# The Future of Misinformation Detection: New Perspectives and Trends

arXiv: https://arxiv.org/abs/1909.03654 · HTML: https://mlsanity.com/paper/1909.03654/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2019.

## Built on by (most cited first, 10 of 10)

- [CrAM: Credibility-Aware Attention Modification in LLMs for Combating Misinformation in RAG](https://mlsanity.com/paper/2406.11497/) (2024)
- [A Survey on Event-based News Narrative Extraction](https://mlsanity.com/paper/2302.08351/) (2023)
- [Harnessing the Power of Text-image Contrastive Models for Automatic Detection of Online Misinformation](https://mlsanity.com/paper/2304.10249/) (2023)
- [Monolingual and Multilingual Misinformation Detection for Low-Resource Languages: A Comprehensive Survey](https://mlsanity.com/paper/2410.18390/) (2024)
- [A Self-Learning Multimodal Approach for Fake News Detection](https://mlsanity.com/paper/2412.05843/) (2024)
- [From Online Behaviours to Images: A Novel Approach to Social Bot Detection](https://mlsanity.com/paper/2304.07535/) (2023)
- [Misinformation Detection using Large Language Models with Explainability](https://mlsanity.com/paper/2510.18918/) (2025)
- [Capturing Pertinent Symbolic Features for Enhanced Content-Based Misinformation Detection](https://mlsanity.com/paper/2401.16285/) (2024)
- [Bridging Cognition and Emotion: Empathy-Driven Multimodal Misinformation Detection](https://mlsanity.com/paper/2504.17332/) (2025)
- [Towards Explainable Bilingual Multimodal Misinformation Detection and Localization](https://mlsanity.com/paper/2506.22930/) (2025)

## Abstract (arXiv)

The massive spread of misinformation in social networks has become a global risk, implicitly influencing public opinion and threatening social/political development. Misinformation detection (MID) has thus become a surging research topic in recent years. As a promising and rapid developing research field, we find that many efforts have been paid to new research problems and approaches of MID. Therefore, it is necessary to give a comprehensive review of the new research trends of MID. We first give a brief review of the literature history of MID, based on which we present several new research challenges and techniques of it, including early detection, detection by multimodal data fusion, and explanatory detection. We further investigate the extraction and usage of various crowd intelligence in MID, which paves a promising way to tackle MID challenges. Finally, we give our own views on the open issues and future research directions of MID, such as model adaptivity/generality to new events, embracing of novel machine learning models, explanatory detection models, and so on.