# Forming Predictive Features of Tweets for Decision-Making Support

arXiv: https://arxiv.org/abs/2201.02049 · HTML: https://mlsanity.com/paper/2201.02049/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 38 references that are in the map.
- First posted on arXiv 6 Jan 2022.

## Builds on (most cited first, 3 of 3)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Can Twitter Predict Royal Baby's Name ?](https://mlsanity.com/paper/1310.3500/) (2013)
- [Forecasting of Events by Tweet Data Mining](https://mlsanity.com/paper/1310.3499/) (2013)

## Built on by (most cited first, 5 of 5)

- [Analysis of Disinformation and Fake News Detection Using Fine-Tuned Large Language Model](https://mlsanity.com/paper/2309.04704/) (2023)
- [Methods of Informational Trends Analytics and Fake News Detection on Twitter](https://mlsanity.com/paper/2204.04891/) (2022)
- [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)
- [Multilevel Analysis of Cryptocurrency News using RAG Approach with Fine-Tuned Mistral Large Language Model](https://mlsanity.com/paper/2509.03527/) (2025)

## Abstract (arXiv)

The article describes the approaches for forming different predictive features of tweet data sets and using them in the predictive analysis for decision-making support. The graph theory as well as frequent itemsets and association rules theory is used for forming and retrieving different features from these datasests. The use of these approaches makes it possible to reveal a semantic structure in tweets related to a specified entity. It is shown that quantitative characteristics of semantic frequent itemsets can be used in predictive regression models with specified target variables.