# Using Twitter Attribute Information to Predict Stock Prices

arXiv: https://arxiv.org/abs/2105.01402 · HTML: https://mlsanity.com/paper/2105.01402/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 35 references that are in the map.
- First posted on arXiv 4 May 2021.

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

- [Sentiment Analysis of Twitter Data for Predicting Stock Market Movements](https://mlsanity.com/paper/1610.09225/) (2016)
- [Deep Learning for Forecasting Stock Returns in the Cross-Section](https://mlsanity.com/paper/1801.01777/) (2018)
- [Stock Volatility Prediction Using Recurrent Neural Networks with Sentiment Analysis](https://mlsanity.com/paper/1705.02447/) (2017)

## Built on by (most cited first, 3 of 3)

- [Real-Time Online Stock Forecasting Utilizing Integrated Quantitative and Qualitative Analysis](https://mlsanity.com/paper/2311.15218/) (2023)
- [Predictive AI with External Knowledge Infusion: Datasets and Benchmarks for Stock Markets](https://mlsanity.com/paper/2504.20058/) (2025)
- [Analyzing public sentiment to gauge key stock events and determine volatility in conjunction with time and options premiums](https://mlsanity.com/paper/2502.05403/) (2025)

## Abstract (arXiv)

Being able to predict stock prices might be the unspoken wish of stock investors. Although stock prices are complicated to predict, there are many theories about what affects their movements, including interest rates, news and social media. With the help of Machine Learning, complex patterns in data can be identified beyond the human intellect. In this thesis, a Machine Learning model for time series forecasting is created and tested to predict stock prices. The model is based on a neural network with several layers of LSTM and fully connected layers. It is trained with historical stock values, technical indicators and Twitter attribute information retrieved, extracted and calculated from posts on the social media platform Twitter. These attributes are sentiment score, favourites, followers, retweets and if an account is verified. To collect data from Twitter, Twitter's API is used. Sentiment analysis is conducted with VADER. The results show that by adding more Twitter attributes, the MSE between the predicted prices and the actual prices improved by 3%. With technical analysis taken into account, MSE decreases from 0.1617 to 0.1437, which is an improvement of around 11%. The restrictions of this study include that the selected stock has to be publicly listed on the stock market and popular on Twitter and among individual investors. Besides, the stock markets' opening hours differ from Twitter, which constantly available. It may therefore introduce noises in the model.