BigData22

Goal

The BigData22 dataset is designed to advance stock movement prediction with self-supervised learning from sparse noisy tweets. Its primary goals are:

  • To predict stock price movements from noisy social media data

  • To handle sparse and unreliable social media signals

  • To benchmark robust forecasting methods for financial markets

Description

BigData22 is a stock movement prediction dataset focusing on learning from sparse and noisy tweets combined with historical stock prices.

Dataset Composition

  • Source: Twitter posts and historical stock prices

  • Time Period: Recent trading periods with social media activity

  • Sample Size: 7,164 instances

  • Data Modalities: Text (tweets) and time series (stock prices)

Example

Stock: Tesla Inc. (TSLA)

Historical Prices (3-day window): - Day -3: Open $850.00, Close $845.00 (-0.59%) - Day -2: Open $847.00, Close $862.00 (+1.77%) - Day -1: Open $863.00, Close $858.00 (-0.58%)

Sparse Tweets (noisy data): - “$TSLA to the moon 🚀🚀” (3 likes, low credibility) - “Elon musk factory tour tomorrow” (15 likes) - “$TSLA delivery numbers looking strong this quarter - analyst” (250 likes, higher credibility)

Prediction Target: Rise or Fall (movement > ±0.55%)

Ground Truth: Rise (+2.1% next day based on actual delivery numbers)

Task Description

Stock Movement Prediction from Noisy Data

  • Input: Sparse, noisy tweets and historical stock prices

  • Output: Binary classification (Rise or Fall)

  • Challenge: Filtering noise from sparse social media data while extracting meaningful signals for prediction

Key Use Cases

  1. Retail Trading Signals: Understanding retail investor sentiment from social media

  2. Noise Filtering: Developing robust models that handle unreliable data sources

  3. Social Media Analytics: Measuring impact of social chatter on stock prices

Evaluation Metrics

  1. Accuracy (ACC): Percentage of correct predictions

  2. Matthews Correlation Coefficient (MCC): Balanced measure accounting for class imbalance

References

Yejun Soun, Jaemin Yoo, Minyong Cho, Jihyeong Jeon, and U Kang. “Accurate Stock Movement Prediction with Self-supervised Learning from Sparse Noisy Tweets.” In 2022 IEEE International Conference on Big Data (Big Data), pages 1691–1700, 2022.

For dataset access, visit HuggingFace.