal Key Data and Semantic Analysis

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Machine learning Models to Predict COVID-19 Cases

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Graph Attention Neural Network Distributed Model Training

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Deep generative models for vehicle speed trajectories

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Confidence Preservation Property in Knowledge Distillation Abstractions

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Analyzing the Efficacy of Bot Detection Methods on Twitter/XPreprint

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Does Social Bot's Dominant Emotion Differ from Human During Human-Bot Interaction in the Context of Covid-19?Preprint

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A New Novel Approach for Sentiments Analysis using Contextual Mining and Supervised Learning

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Identifying Unconvincing User on Social Media with Limited Features

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Computational Linguistic Approach for Holistic User Behaviors Modeling Through Opinionated Data of Virtual Communities

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Social Bot Detection via Heterogeneous Graph Learning and Sample Balancing Strategies

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LLM-Based Abstractive Summarization of PubMed Articles

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Sentiment, Bias, and Virality: Investigating Content Dynamics on Reddit

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Advanced Feature Engineering for Twitter Bot Detection: Utilizing Metadata, NLP, and Transformers

Chen, H., Li, Z., Shao, Y., & Makanju, A. (2025) · Conference proceedings - Canadian Conference on Electrical and Computer Engineering · doi:10.1109/ccece64018.2025.11364472

An integration of semantic and visual features for scale recognition of vector geographic data

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Bert Model for Social Media Bot Detection

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Are You Human? Detecting Bots on Twitter Using BERT

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

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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.

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