mlsanityData to 14 Oct 2026

Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions

arXiv 2110.13980 ↗First posted 26 Oct 2021cs.CLcited by 4

Not on the map. Why?

Abstract (arXiv)

The growing use of social media has led to the development of several Machine Learning (ML) and Natural Language Processing(NLP) tools to process the unprecedented amount of social media content to make actionable decisions. However, these MLand NLP algorithms have been widely shown to be vulnerable to adversarial attacks. These vulnerabilities allow adversaries to launch a diversified set of adversarial attacks on these algorithms in different applications of social media text processing. In this paper, we provide a comprehensive review of the main approaches for adversarial attacks and defenses in the context of social media applications with a particular focus on key challenges and future research directions. In detail, we cover literature on six key applications, namely (i) rumors detection, (ii) satires detection, (iii) clickbait & spams identification, (iv) hate speech detection, (v)misinformation detection, and (vi) sentiment analysis. We then highlight the concurrent and anticipated future research questions and provide recommendations and directions for future work.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 86 of its 183 references that are in the map. First posted on arXiv 26 Oct 2021.

Builds on

86 of 183 references.

References · 86

  1. 2017Attention Is All You Needcited by 60,361
  2. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  3. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  4. 2013Intriguing properties of neural networkscited by 5,155
  5. 2015Character-level Convolutional Networks for Text Classificationcited by 2,330
  6. 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,318
  7. 2018Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examplescited by 1,268
  8. 2017Ensemble Adversarial Training: Attacks and Defensescited by 840
  9. 2014Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Imagescited by 776
  10. 2017Automated Hate Speech Detection and the Problem of Offensive Languagecited by 736
  11. 2019Universal Adversarial Triggers for Attacking and Analyzing NLPcited by 722
  12. 2017Evasion Attacks against Machine Learning at Test Timecited by 689
38 more
  1. 2015Distillation as a Defense to Adversarial Perturbations against Deep Neural Networkscited by 678
  2. 2019Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailmentcited by 671
  3. 2017Adversarial Examples for Evaluating Reading Comprehension Systemscited by 571
  4. 2019Defending Against Neural Fake Newscited by 559
  5. 2017Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networkscited by 486
  6. 2018Generating Natural Language Adversarial Examplescited by 424
  7. 2018TextBugger: Generating Adversarial Text Against Real-world Applicationscited by 381
  8. 2018Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifierscited by 345
  9. 2017Fake News Detection on Social Media: A Data Mining Perspectivecited by 336
  10. 2017Synthetic and Natural Noise Both Break Neural Machine Translationcited by 306
  11. 2018Adversarial Example Generation with Syntactically Controlled Paraphrase Networkscited by 286
  12. 2018FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Mediacited by 285
  13. 2016Adversarial Training Methods for Semi-Supervised Text Classificationcited by 280
  14. 2018Analysis Methods in Neural Language Processing: A Surveycited by 277
  15. 2019Representation Learning for Dynamic Graphs: A Surveycited by 265
  16. 2019Adversarial Attacks and Defenses in Images, Graphs and Text: A Reviewcited by 237
  17. 2016Understanding Neural Networks through Representation Erasurecited by 224
  18. 2019SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)cited by 207
  19. 2020GNNGuard: Defending Graph Neural Networks against Adversarial Attackscited by 195
  20. 2019Combating Adversarial Misspellings with Robust Word Recognitioncited by 163
  21. 2020SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)cited by 138
  22. 2019Robust Neural Machine Translation with Doubly Adversarial Inputscited by 106
  23. 2017Detection and Resolution of Rumours in Social Media: A Surveycited by 104
  24. 2017Deceiving Google's Perspective API Built for Detecting Toxic Commentscited by 102
  25. 2019Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systemscited by 101
  26. 2019A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Mediacited by 96
  27. 2018On Adversarial Examples for Character-Level Neural Machine Translationcited by 85
  28. 2021Node-Level Membership Inference Attacks Against Graph Neural Networkscited by 84
  29. 2018GenAttack: Practical Black-box Attacks with Gradient-Free Optimizationcited by 82
  30. 2020Quantifying Privacy Leakage in Graph Embeddingcited by 82
  31. 2020Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Societycited by 72
  32. 2018Interpretable Adversarial Perturbation in Input Embedding Space for Textcited by 67
  33. 2019The Limitations of Adversarial Training and the Blind-Spot Attackcited by 63
  34. 2019The Limitations of Stylometry for Detecting Machine-Generated Fake Newscited by 63
  35. 2018Adversarial Attacks Against Medical Deep Learning Systemscited by 62
  36. 2020Demoting Racial Bias in Hate Speech Detectioncited by 62
  37. 2019Learning to Discriminate Perturbations for Blocking Adversarial Attacks in Text Classificationcited by 57
  38. 2018All You Need is "Love": Evading Hate-speech Detectioncited by 56

50 most cited of 86 All 86 (JSON)

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 3.

4 papers.

  1. 2024GenAI Detection Tools, Adversarial Techniques and Implications for Inclusivity in Higher Educationcited by 14
  2. 2024Improving Adversarial Data Collection by Supporting Annotators: Lessons from GAHD, a German Hate Speech Datasetcited by 13
  3. 2023Mutation-Based Adversarial Attacks on Neural Text Detectorscited by 9
  4. 2024Deep Learning Approaches for Detecting Adversarial Cyberbullying and Hate Speech in Social Networkscited by 2