Enabling Contextual Soft Moderation on Social Media through Contrastive Textual Deviation
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Abstract (arXiv)
Automated soft moderation systems are unable to ascertain if a post supports or refutes a false claim, resulting in a large number of contextual false positives. This limits their effectiveness, for example undermining trust in health experts by adding warnings to their posts or resorting to vague warnings instead of granular fact-checks, which result in desensitizing users. In this paper, we propose to incorporate stance detection into existing automated soft-moderation pipelines, with the goal of ruling out contextual false positives and providing more precise recommendations for social media content that should receive warnings. We develop a textual deviation task called Contrastive Textual Deviation (CTD) and show that it outperforms existing stance detection approaches when applied to soft moderation.We then integrate CTD into the stateof-the-art system for automated soft moderation Lambretta, showing that our approach can reduce contextual false positives from 20% to 2.1%, providing another important building block towards deploying reliable automated soft moderation tools on social media.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 56 of its 87 references that are in the map. First posted on arXiv 30 Jul 2024.
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References · 56
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2023GPT-4 Technical Reportcited by 18,132
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
- 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,854
- 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
- 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
- 2022Scaling Instruction-Finetuned Language Modelscited by 2,997
- 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,833
- 2022Emergent Abilities of Large Language Modelscited by 2,483
- 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,968
38 more
- 2022Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?cited by 1,413
- 2019SciBERT: A Pretrained Language Model for Scientific Textcited by 1,404
- 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,362
- 2022Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learningcited by 886
- 2019Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inferencecited by 864
- 2020A Primer in BERTology: What we know about how BERT workscited by 807
- 2020Fact or Fiction: Verifying Scientific Claimscited by 546
- 2021Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Modelscited by 462
- 2019Multi-Task Deep Neural Networks for Natural Language Understandingcited by 349
- 2023LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Modelscited by 317
- 2020The Pushshift Reddit Datasetcited by 289
- 2020CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claimscited by 195
- 2020Intermediate-Task Transfer Learning with Pretrained Models for Natural Language Understanding: When and Why Does It Work?cited by 127
- 2017Detection and Resolution of Rumours in Social Media: A Surveycited by 103
- 2021COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemiccited by 100
- 2020Stance Detection on Social Media: State of the Art and Trendscited by 93
- 2019Hierarchical Clustering with Hard-batch Triplet Loss for Person Re-identificationcited by 83
- 2021A Survey on Stance Detection for Mis- and Disinformation Identificationcited by 75
- 2019Seeing Things from a Different Angle: Discovering Diverse Perspectives about Claimscited by 73
- 2023PolyLM: An Open Source Polyglot Large Language Modelcited by 64
- 2015Analysing How People Orient to and Spread Rumours in Social Media by Looking at Conversational Threadscited by 62
- 2018UKP-Athene: Multi-Sentence Textual Entailment for Claim Verificationcited by 60
- 2021Fine-Tuning Large Neural Language Models for Biomedical Natural Language Processingcited by 57
- 2019Zero-shot Text Classification With Generative Language Modelscited by 55
- 2022POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detectioncited by 50
- 2020Detecting Stance in Media on Global Warmingcited by 48
- 2018Integrating Stance Detection and Fact Checking in a Unified Corpuscited by 41
- 2020English Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer Toocited by 39
- 2021Adversarial Learning for Zero-Shot Stance Detection on Social Mediacited by 36
- 2020Stance Detection Benchmark: How Robust Is Your Stance Detection?cited by 33
- 2016Hoaxy: A Platform for Tracking Online Misinformationcited by 17
- 2019FAKTA: An Automatic End-to-End Fact Checking Systemcited by 13
- 2020A Stance Data Set on Polarized Conversations on Twitter about the Efficacy of Hydroxychloroquine as a Treatment for COVID-19cited by 12
- 2021"I Won the Election!": An Empirical Analysis of Soft Moderation Interventions on Twittercited by 10
- 2021TROLLMAGNIFIER: Detecting State-Sponsored Troll Accounts on Redditcited by 10
- 2021AraStance: A Multi-Country and Multi-Domain Dataset of Arabic Stance Detection for Fact Checkingcited by 8
- 2022Stanceosaurus: Classifying Stance Towards Multilingual Misinformationcited by 8
- 2022LAMBRETTA: Learning to Rank for Twitter Soft Moderationcited by 8
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