# Fake News Detection: It's All in the Data!

arXiv: https://arxiv.org/abs/2407.02122 · HTML: https://mlsanity.com/paper/2407.02122/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 92 references that are in the map.
- First posted on arXiv 2 Jul 2024.

## Builds on (most cited first, 27 of 27)

- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detection](https://mlsanity.com/paper/2305.14902/) (2023)
- [The science of fake news](https://mlsanity.com/paper/2307.07903/) (2023)
- [Combating Misinformation in the Age of LLMs: Opportunities and Challenges](https://mlsanity.com/paper/2311.05656/) (2023)
- [Beyond News Contents: The Role of Social Context for Fake News Detection](https://mlsanity.com/paper/1712.07709/) (2017)
- [Predicting Factuality of Reporting and Bias of News Media Sources](https://mlsanity.com/paper/1810.01765/) (2018)
- [Analysing How People Orient to and Spread Rumours in Social Media by Looking at Conversational Threads](https://mlsanity.com/paper/1511.07487/) (2015)
- [This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real News](https://mlsanity.com/paper/1703.09398/) (2017)
- [The Role of User Profile for Fake News Detection](https://mlsanity.com/paper/1904.13355/) (2019)
- [N24News: A New Dataset for Multimodal News Classification](https://mlsanity.com/paper/2108.13327/) (2021)
- [Stop Clickbait: Detecting and Preventing Clickbaits in Online News Media](https://mlsanity.com/paper/1610.09786/) (2016)
- [Weak Supervision for Fake News Detection via Reinforcement Learning](https://mlsanity.com/paper/1912.12520/) (2019)
- [NELA-GT-2018: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articles](https://mlsanity.com/paper/1904.01546/) (2019)
- [Cross-modal Contrastive Learning for Multimodal Fake News Detection](https://mlsanity.com/paper/2302.14057/) (2023)
- [Some Like it Hoax: Automated Fake News Detection in Social Networks](https://mlsanity.com/paper/1704.07506/) (2017)
- [Multi-modal Misinformation Detection: Approaches, Challenges and Opportunities](https://mlsanity.com/paper/2203.13883/) (2022)
- [Multimodal Fake News Detection](https://mlsanity.com/paper/2112.04831/) (2021)
- [A framework for fake review detection in online consumer electronics retailers](https://mlsanity.com/paper/1903.12452/) (2019)
- [Dataset of Fake News Detection and Fact Verification: A Survey](https://mlsanity.com/paper/2111.03299/) (2021)
- [Mitigating Bias in Algorithmic Systems -- A Fish-Eye View](https://mlsanity.com/paper/2103.16953/) (2021)
- [Detecting Fake News with Capsule Neural Networks](https://mlsanity.com/paper/2002.01030/) (2020)
- [Advanced Machine Learning Techniques for Fake News (Online Disinformation) Detection: A Systematic Mapping Study](https://mlsanity.com/paper/2101.01142/) (2020)
- [Detection of Machine-Generated Text: Literature Survey](https://mlsanity.com/paper/2402.01642/) (2024)
- [It's All in the Embedding! Fake News Detection Using Document Embeddings](https://mlsanity.com/paper/2304.07781/) (2023)
- [FakeWatch ElectionShield: A Benchmarking Framework to Detect Fake News for Credible US Elections](https://mlsanity.com/paper/2312.03730/) (2023)
- [POLygraph: Polish Fake News Dataset](https://mlsanity.com/paper/2407.01393/) (2024)

## Built on by (most cited first, 4 of 4)

- [HULLMI: Human vs LLM identification with explainability](https://mlsanity.com/paper/2409.04808/) (2024)
- [ZoFia: Zero-Shot Fake News Detection with Entity-Guided Retrieval and Multi-LLM Interaction](https://mlsanity.com/paper/2511.01188/) (2025)
- [Addressing Data Scarcity in Bangla Fake News Detection: An LLM-Based Dataset Augmentation Approach](https://mlsanity.com/paper/2605.01292/) (2026)
- [Clickbait detection: quick inference with maximum impact](https://mlsanity.com/paper/2604.08148/) (2026)

## Abstract (arXiv)

This comprehensive survey serves as an indispensable resource for researchers embarking on the journey of fake news detection. By highlighting the pivotal role of dataset quality and diversity, it underscores the significance of these elements in the effectiveness and robustness of detection models. The survey meticulously outlines the key features of datasets, various labeling systems employed, and prevalent biases that can impact model performance. Additionally, it addresses critical ethical issues and best practices, offering a thorough overview of the current state of available datasets. Our contribution to this field is further enriched by the provision of GitHub repository, which consolidates publicly accessible datasets into a single, user-friendly portal. This repository is designed to facilitate and stimulate further research and development efforts aimed at combating the pervasive issue of fake news.