Automated Identification of Social Media Bots Using Deepfake Text Detection

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works

Distinctive Image Features from Scale-Invariant Keypoints

Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589 · see citing works

SciBERT: A Pretrained Language Model for Scientific Text

Beltagy, I., Lo, K., & Cohan, A. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1371 · Cited by 3,218 · see citing works

Online Human-Bot Interactions: Detection, Estimation, and Characterization

Varol, O., Ferrara, E., Davis, C., Menczer, F., & Flammini, A. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14871 · Cited by 928 · cited 1× · see citing works

Defending Against Neural Fake News

Zellers, R., Holtzman, A., Rashkin, H., Bisk, Y., Farhadi, A., Roesner, F., & Choi, Y. (2019) · arXiv · doi:10.48550/arxiv.1905.12616 · Cited by 569 · see citing works

Deep neural networks for bot detection

Kudugunta, S., & Ferrara, E. (2018) · Information Sciences · doi:10.1016/j.ins.2018.08.019 · Cited by 506 · see citing works

GLTR: Statistical Detection and Visualization of Generated Text

Gehrmann, S., Strobelt, H., & Rush, A. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations · doi:10.18653/v1/p19-3019 · Cited by 470 · cited 1× · see citing works

The Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the Arms Race

Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2017) · Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion · doi:10.1145/3041021.3055135 · Cited by 381 · cited 2× · see citing works

Automatic Detection of Generated Text is Easiest when Humans are Fooled

Ippolito, D., Duckworth, D., Callison-Burch, C., & Eck, D. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.164 · Cited by 264 · see citing works

Automatic Detection of Generated Text is Easiest when Humans are Fooled

Ippolito, D., Duckworth, D., Callison-Burch, C., & Eck, D. (2019) · arXiv · doi:10.48550/arxiv.1911.00650 · Cited by 264 · cited 4×

DeBot: Twitter Bot Detection via Warped Correlation

Chavoshi, N., Hamooni, H., & Mueen, A. (2016) · 2016 IEEE 16th International Conference on Data Mining (ICDM) · doi:10.1109/icdm.2016.0096 · Cited by 260 · see citing works

TweepFake: about detecting deepfake tweets

Fagni, T., Falchi, F., Gambini, M., Martella, A., & Tesconi, M. (2021) · PLOS ONE · doi:10.1371/journal.pone.0251415 · Cited by 185 · see citing works

Dissecting a Social Botnet

Abokhodair, N., Yoo, D., & McDonald, D. W. (2015) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2675133.2675208 · Cited by 177 · see citing works

Random Walk Based Fake Account Detection in Online Social Networks

Jia, J., Wang, B., & Gong, N. Z. (2017) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn.2017.55 · Cited by 147 · see citing works

NeXtVLAD: An Efficient Neural Network to Aggregate Frame-Level Features for Large-Scale Video Classification

Lin, R., Xiao, J., & Jianping Fan, B. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11018-5_19 · Cited by 110 · see citing works

Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-Based Detection

Adelani, D. I., Mai, H., Fang, F., Nguyen, H. H., Yamagishi, J., & Echizen, I. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-44041-1_114 · Cited by 108 · see citing works

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

Heidari, M., & Jones, J. H. (2020) · 2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) · doi:10.1109/uemcon51285.2020.9298158 · Cited by 102 · see citing works

Detecting Social Bots on Twitter: A Literature Review

Alothali, E., Zaki, N., Mohamed, E. A., & Alashwal, H. (2018) · 2018 International Conference on Innovations in Information Technology (IIT) · doi:10.1109/innovations.2018.8605995 · Cited by 102 · see citing works

A Novel Hierarchical BERT Architecture for Sarcasm Detection

Srivastava, H., Varshney, V., Kumari, S., & Srivastava, S. (2020) · Workshop on Figurative Language Processing (FigLang) · doi:10.18653/v1/2020.figlang-1.14 · Cited by 58 · see citing works

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 57 · see citing works

Social Media Won't Free Us

Gayo-Avello, D. (2017) · IEEE Internet Computing · doi:10.1109/mic.2017.2911439 · Cited by 20 · see citing works

Augmenting Data for Sarcasm Detection with Unlabeled Conversation Context

Lee, H., Yu, Y., & Kim, G. (2020) · Workshop on Figurative Language Processing (FigLang) · doi:10.18653/v1/2020.figlang-1.2 · Cited by 20 · 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

Deep learning detection method for large language models-generated scientific content

Alhijawi, B., Jarrar, R., AbuAlRub, A., & Bader, A. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10538-y · Cited by 40 · see citing works

Risk, regulation, and governance: evaluating artificial intelligence across diverse application scenarios

Szádeczky, T., & Bederna, Z. (2025) · Security Journal · doi:10.1057/s41284-025-00495-z · Cited by 23 · 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

On pushing DeepFake Tweet Detection capabilities to the limits

Gambini, M., Fagni, T., Falchi, F., & Tesconi, M. (2022) · 14th ACM Web Science Conference 2022 · doi:10.1145/3501247.3531560 · Cited by 19 · 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

A novel approach to identify deepfake text using social media data

Devi, B. T., & Rajasekaran, R. (2025) · Social Network Analysis and Mining · doi:10.1007/s13278-025-01536-6 · Cited by 5 · see citing works

Automation of Vulnerability Information Extraction Using Transformer-Based Language Models

Chaleshtori, F. H., & Ray, I. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25460-4_37 · Cited by 7 · see citing works

Automated Code Extraction from Discussion Board Text Dataset

Saravani, S. M., Ghaffari, S., Luther, Y., Folkestad, J., & Moraes, M. C. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-31726-2_16 · Cited by 4 · see citing works

Beyond Words: Stylometric Analysis for Detecting AI Manipulation on Social Media

Ullah, U., Laudanna, S., Vinod, P., Sorbo, A. D., Visaggio, C. A., & Canfora, G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70879-4_11 · Cited by 3 · see citing works

Distinguishing Abstracts of Human-Written and ChatGPT-Generated Papers in the Field of Computer Science

Arzani, M., Vahdat‐Nejad, H., & Hossein-Pour, M. (2024) · International Conference on Computer and Knowledge Engineering · doi:10.1109/iccke65377.2024.10874567 · Cited by 1 · see citing works

DeBERTa-based framework for detecting machine-generated content on social media: a comparative study

Sweidan, S., Farouk, N., Abouhawwash, M., Askar, S., & Taha, M. (2026) · Journal Of Big Data · doi:10.1186/s40537-025-01349-6 · Cited by 1 · see citing works

Graph Attention Neural Network Distributed Model Training

Esmaeilzadeh, A., Kambar, M. E. Z. N., & Heidari, M. (2022) · IEEE World AI IoT Congress (AIIoT) · doi:10.1109/aiiot54504.2022.9817156

Analyzing the Efficacy of Bot Detection Methods on Twitter/XPreprint

Javed, D., Zaman, N., Khan, N. A., Ray, S. K., Mazroa, A. A., Ayaz, R., & Das, S. R. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4665612

Comprehensive Strategies for Identifying X(Twitter) Bots

Dhanesh, A., Jyothika, K., Jayaraj, M., Antony, N. J., & George, A. T. (2025) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem43583

Enhancing COVID-19 Tweet Analysis with Transformer Hybrid Models

Yandrapati, P. B., Kumar, A. B. P., & Karanam, S. R. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-05855-3_29

The anatomy of conspiratorial infodemic: exploring super-spreaders in COVID-19 discourse on Twitter

Shao, C., & Kwon, K. H. (2025) · Quality & Quantity · doi:10.1007/s11135-025-02495-y

BotEvo: LLM-Driven social bot detection via behavioral evolution modeling and cross-modal fusion

Zhang, Y., Qiao, K., Shi, S., Liu, J., Liu, Z., Chen, J., Li, L., & Yan, B. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00814-3

Deepfake Detection on Social Media: Leveraging Deep Learning and Fast Text Embeddings for Identifying Machine-Generated Tweets

DEVI, P. G., LAKSHMI, K. B., & MADHURI, G. (2024) · The Bioscan. · doi:10.63001/tbs.2024.v19.i02.s.i(1).pp230-233

Deepfake Text Detection Using Deep Convolutional Neural Networks with Explainable AiPreprint

Sadiq, S., & Ullah, S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5265122

A novel approach to identify deepfake text using social media data

Devi, B. T., & Rajasekaran, R. (2025) · Social Network Analysis and Mining · doi:10.1007/s13278-025-01536-6 · Cited by 5 · see citing works

Unmasking Deepfake Tweets: Leveraging Deep Learning and Word Embeddings for Accurate Classification of Machine-Generated Text on Social MediaPreprint

Sadiq, S., & Ullah, S. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4494619 · Cited by 1 · see citing works

TweetScan: An Intelligent Framework for Deepfake Tweet Detection Using CNN and FastText

Adusumalli, B., Kumar, L. N., Kavya, N., Deepak, K. P., & Indu, P. (2025) · International Journal Of Recent Advances in Engineering & Technology · doi:10.65521/intjournalrecadvengtech.v14i1.180