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Analyzing user reactions using relevance between location information of tweets and news articles

Publication

Paper Title: Analyzing user reactions using relevance between location information of tweets and news articles
Authors: Yun-Tae Jin, JaeBeom You, Shoko Wakamiya, Hyuk-Yoon Kwon
Journal: EPJ Data Science
Year: 2024
DOI: 10.1140/epjds/s13688-024-00465-2
Publisher: SpringerOpen
Published: 15 February 2024

Link: https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-024-00465-2

Abstract

This study investigates the relationship between location information in tweets and news articles to analyze user reactions. We propose a method to identify the relevance between geographical information embedded in social media posts and news content, enabling better understanding of how location-based events influence public discourse and sentiment.

Directory Structure

  • ./pretrained_models: directory for pretrained models (Word2Vec, Doc2Vec, Glove, FastText's pretrained model)
  • ./data: directory for data (news_data, tweet_data, result_data)
  • ./analyze_data: directory for analyze source code
  • ./data_collecting_tool: directory for data collecting tool
  • ./data_preprocessing: directory for data preprocessing source code

Getting Started

  1. Data Collection: Collect tweet and news data using the data collecting tool in ./data_collecting_tool
  2. Data Preprocessing: Preprocess the collected data using the source code in ./data_preprocessing
  3. Model Setup: Download the pretrained models and place them in ./pretrained_models
  4. Analysis: Analyze the preprocessed data using the source code in ./analyze_data

Features

  • Location-based tweet analysis
  • News article processing and correlation
  • Relevance scoring between tweets and news articles
  • Geographic sentiment analysis
  • Multi-modal data integration

Citation

If you use this code or reference this work, please cite:

@article{jin2024analyzing,
  title={Analyzing user reactions using relevance between location information of tweets and news articles},
  author={Jin, Yun-Tae and You, JaeBeom and Wakamiya, Shoko and Kwon, Hyuk-Yoon},
  journal={EPJ Data Science},
  volume={13},
  number={1},
  pages={8},
  year={2024},
  publisher={SpringerOpen},
  doi={10.1140/epjds/s13688-024-00465-2}
}

Contact

For questions about this research, please contact the authors through the paper's corresponding author information.