# It Takes Nine to Smell a Rat: Neural Multi-Task Learning for Check-Worthiness Prediction

arXiv: https://arxiv.org/abs/1908.07912 · HTML: https://mlsanity.com/paper/1908.07912/

## Key facts

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

## Built on by (most cited first, 14 of 14)

- [Automated Fact-Checking for Assisting Human Fact-Checkers](https://mlsanity.com/paper/2103.07769/) (2021)
- [COSMOS: Catching Out-of-Context Misinformation with Self-Supervised Learning](https://mlsanity.com/paper/2101.06278/) (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)
- [NewsClaims: A New Benchmark for Claim Detection from News with Attribute Knowledge](https://mlsanity.com/paper/2112.08544/) (2021)
- [The Role of Context in Detecting Previously Fact-Checked Claims](https://mlsanity.com/paper/2104.07423/) (2021)
- [From Chaos to Clarity: Claim Normalization to Empower Fact-Checking](https://mlsanity.com/paper/2310.14338/) (2023)
- [Same Same, But Different: Conditional Multi-Task Learning for Demographic-Specific Toxicity Detection](https://mlsanity.com/paper/2302.07372/) (2023)
- [Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AI](https://mlsanity.com/paper/2308.07213/) (2023)
- [Detecting Check-Worthy Claims in Political Debates, Speeches, and Interviews Using Audio Data](https://mlsanity.com/paper/2306.05535/) (2023)
- [A Second Pandemic? Analysis of Fake News About COVID-19 Vaccines in Qatar](https://mlsanity.com/paper/2109.11372/) (2021)
- [Multilingual Detection of Check-Worthy Claims using World Languages and Adapter Fusion](https://mlsanity.com/paper/2301.05494/) (2023)
- [Leveraging Social Discourse to Measure Check-worthiness of Claims for Fact-checking](https://mlsanity.com/paper/2309.09274/) (2023)
- [Entity-Assisted Language Models for Identifying Check-worthy Sentences](https://mlsanity.com/paper/2211.10678/) (2022)
- [ClaimPT: A Portuguese Dataset of Annotated Claims in News Articles](https://mlsanity.com/paper/2601.19490/) (2026)

## Abstract (arXiv)

We propose a multi-task deep-learning approach for estimating the check-worthiness of claims in political debates. Given a political debate, such as the 2016 US Presidential and Vice-Presidential ones, the task is to predict which statements in the debate should be prioritized for fact-checking. While different fact-checking organizations would naturally make different choices when analyzing the same debate, we show that it pays to learn from multiple sources simultaneously (PolitiFact, FactCheck, ABC, CNN, NPR, NYT, Chicago Tribune, The Guardian, and Washington Post) in a multi-task learning setup, even when a particular source is chosen as a target to imitate. Our evaluation shows state-of-the-art results on a standard dataset for the task of check-worthiness prediction.