# Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancements

arXiv: https://arxiv.org/abs/2001.00623 · HTML: https://mlsanity.com/paper/2001.00623/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2020.

## Built on by (most cited first, 17 of 17)

- [Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward](https://mlsanity.com/paper/2103.00484/) (2021)
- [Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods](https://mlsanity.com/paper/2210.07321/) (2022)
- [Can Large Language Models Identify Authorship?](https://mlsanity.com/paper/2403.08213/) (2024)
- [Targeted Phishing Campaigns using Large Scale Language Models](https://mlsanity.com/paper/2301.00665/) (2022)
- [Knowledge Graph informed Fake News Classification via Heterogeneous Representation Ensembles](https://mlsanity.com/paper/2110.10457/) (2021)
- [An Empirical Study of AI Generated Text Detection Tools](https://mlsanity.com/paper/2310.01423/) (2023)
- [Chatbots to ChatGPT in a Cybersecurity Space: Evolution, Vulnerabilities, Attacks, Challenges, and Future Recommendations](https://mlsanity.com/paper/2306.09255/) (2023)
- [Mitigation of Diachronic Bias in Fake News Detection Dataset](https://mlsanity.com/paper/2108.12601/) (2021)
- [ExClaim: Explainable Neural Claim Verification Using Rationalization](https://mlsanity.com/paper/2301.08914/) (2023)
- [Quantitative Analysis of AI-Generated Texts in Academic Research: A Study of AI Presence in Arxiv Submissions using AI Detection Tool](https://mlsanity.com/paper/2403.13812/) (2024)
- [Deriving Disinformation Insights from Geolocalized Twitter Callouts](https://mlsanity.com/paper/2108.03067/) (2021)
- [Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or Something Else?](https://mlsanity.com/paper/2111.05139/) (2021)
- [Graphical Models of False Information and Fact Checking Ecosystems](https://mlsanity.com/paper/2208.11582/) (2022)
- [FNDEX: Fake News and Doxxing Detection with Explainable AI](https://mlsanity.com/paper/2410.22390/) (2024)
- [Do Voters Get the Information They Want? Understanding Authentic Voter FAQs in the US and How to Improve for Informed Electoral Participation](https://mlsanity.com/paper/2412.15273/) (2024)
- [Enhancing Social Media Rumor Detection: A Semantic and Graph Neural Network Approach for the 2024 Global Election](https://mlsanity.com/paper/2503.01394/) (2025)
- [Leveraging Knowledge Graphs and LLMs for Structured Generation of Misinformation](https://mlsanity.com/paper/2505.24479/) (2025)

## Abstract (arXiv)

In recent years, disinformation including fake news, has became a global phenomenon due to its explosive growth, particularly on social media. The wide spread of disinformation and fake news can cause detrimental societal effects. Despite the recent progress in detecting disinformation and fake news, it is still non-trivial due to its complexity, diversity, multi-modality, and costs of fact-checking or annotation. The goal of this chapter is to pave the way for appreciating the challenges and advancements via: (1) introducing the types of information disorder on social media and examine their differences and connections; (2) describing important and emerging tasks to combat disinformation for characterization, detection and attribution; and (3) discussing a weak supervision approach to detect disinformation with limited labeled data. We then provide an overview of the chapters in this book that represent the recent advancements in three related parts: (1) user engagements in the dissemination of information disorder; (2) techniques on detecting and mitigating disinformation; and (3) trending issues such as ethics, blockchain, clickbaits, etc. We hope this book to be a convenient entry point for researchers, practitioners, and students to understand the problems and challenges, learn state-of-the-art solutions for their specific needs, and quickly identify new research problems in their domains.