# Methods of Informational Trends Analytics and Fake News Detection on Twitter

arXiv: https://arxiv.org/abs/2204.04891 · HTML: https://mlsanity.com/paper/2204.04891/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 11 Apr 2022.

## Builds on (most cited first, 6 of 6)

- [Automatic Detection of Machine Generated Text: A Critical Survey](https://mlsanity.com/paper/2011.01314/) (2020)
- [TweepFake: about Detecting Deepfake Tweets](https://mlsanity.com/paper/2008.00036/) (2020)
- [Fake News Identification on Twitter with Hybrid CNN and RNN Models](https://mlsanity.com/paper/1806.11316/) (2018)
- [Dataset of Fake News Detection and Fact Verification: A Survey](https://mlsanity.com/paper/2111.03299/) (2021)
- [Forming Predictive Features of Tweets for Decision-Making Support](https://mlsanity.com/paper/2201.02049/) (2022)
- [Can Twitter Predict Royal Baby's Name ?](https://mlsanity.com/paper/1310.3500/) (2013)

## Built on by (most cited first, 6 of 6)

- [Analysis of Disinformation and Fake News Detection Using Fine-Tuned Large Language Model](https://mlsanity.com/paper/2309.04704/) (2023)
- [Financial News Analytics Using Fine-Tuned Llama 2 GPT Model](https://mlsanity.com/paper/2308.13032/) (2023)
- [Analytics of Business Time Series Using Machine Learning and Bayesian Inference](https://mlsanity.com/paper/2205.12905/) (2022)
- [AI Approaches to Qualitative and Quantitative News Analytics on NATO Unity](https://mlsanity.com/paper/2505.06313/) (2025)
- [Multilevel Analysis of Cryptocurrency News using RAG Approach with Fine-Tuned Mistral Large Language Model](https://mlsanity.com/paper/2509.03527/) (2025)
- [Using GPT Models for Qualitative and Quantitative News Analytics in the 2024 US Presidental Election Process](https://mlsanity.com/paper/2410.15884/) (2024)

## Abstract (arXiv)

In the paper, different approaches for the analysis of news trends on Twitter has been considered. For the analysis and case study, informational trends on Twitter caused by Russian invasion of Ukraine in 2022 year have been studied. A deep learning approach for fake news detection has been analyzed. The use of the theory of frequent itemsets and association rules, graph theory for news trends analytics have been considered.