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

IF 3 2区计算机科学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

EPJ Data Science Pub Date : 2024-06-26 DOI:10.1140/epjds/s13688-024-00465-2

Yun-Tae Jin, JaeBeom You, Shoko Wakamiya, Hyuk-Yoon Kwon

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引用次数: 0

Abstract

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.

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利用推文和新闻文章的位置信息之间的相关性分析用户反应

在本研究中，我们根据用户对新闻报道的推文分析了用户的反应程度，从而展示了家庭位置预测的潜力。为此，我们根据推文与新闻文章之间的文本相似度量化了用户对特定新闻文章的反应，结果显示，用户对有关其所在城市的新闻文章的反应明显高于对其他城市的反应。为了最大限度地缩小反应差异，我们引入了 "新闻独特性 "的概念，突出显示影响特定地点的新闻文章。通过将 "新闻独特性 "与用户对新闻的反应相结合，我们放大了其效果。通过对收集自美国五个代表性城市用户的推文和描述这些城市所发生事件的新闻文章进行实验，我们观察到，与用户对家庭所在地以外新闻的平均反应相比，用户对新闻的反应得分提高了 6.75% 到 40%，明显预测了用户的家庭所在地。此外，"新闻独特性 "还能将家乡新闻与家乡以外新闻的平均反应分值之差提高 12% 至 194%。这些结果表明，我们提出的想法可以用来预测用户的位置，从而有可能根据用户感兴趣的领域推荐有意义的信息。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

EPJ Data Science MATHEMATICS, INTERDISCIPLINARY APPLICATIONS -

CiteScore

6.10

自引率

5.60%

发文量

审稿时长

13 weeks

期刊介绍： EPJ Data Science covers a broad range of research areas and applications and particularly encourages contributions from techno-socio-economic systems, where it comprises those research lines that now regard the digital “tracks” of human beings as first-order objects for scientific investigation. Topics include, but are not limited to, human behavior, social interaction (including animal societies), economic and financial systems, management and business networks, socio-technical infrastructure, health and environmental systems, the science of science, as well as general risk and crisis scenario forecasting up to and including policy advice.