Social media platforms like Twitter (now X) have been pivotal in information dissemination and public engagement. The objective of our research is to analyze the effect of localized engagement on social media conversations. This study examines the impact of geographic co-location, as a proxy for localized engagement. Our research is grounded in a COVID-19 dataset. A key goal during the pandemic for public health experts was to encourage prosocial behavior that could impact local outcomes such as masking and social distancing. Given the importance of local news and guidance during COVID-19, we analyze the effect of localized engagement, between public health experts (PHEs) and the public, on social media. We analyze a Twitter Conversation dataset from January 2020 to November 2021, comprising over 19 K tweets from nearly five hundred PHEs, and 800 K replies from 350 K participants. We use a Poisson regression model to show that geo-co-location is indeed associated with higher engagement. Lexical features associated with emotion and personal experiences were more common in geo-co-located conversations. To complement our statistical analysis, we also applied a large language model (LLM)-based method to automatically generate and evaluate hypotheses; the LLM results confirm the results using lexical features. This research provides insights into how geographic co-location influences social media engagement and can inform strategies to improve public health messaging.

翻译：社交媒体平台（如推特，现更名为X）在信息传播与公众参与中扮演着关键角色。本研究旨在分析本地化互动对社交媒体对话的影响。我们以地理共址作为本地化互动的代理变量，考察其对社交媒体互动的作用。研究基于COVID-19数据集展开。疫情期间，公共卫生专家的核心目标之一是鼓励亲社会行为（如佩戴口罩和保持社交距离），这些行为可能影响本地防疫成效。鉴于COVID-19期间本地新闻和指导方针的重要性，我们分析了公共卫生专家与公众之间本地化互动对社交媒体的影响。研究使用了2020年1月至2021年11月的推特对话数据集，涵盖近500位公共卫生专家发布的超过1.9万条推文，以及35万参与者发布的80万条回复。我们采用泊松回归模型证明，地理共址确实与更高的互动率相关。地理共址对话中更常出现与情感和个人经历相关的词汇特征。为补充统计分析，我们还应用了基于大语言模型的方法自动生成并评估假设；大语言模型结果验证了基于词汇特征的结论。本研究揭示了地理共址如何影响社交媒体互动，并为优化公共卫生信息传播策略提供了见解。