# TweetCred: Real-Time Credibility Assessment of Content on Twitter

arXiv: https://arxiv.org/abs/1405.5490 · HTML: https://mlsanity.com/paper/1405.5490/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2014.

## Built on by (most cited first, 12 of 12)

- [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)
- [Multimodal Emergent Fake News Detection via Meta Neural Process Networks](https://mlsanity.com/paper/2106.13711/) (2021)
- [Cross-SEAN: A Cross-Stitch Semi-Supervised Neural Attention Model for COVID-19 Fake News Detection](https://mlsanity.com/paper/2102.08924/) (2021)
- [TieFake: Title-Text Similarity and Emotion-Aware Fake News Detection](https://mlsanity.com/paper/2304.09421/) (2023)
- [Seeing and Believing: Evaluating the Trustworthiness of Twitter Users](https://mlsanity.com/paper/2107.08027/) (2021)
- [Trust and Believe -- Should We? Evaluating the Trustworthiness of Twitter Users](https://mlsanity.com/paper/2210.15214/) (2022)
- [LRCTI: A Large Language Model-Based Framework for Multi-Step Evidence Retrieval and Reasoning in Cyber Threat Intelligence Credibility Verification](https://mlsanity.com/paper/2507.11310/) (2025)
- [SOK: Fake News Outbreak 2021: Can We Stop the Viral Spread?](https://mlsanity.com/paper/2105.10671/) (2021)
- [DeepTrust: A Reliable Financial Knowledge Retrieval Framework For Explaining Extreme Pricing Anomalies](https://mlsanity.com/paper/2203.08144/) (2022)
- [Graphical Models of False Information and Fact Checking Ecosystems](https://mlsanity.com/paper/2208.11582/) (2022)
- [SceneGraMMi: Scene Graph-boosted Hybrid-fusion for Multi-Modal Misinformation Veracity Prediction](https://mlsanity.com/paper/2410.15517/) (2024)
- [CrediBench: Building Web-Scale Network Datasets for Information Integrity](https://mlsanity.com/paper/2509.23340/) (2025)

## Abstract (arXiv)

During sudden onset crisis events, the presence of spam, rumors and fake content on Twitter reduces the value of information contained on its messages (or "tweets"). A possible solution to this problem is to use machine learning to automatically evaluate the credibility of a tweet, i.e. whether a person would deem the tweet believable or trustworthy. This has been often framed and studied as a supervised classification problem in an off-line (post-hoc) setting. In this paper, we present a semi-supervised ranking model for scoring tweets according to their credibility. This model is used in TweetCred, a real-time system that assigns a credibility score to tweets in a user's timeline. TweetCred, available as a browser plug-in, was installed and used by 1,127 Twitter users within a span of three months. During this period, the credibility score for about 5.4 million tweets was computed, allowing us to evaluate TweetCred in terms of response time, effectiveness and usability. To the best of our knowledge, this is the first research work to develop a real-time system for credibility on Twitter, and to evaluate it on a user base of this size.