https://doi.org/10.1140/epjds/s13688-024-00465-2
Research
Analyzing user reactions using relevance between location information of tweets and news articles
1
Department of Industrial Engineering, Seoul National University of Science and Technology, 01811, Seoul, South Korea
2
Graduate School of Data Science, Seoul National University of Science and Technology, 01811, Seoul, South Korea
3
Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 6300192, Nara, Japan
4
Department of Industrial Engineering / Graduate School of Data Science, Seoul National University of Science and Technology, 01811, Seoul, South Korea
d
hyukyoon.kwon@seoultech.ac.kr
Received:
26
December
2023
Accepted:
18
March
2024
Published online:
26
June
2024
In this study, we analyze the extent of user reactions based on user’s tweets to news articles, demonstrating the potential for home location prediction. To achieve this, we quantify users’ reactions to specific news articles based on the textual similarity between tweets and news articles, showcasing that users’ reactions to news articles about their cities are significantly higher than those about other cities. To maximize the difference in reactions, we introduce the concept of News Distinctness, which highlights the news articles that affect a specific location. By incorporating News Distinctness with users’ reactions to the news, we magnify its effects. Through experiments conducted with tweets collected from users whose home locations are in five representative cities within the United States and news articles describing events occurring in those cities, we observed a 6.75% to 40% improvement in the reaction score when compared to the average reactions towards news for outside of home location, clearly predicting the home location. Furthermore, News Distinctness increases the difference in reaction score between news in the home location and the average of the news outside of the home location by 12% to 194%. These results demonstrate that our proposed idea can be utilized to predict the users’ location, potentially recommending meaningful information based on the users’ areas of interest.
Key words: Textual similarity / Location Prediction / SNS analysis / News distinctness
© The Author(s) 2024
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