# Sentimental LIAR: Extended Corpus and Deep Learning Models for Fake Claim Classification

arXiv: https://arxiv.org/abs/2009.01047 · HTML: https://mlsanity.com/paper/2009.01047/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2020.

## Built on by (most cited first, 7 of 7)

- [Overview of Robust and Multilingual Automatic Evaluation Metrics for Open-Domain Dialogue Systems at DSTC 11 Track 4](https://mlsanity.com/paper/2306.12794/) (2023)
- [It's All in the Embedding! Fake News Detection Using Document Embeddings](https://mlsanity.com/paper/2304.07781/) (2023)
- [Deception Detection with Feature-Augmentation by soft Domain Transfer](https://mlsanity.com/paper/2305.01011/) (2023)
- [Fake News and Phishing Detection Using a Machine Learning Trained Expert System](https://mlsanity.com/paper/2108.08264/) (2021)
- [Effects of Soft-Domain Transfer and Named Entity Information on Deception Detection](https://mlsanity.com/paper/2410.14814/) (2024)
- [HSFN: Hierarchical Selection for Fake News Detection building Heterogeneous Ensemble](https://mlsanity.com/paper/2508.21482/) (2025)
- [Exposing Pink Slime Journalism: Linguistic Signatures and Robust Detection Against LLM-Generated Threats](https://mlsanity.com/paper/2512.05331/) (2025)

## Abstract (arXiv)

The rampant integration of social media in our every day lives and culture has given rise to fast and easier access to the flow of information than ever in human history. However, the inherently unsupervised nature of social media platforms has also made it easier to spread false information and fake news. Furthermore, the high volume and velocity of information flow in such platforms make manual supervision and control of information propagation infeasible. This paper aims to address this issue by proposing a novel deep learning approach for automated detection of false short-text claims on social media. We first introduce Sentimental LIAR, which extends the LIAR dataset of short claims by adding features based on sentiment and emotion analysis of claims. Furthermore, we propose a novel deep learning architecture based on the BERT-Base language model for classification of claims as genuine or fake. Our results demonstrate that the proposed architecture trained on Sentimental LIAR can achieve an accuracy of 70%, which is an improvement of ~30% over previously reported results for the LIAR benchmark.