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

arXiv: https://arxiv.org/abs/2310.13859 · HTML: https://mlsanity.com/paper/2310.13859/

## 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 (most cited first, 17 of 17)

- [Dynabench: Rethinking Benchmarking in NLP](https://mlsanity.com/paper/2104.14337/) (2021)
- [Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations](https://mlsanity.com/paper/2110.05719/) (2021)
- [VLM: Task-agnostic Video-Language Model Pre-training for Video Understanding](https://mlsanity.com/paper/2105.09996/) (2021)
- [Dynaboard: An Evaluation-As-A-Service Platform for Holistic Next-Generation Benchmarking](https://mlsanity.com/paper/2106.06052/) (2021)
- [Fool Me Twice: Entailment from Wikipedia Gamification](https://mlsanity.com/paper/2104.04725/) (2021)
- [Automatic Argument Quality Assessment -- New Datasets and Methods](https://mlsanity.com/paper/1909.01007/) (2019)
- [FakeSV: A Multimodal Benchmark with Rich Social Context for Fake News Detection on Short Video Platforms](https://mlsanity.com/paper/2211.10973/) (2022)
- [To Click or Not To Click: Automatic Selection of Beautiful Thumbnails from Videos](https://mlsanity.com/paper/1609.01388/) (2016)
- [Shifting the Baseline: Single Modality Performance on Visual Navigation & QA](https://mlsanity.com/paper/1811.00613/) (2018)
- [Trends in the Diffusion of Misinformation on Social Media](https://mlsanity.com/paper/1809.05901/) (2018)
- [Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank](https://mlsanity.com/paper/2010.03662/) (2020)
- [Diving Deep into Clickbaits: Who Use Them to What Extents in Which Topics with What Effects?](https://mlsanity.com/paper/1703.09400/) (2017)
- [Multi-Modal Semantic Inconsistency Detection in Social Media News Posts](https://mlsanity.com/paper/2105.12855/) (2021)
- [Learning to Identify Ambiguous and Misleading News Headlines](https://mlsanity.com/paper/1705.06031/) (2017)
- [Breaking the News: First Impressions Matter on Online News](https://mlsanity.com/paper/1503.07921/) (2015)
- [Crowdsourcing strong labels for sound event detection](https://mlsanity.com/paper/2107.12089/) (2021)
- [Towards Reliable Online Clickbait Video Detection: A Content-Agnostic Approach](https://mlsanity.com/paper/1907.07604/) (2019)

## Built on by (most cited first, 5 of 5)

- [DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection](https://mlsanity.com/paper/2402.10426/) (2024)
- [MODS: Moderating a Mixture of Document Speakers to Summarize Debatable Queries in Document Collections](https://mlsanity.com/paper/2502.00322/) (2025)
- [Looking Beyond the Obvious: A Survey on Abstract Concept Recognition for Video Understanding](https://mlsanity.com/paper/2508.20765/) (2025)
- [MOMENTA: Mixture-of-Experts Over Multimodal Embeddings with Neural Temporal Aggregation for Misinformation Detection](https://mlsanity.com/paper/2604.16172/) (2026)
- [YTClickbait21K: Human-Annotated Multimodal Dataset for YouTube Clickbait Detection Across Diverse Channels and Content Categories](https://mlsanity.com/paper/2606.14780/) (2026)

## 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.