# Fully Automated Fact Checking Using External Sources

arXiv: https://arxiv.org/abs/1710.00341 · HTML: https://mlsanity.com/paper/1710.00341/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2017.

## Built on by (most cited first, 27 of 27)

- [RARR: Researching and Revising What Language Models Say, Using Language Models](https://mlsanity.com/paper/2210.08726/) (2022)
- [Automated Fact-Checking for Assisting Human Fact-Checkers](https://mlsanity.com/paper/2103.07769/) (2021)
- [AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Web](https://mlsanity.com/paper/2305.13117/) (2023)
- [Towards Few-Shot Fact-Checking via Perplexity](https://mlsanity.com/paper/2103.09535/) (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)
- [FakeFlow: Fake News Detection by Modeling the Flow of Affective Information](https://mlsanity.com/paper/2101.09810/) (2021)
- [A Survey on Predicting the Factuality and the Bias of News Media](https://mlsanity.com/paper/2103.12506/) (2021)
- [NewsClaims: A New Benchmark for Claim Detection from News with Attribute Knowledge](https://mlsanity.com/paper/2112.08544/) (2021)
- [Overview of the CLEF-2019 CheckThat!: Automatic Identification and Verification of Claims](https://mlsanity.com/paper/2109.15118/) (2021)
- [AVerImaTeC: A Dataset for Automatic Verification of Image-Text Claims with Evidence from the Web](https://mlsanity.com/paper/2505.17978/) (2025)
- [Can LLMs Improve Multimodal Fact-Checking by Asking Relevant Questions?](https://mlsanity.com/paper/2410.04616/) (2024)
- [FacTeR-Check: Semi-automated fact-checking through Semantic Similarity and Natural Language Inference](https://mlsanity.com/paper/2110.14532/) (2021)
- [FACTOID: FACtual enTailment fOr hallucInation Detection](https://mlsanity.com/paper/2403.19113/) (2024)
- [Credible, Unreliable or Leaked?: Evidence Verification for Enhanced Automated Fact-checking](https://mlsanity.com/paper/2404.18971/) (2024)
- [Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Language](https://mlsanity.com/paper/2109.07909/) (2021)
- [A Second Pandemic? Analysis of Fake News About COVID-19 Vaccines in Qatar](https://mlsanity.com/paper/2109.11372/) (2021)
- [Who Are All The Stochastic Parrots Imitating? They Should Tell Us!](https://mlsanity.com/paper/2310.10583/) (2023)
- [TrumorGPT: Graph-Based Retrieval-Augmented Large Language Model for Fact-Checking](https://mlsanity.com/paper/2505.07891/) (2025)
- [Beyond True or False: Retrieval-Augmented Hierarchical Analysis of Nuanced Claims](https://mlsanity.com/paper/2506.10728/) (2025)
- [Multiverse: Multilingual Evidence for Fake News Detection](https://mlsanity.com/paper/2211.14279/) (2022)
- [Multi-Layer Ranking with Large Language Models for News Source Recommendation](https://mlsanity.com/paper/2406.11745/) (2024)
- [Fact-checking with Generative AI: A Systematic Cross-Topic Examination of LLMs Capacity to Detect Veracity of Political Information](https://mlsanity.com/paper/2503.08404/) (2025)
- [The Choice of Knowledge Base in Automated Claim Checking](https://mlsanity.com/paper/2111.07795/) (2021)
- [Science Checker: Extractive-Boolean Question Answering For Scientific Fact Checking](https://mlsanity.com/paper/2204.12263/) (2022)
- [Automated Query Generation for Evidence Collection from Web Search Engines](https://mlsanity.com/paper/2303.08652/) (2023)
- [Generative Debunking of Climate Misinformation](https://mlsanity.com/paper/2407.05599/) (2024)
- [Evidence-based Factual Error Correction](https://mlsanity.com/paper/2106.01072/) (2021)

## Abstract (arXiv)

Given the constantly growing proliferation of false claims online in recent years, there has been also a growing research interest in automatically distinguishing false rumors from factually true claims. Here, we propose a general-purpose framework for fully-automatic fact checking using external sources, tapping the potential of the entire Web as a knowledge source to confirm or reject a claim. Our framework uses a deep neural network with LSTM text encoding to combine semantic kernels with task-specific embeddings that encode a claim together with pieces of potentially-relevant text fragments from the Web, taking the source reliability into account. The evaluation results show good performance on two different tasks and datasets: (i) rumor detection and (ii) fact checking of the answers to a question in community question answering forums.