mlsanityData to 14 Oct 2026

Not all Fake News is Written: A Dataset and Analysis of Misleading Video Headlines

arXiv 2310.13859 ↗First posted 20 Oct 2023cs.CL cs.CVcited by 5

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Abstract (arXiv)

Polarization and the marketplace for impressions have conspired to make navigating information online difficult for users, and while there has been a significant effort to detect false or misleading text, multimodal datasets have received considerably less attention. To complement existing resources, we present multimodal Video Misleading Headline (VMH), a dataset that consists of videos and whether annotators believe the headline is representative of the video's contents. After collecting and annotating this dataset, we analyze multimodal baselines for detecting misleading headlines. Our annotation process also focuses on why annotators view a video as misleading, allowing us to better understand the interplay of annotators' background and the content of the videos.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 17 of its 58 references that are in the map. First posted on arXiv 20 Oct 2023.

Builds on

17 of 58 references.

References · 17

  1. 2021Dynabench: Rethinking Benchmarking in NLPcited by 459
  2. 2021Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotationscited by 314
  3. 2021VLM: Task-agnostic Video-Language Model Pre-training for Video Understandingcited by 102
  4. 2021Dynaboard: An Evaluation-As-A-Service Platform for Holistic Next-Generation Benchmarkingcited by 53
  5. 2021Fool Me Twice: Entailment from Wikipedia Gamificationcited by 50
  6. 2019Automatic Argument Quality Assessment -- New Datasets and Methodscited by 47
  7. 2022FakeSV: A Multimodal Benchmark with Rich Social Context for Fake News Detection on Short Video Platformscited by 41
  8. 2016To Click or Not To Click: Automatic Selection of Beautiful Thumbnails from Videoscited by 34
  9. 2018Shifting the Baseline: Single Modality Performance on Visual Navigation & QAcited by 30
  10. 2018Trends in the Diffusion of Misinformation on Social Mediacited by 25
  11. 2020Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rankcited by 18
  12. 2017Diving Deep into Clickbaits: Who Use Them to What Extents in Which Topics with What Effects?cited by 11
5 more
  1. 2021Multi-Modal Semantic Inconsistency Detection in Social Media News Postscited by 10
  2. 2017Learning to Identify Ambiguous and Misleading News Headlinescited by 7
  3. 2015Breaking the News: First Impressions Matter on Online Newscited by 4
  4. 2021Crowdsourcing strong labels for sound event detectioncited by 3
  5. 2019Towards Reliable Online Clickbait Video Detection: A Content-Agnostic Approachcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 2.

5 papers.

  1. 2024DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detectioncited by 41
  2. 2025MODS: Moderating a Mixture of Document Speakers to Summarize Debatable Queries in Document Collectionscited by 4
  3. 2025Looking Beyond the Obvious: A Survey on Abstract Concept Recognition for Video Understandingcited by 3
  4. 2026MOMENTA: Mixture-of-Experts Over Multimodal Embeddings with Neural Temporal Aggregation for Misinformation Detectioncited by 0
  5. 2026YTClickbait21K: Human-Annotated Multimodal Dataset for YouTube Clickbait Detection Across Diverse Channels and Content Categoriescited by 0