# "The Data Says Otherwise"-Towards Automated Fact-checking and Communication of Data Claims

arXiv: https://arxiv.org/abs/2409.10713 · HTML: https://mlsanity.com/paper/2409.10713/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2024.

## Built on by (most cited first, 3 of 3)

- [DATAWEAVER: Authoring Data-Driven Narratives through the Integrated Composition of Visualization and Text](https://mlsanity.com/paper/2503.22946/) (2025)
- [RAGTrace: Understanding and Refining Retrieval-Generation Dynamics in Retrieval-Augmented Generation](https://mlsanity.com/paper/2508.06056/) (2025)
- [T-REX: Table -- Refute or Entail eXplainer](https://mlsanity.com/paper/2508.14055/) (2025)

## Abstract (arXiv)

Fact-checking data claims requires data evidence retrieval and analysis, which can become tedious and intractable when done manually. This work presents Aletheia, an automated fact-checking prototype designed to facilitate data claims verification and enhance data evidence communication. For verification, we utilize a pre-trained LLM to parse the semantics for evidence retrieval. To effectively communicate the data evidence, we design representations in two forms: data tables and visualizations, tailored to various data fact types. Additionally, we design interactions that showcase a real-world application of these techniques. We evaluate the performance of two core NLP tasks with a curated dataset comprising 400 data claims and compare the two representation forms regarding viewers' assessment time, confidence, and preference via a user study with 20 participants. The evaluation offers insights into the feasibility and bottlenecks of using LLMs for data fact-checking tasks, potential advantages and disadvantages of using visualizations over data tables, and design recommendations for presenting data evidence.