# In Search of Credible News

arXiv: https://arxiv.org/abs/1911.08125 · HTML: https://mlsanity.com/paper/1911.08125/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2019.

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

- [Overview of the CLEF-2019 CheckThat!: Automatic Identification and Verification of Claims](https://mlsanity.com/paper/2109.15118/) (2021)
- [Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims](https://mlsanity.com/paper/2204.12294/) (2022)
- [EUvsDisinfo: A Dataset for Multilingual Detection of Pro-Kremlin Disinformation in News Articles](https://mlsanity.com/paper/2406.12614/) (2024)
- [Predicting Sentence-Level Factuality of News and Bias of Media Outlets](https://mlsanity.com/paper/2301.11850/) (2023)
- [A Second Pandemic? Analysis of Fake News About COVID-19 Vaccines in Qatar](https://mlsanity.com/paper/2109.11372/) (2021)
- [bgGLUE: A Bulgarian General Language Understanding Evaluation Benchmark](https://mlsanity.com/paper/2306.02349/) (2023)
- [Two-Stage Classifier for COVID-19 Misinformation Detection Using BERT: a Study on Indonesian Tweets](https://mlsanity.com/paper/2206.15359/) (2022)
- [MINT -- Mainstream and Independent News Text Corpus](https://mlsanity.com/paper/2108.06249/) (2021)
- [Multimodal Misinformation Detection Using Early Fusion of Linguistic, Visual, and Social Features](https://mlsanity.com/paper/2507.01984/) (2025)
- [COVID-19 Infodemic. Understanding content features in detecting fake news using a machine learning approach](https://mlsanity.com/paper/2605.06435/) (2026)

## Abstract (arXiv)

We study the problem of finding fake online news. This is an important problem as news of questionable credibility have recently been proliferating in social media at an alarming scale. As this is an understudied problem, especially for languages other than English, we first collect and release to the research community three new balanced credible vs. fake news datasets derived from four online sources. We then propose a language-independent approach for automatically distinguishing credible from fake news, based on a rich feature set. In particular, we use linguistic (n-gram), credibility-related (capitalization, punctuation, pronoun use, sentiment polarity), and semantic (embeddings and DBPedia data) features. Our experiments on three different testsets show that our model can distinguish credible from fake news with very high accuracy.