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Does Geo-co-location Matter? A Case Study of Public Health Conversations during COVID-19

arXiv 2405.17710 ↗First posted 28 May 2024cs.SI cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 106 references that are in the map. First posted on arXiv 28 May 2024.

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14 of 106 references.

References · 14

  1. 2019SemEval-2017 Task 4: Sentiment Analysis in Twittercited by 210
  2. 2022TimeLMs: Diachronic Language Models from Twittercited by 131
  3. 2023Goal Driven Discovery of Distributional Differences via Language Descriptionscited by 62
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  5. 2023Affective Polarization and Dynamics of Information Spread in Online Networkscited by 12
  6. 2024Improving the TENOR of Labeling: Re-evaluating Topic Models for Content Analysiscited by 10
  7. 2015Confounds and Consequences in Geotagged Twitter Datacited by 8
  8. 2020Using Noisy Self-Reports to Predict Twitter User Demographicscited by 8
  9. 2024Emojis Decoded: Leveraging ChatGPT for Enhanced Understanding in Social Media Communicationscited by 7
  10. 2023Pandemic Culture Wars: Partisan Differences in the Moral Language of COVID-19 Discussionscited by 6
  11. 2020Sustained Online Amplification of COVID-19 Elites in the United Statescited by 4
  12. 2021More than Meets the Tie: Examining the Role of Interpersonal Relationships in Social Networkscited by 4
2 more
  1. 2024Where on Earth Do Users Say They Are?: Geo-Entity Linking for Noisy Multilingual User Inputcited by 4
  2. 2021COVID-19 Vaccine and Social Media: Exploring Emotions and Discussions on Twittercited by 1

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