# Emotion Detection for Misinformation: A Review

arXiv: https://arxiv.org/abs/2311.00671 · HTML: https://mlsanity.com/paper/2311.00671/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 57 of its 252 references that are in the map.
- First posted on arXiv 1 Nov 2023.

## Builds on (most cited first, 30 of 57)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://mlsanity.com/paper/1908.03557/) (2019)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification](https://mlsanity.com/paper/2010.12421/) (2020)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media](https://mlsanity.com/paper/1809.01286/) (2018)
- [Crowdsourcing a Word-Emotion Association Lexicon](https://mlsanity.com/paper/1308.6297/) (2013)
- [MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims](https://mlsanity.com/paper/1909.03242/) (2019)
- [Automatic Detection of Fake News](https://mlsanity.com/paper/1708.07104/) (2017)
- [Fighting an Infodemic: COVID-19 Fake News Dataset](https://mlsanity.com/paper/2011.03327/) (2020)
- [SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours](https://mlsanity.com/paper/1704.05972/) (2017)
- [RumourEval 2019: Determining Rumour Veracity and Support for Rumours](https://mlsanity.com/paper/1809.06683/) (2018)
- [A Survey on Stance Detection for Mis- and Disinformation Identification](https://mlsanity.com/paper/2103.00242/) (2021)
- [MDFEND: Multi-domain Fake News Detection](https://mlsanity.com/paper/2201.00987/) (2022)
- [Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models](https://mlsanity.com/paper/2310.04027/) (2023)
- [Stance and Sentiment in Tweets](https://mlsanity.com/paper/1605.01655/) (2016)
- [This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real News](https://mlsanity.com/paper/1703.09398/) (2017)
- [Empath: Understanding Topic Signals in Large-Scale Text](https://mlsanity.com/paper/1602.06979/) (2016)
- [Word Affect Intensities](https://mlsanity.com/paper/1704.08798/) (2017)

## Built on by (most cited first, 22 of 22)

- [EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective Analysis](https://mlsanity.com/paper/2401.08508/) (2024)
- [RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning Based on Emotional Information](https://mlsanity.com/paper/2406.11093/) (2024)
- [Robust Fake News Detection using Large Language Models under Adversarial Sentiment Attacks](https://mlsanity.com/paper/2601.15277/) (2026)
- [Monolingual and Multilingual Misinformation Detection for Low-Resource Languages: A Comprehensive Survey](https://mlsanity.com/paper/2410.18390/) (2024)
- [Mamba-Enhanced Text-Audio-Video Alignment Network for Emotion Recognition in Conversations](https://mlsanity.com/paper/2409.05243/) (2024)
- [ConspEmoLLM: Conspiracy Theory Detection Using an Emotion-Based Large Language Model](https://mlsanity.com/paper/2403.06765/) (2024)
- [AMPLE: Emotion-Aware Multimodal Fusion Prompt Learning for Fake News Detection](https://mlsanity.com/paper/2410.15591/) (2024)
- [Rumor Detection by Multi-task Suffix Learning based on Time-series Dual Sentiments](https://mlsanity.com/paper/2502.14383/) (2025)
- [Advanced Health Misinformation Detection Through Hybrid CNN-LSTM Models Informed by the Elaboration Likelihood Model (ELM)](https://mlsanity.com/paper/2507.09149/) (2025)
- [Detecting harassment and defamation in cyberbullying with emotion-adaptive training](https://mlsanity.com/paper/2501.16925/) (2025)
- [ConspEmoLLM-v2: A robust and stable model to detect sentiment-transformed conspiracy theories](https://mlsanity.com/paper/2505.14917/) (2025)
- [Hope Speech Detection in code-mixed Roman Urdu tweets: A Positive Turn in Natural Language Processing](https://mlsanity.com/paper/2506.21583/) (2025)
- [Effectiveness of Large Multimodal Models in Detecting Disinformation: Experimental Results](https://mlsanity.com/paper/2509.22377/) (2025)
- [DimABSA: Building Multilingual and Multidomain Datasets for Dimensional Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2601.23022/) (2026)
- [MoEITS: A Green AI approach for simplifying MoE-LLMs](https://mlsanity.com/paper/2604.10603/) (2026)
- [Fear Is Not Always Fear: Variation in Emotion Label Equivalence Across Datasets](https://mlsanity.com/paper/2610.12841/) (2026)
- [An Analytical Emotion Framework of Rumour Threads on Social Media](https://mlsanity.com/paper/2502.16560/) (2025)
- [Bridging Cognition and Emotion: Empathy-Driven Multimodal Misinformation Detection](https://mlsanity.com/paper/2504.17332/) (2025)
- [LLMs vs. Traditional Sentiment Tools in Psychology: An Evaluation on Belgian-Dutch Narratives](https://mlsanity.com/paper/2511.07641/) (2025)
- [An Evidence-Grounded Retrieval-Augmented Transformer Framework for Health Misinformation Verification](https://mlsanity.com/paper/2608.02310/) (2026)
- [Effects of Transcript Compression on LLM-based Medical Misinformation Detection in Japanese YouTube Videos](https://mlsanity.com/paper/2609.30882/) (2026)
- [Leveraging LLM-Generated Explanations for Detecting Emotionally Rewritten Fake News](https://mlsanity.com/paper/2610.08835/) (2026)

## Abstract (arXiv)

With the advent of social media, an increasing number of netizens are sharing and reading posts and news online. However, the huge volumes of misinformation (e.g., fake news and rumors) that flood the internet can adversely affect people's lives, and have resulted in the emergence of rumor and fake news detection as a hot research topic. The emotions and sentiments of netizens, as expressed in social media posts and news, constitute important factors that can help to distinguish fake news from genuine news and to understand the spread of rumors. This article comprehensively reviews emotion-based methods for misinformation detection. We begin by explaining the strong links between emotions and misinformation. We subsequently provide a detailed analysis of a range of misinformation detection methods that employ a variety of emotion, sentiment and stance-based features, and describe their strengths and weaknesses. Finally, we discuss a number of ongoing challenges in emotion-based misinformation detection based on large language models and suggest future research directions, including data collection (multi-platform, multilingual), annotation, benchmark, multimodality, and interpretability.