# Online Misinformation Detection in Live Streaming Videos

arXiv: https://arxiv.org/abs/2503.12627 · HTML: https://mlsanity.com/paper/2503.12627/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 16 Mar 2025.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Spatiotemporal Contrastive Video Representation Learning](https://mlsanity.com/paper/2008.03800/) (2020)
- [Detecting Harmful Memes and Their Targets](https://mlsanity.com/paper/2110.00413/) (2021)
- [Analysing How People Orient to and Spread Rumours in Social Media by Looking at Conversational Threads](https://mlsanity.com/paper/1511.07487/) (2015)
- [StartNet: Online Detection of Action Start in Untrimmed Videos](https://mlsanity.com/paper/1903.09868/) (2019)
- [A framework for fake review detection in online consumer electronics retailers](https://mlsanity.com/paper/1903.12452/) (2019)
- [Misinformation Detection on YouTube Using Video Captions](https://mlsanity.com/paper/2107.00941/) (2021)
- [Fake Colorized Image Detection](https://mlsanity.com/paper/1801.02768/) (2018)

## Abstract (arXiv)

Online misinformation detection is an important issue and methods are proposed to detect and curb misinformation in various forms. However, previous studies are conducted in an offline manner. We claim a realistic misinformation detection setting that has not been studied yet is online misinformation detection in live streaming videos (MDLS). In the proposal, we formulate the problem of MDLS and illustrate the importance and the challenge of the task. Besides, we propose feasible ways of developing the problem into AI challenges as well as potential solutions to the problem.