# SemCAFE: When Named Entities make the Difference Assessing Web Source Reliability through Entity-level Analytics

arXiv: https://arxiv.org/abs/2504.08776 · HTML: https://mlsanity.com/paper/2504.08776/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 52 references that are in the map.
- First posted on arXiv 3 Apr 2025.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [MDFEND: Multi-domain Fake News Detection](https://mlsanity.com/paper/2201.00987/) (2022)
- [FakeCovid -- A Multilingual Cross-domain Fact Check News Dataset for COVID-19](https://mlsanity.com/paper/2006.11343/) (2020)
- [Integrating Pattern- and Fact-based Fake News Detection via Model Preference Learning](https://mlsanity.com/paper/2109.11333/) (2021)
- [Media Cloud: Massive Open Source Collection of Global News on the Open Web](https://mlsanity.com/paper/2104.03702/) (2021)
- [Knowledge Graph informed Fake News Classification via Heterogeneous Representation Ensembles](https://mlsanity.com/paper/2110.10457/) (2021)
- [Propaganda and Misinformation on Facebook and Twitter during the Russian Invasion of Ukraine](https://mlsanity.com/paper/2212.00419/) (2022)
- [Retrieving false claims on Twitter during the Russia-Ukraine conflict](https://mlsanity.com/paper/2303.10121/) (2023)
- [Analysing State-Backed Propaganda Websites: a New Dataset and Linguistic Study](https://mlsanity.com/paper/2310.14032/) (2023)
- [A Study of Partisan News Sharing in the Russian invasion of Ukraine](https://mlsanity.com/paper/2311.15294/) (2023)
- [Multidimensional Knowledge Graph Embeddings for International Trade Flow Analysis](https://mlsanity.com/paper/2410.19835/) (2024)

## Abstract (arXiv)

With the shift from traditional to digital media, the online landscape now hosts not only reliable news articles but also a significant amount of unreliable content. Digital media has faster reachability by significantly influencing public opinion and advancing political agendas. While newspaper readers may be familiar with their preferred outlets political leanings or credibility, determining unreliable news articles is much more challenging. The credibility of many online sources is often opaque, with AI generated content being easily disseminated at minimal cost. Unreliable news articles, particularly those that followed the Russian invasion of Ukraine in 2022, closely mimic the topics and writing styles of credible sources, making them difficult to distinguish. To address this, we introduce SemCAFE, a system designed to detect news reliability by incorporating entity relatedness into its assessment. SemCAFE employs standard Natural Language Processing techniques, such as boilerplate removal and tokenization, alongside entity level semantic analysis using the YAGO knowledge base. By creating a semantic fingerprint for each news article, SemCAFE could assess the credibility of 46,020 reliable and 3,407 unreliable articles on the 2022 Russian invasion of Ukraine. Our approach improved the macro F1 score by 12% over state of the art methods. The sample data and code are available on GitHub