# SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours

arXiv: https://arxiv.org/abs/1704.05972 · HTML: https://mlsanity.com/paper/1704.05972/

## Key facts

- Cited by 77 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2017.

## Built on by (most cited first, 30 of 77)

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- [Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training](https://mlsanity.com/paper/2109.06050/) (2021)
- [Overview of the CLEF--2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News](https://mlsanity.com/paper/2109.12987/) (2021)
- [QuanTemp: A real-world open-domain benchmark for fact-checking numerical claims](https://mlsanity.com/paper/2403.17169/) (2024)
- [Emotion Detection for Misinformation: A Review](https://mlsanity.com/paper/2311.00671/) (2023)
- [The Intended Uses of Automated Fact-Checking Artefacts: Why, How and Who](https://mlsanity.com/paper/2304.14238/) (2023)
- [Navigating Prompt Complexity for Zero-Shot Classification: A Study of Large Language Models in Computational Social Science](https://mlsanity.com/paper/2305.14310/) (2023)
- [Improved Target-specific Stance Detection on Social Media Platforms by Delving into Conversation Threads](https://mlsanity.com/paper/2211.03061/) (2022)
- [Investigating label suggestions for opinion mining in German Covid-19 social media](https://mlsanity.com/paper/2105.12980/) (2021)
- [Human-in-the-loop Evaluation for Early Misinformation Detection: A Case Study of COVID-19 Treatments](https://mlsanity.com/paper/2212.09683/) (2022)
- [VaxxHesitancy: A Dataset for Studying Hesitancy towards COVID-19 Vaccination on Twitter](https://mlsanity.com/paper/2301.06660/) (2023)
- [The CLEF-2025 CheckThat! Lab: Subjectivity, Fact-Checking, Claim Normalization, and Retrieval](https://mlsanity.com/paper/2503.14828/) (2025)
- [Claim-Dissector: An Interpretable Fact-Checking System with Joint Re-ranking and Veracity Prediction](https://mlsanity.com/paper/2207.14116/) (2022)
- [Dataset of Fake News Detection and Fact Verification: A Survey](https://mlsanity.com/paper/2111.03299/) (2021)
- [Semi-automatic Generation of Multilingual Datasets for Stance Detection in Twitter](https://mlsanity.com/paper/2101.11978/) (2021)
- [On Unifying Misinformation Detection](https://mlsanity.com/paper/2104.05243/) (2021)
- [A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning](https://mlsanity.com/paper/2204.02626/) (2022)
- [VRoC: Variational Autoencoder-aided Multi-task Rumor Classifier Based on Text](https://mlsanity.com/paper/2102.00816/) (2021)
- [STEM: Unsupervised STructural EMbedding for Stance Detection](https://mlsanity.com/paper/2112.00712/) (2021)
- [CrowdChecked: Detecting Previously Fact-Checked Claims in Social Media](https://mlsanity.com/paper/2210.04447/) (2022)
- [AraStance: A Multi-Country and Multi-Domain Dataset of Arabic Stance Detection for Fact Checking](https://mlsanity.com/paper/2104.13559/) (2021)
- [What goes on inside rumour and non-rumour tweets and their reactions: A Psycholinguistic Analyses](https://mlsanity.com/paper/2112.03003/) (2021)
- [Stanceosaurus: Classifying Stance Towards Multilingual Misinformation](https://mlsanity.com/paper/2210.15954/) (2022)
- [A Survey of Stance Detection on Social Media: New Directions and Perspectives](https://mlsanity.com/paper/2409.15690/) (2024)
- [Truth over Tricks: Measuring and Mitigating Shortcut Learning in Misinformation Detection](https://mlsanity.com/paper/2506.02350/) (2025)

## Abstract (arXiv)

Media is full of false claims. Even Oxford Dictionaries named "post-truth" as the word of 2016. This makes it more important than ever to build systems that can identify the veracity of a story, and the kind of discourse there is around it. RumourEval is a SemEval shared task that aims to identify and handle rumours and reactions to them, in text. We present an annotation scheme, a large dataset covering multiple topics - each having their own families of claims and replies - and use these to pose two concrete challenges as well as the results achieved by participants on these challenges.