# Fact Check: Analyzing Financial Events from Multilingual News Sources

arXiv: https://arxiv.org/abs/2106.15221 · HTML: https://mlsanity.com/paper/2106.15221/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 29 Jun 2021.

## Builds on (most cited first, 4 of 4)

- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Generating Plausible Counterfactual Explanations for Deep Transformers in Financial Text Classification](https://mlsanity.com/paper/2010.12512/) (2020)
- [Tanbih: Get To Know What You Are Reading](https://mlsanity.com/paper/1910.02028/) (2019)
- [Explainable Text-Driven Neural Network for Stock Prediction](https://mlsanity.com/paper/1902.04994/) (2019)

## Built on by (most cited first, 1 of 1)

- [Revolutionizing Finance with LLMs: An Overview of Applications and Insights](https://mlsanity.com/paper/2401.11641/) (2024)

## Abstract (arXiv)

The explosion in the sheer magnitude and complexity of financial news data in recent years makes it increasingly challenging for investment analysts to extract valuable insights and perform analysis. We propose FactCheck in finance, a web-based news aggregator with deep learning models, to provide analysts with a holistic view of important financial events from multilingual news sources and extract events using an unsupervised clustering method. A web interface is provided to examine the credibility of news articles using a transformer-based fact-checker. The performance of the fact checker is evaluated using a dataset related to merger and acquisition (M&A) events and is shown to outperform several strong baselines.