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

arXiv: https://arxiv.org/abs/2405.17710 · HTML: https://mlsanity.com/paper/2405.17710/

## 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.

## Builds on (most cited first, 14 of 14)

- [SemEval-2017 Task 4: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.00741/) (2019)
- [TimeLMs: Diachronic Language Models from Twitter](https://mlsanity.com/paper/2202.03829/) (2022)
- [Goal Driven Discovery of Distributional Differences via Language Descriptions](https://mlsanity.com/paper/2302.14233/) (2023)
- [Describing Differences between Text Distributions with Natural Language](https://mlsanity.com/paper/2201.12323/) (2022)
- [Affective Polarization and Dynamics of Information Spread in Online Networks](https://mlsanity.com/paper/2310.18553/) (2023)
- [Improving the TENOR of Labeling: Re-evaluating Topic Models for Content Analysis](https://mlsanity.com/paper/2401.16348/) (2024)
- [Confounds and Consequences in Geotagged Twitter Data](https://mlsanity.com/paper/1506.02275/) (2015)
- [Using Noisy Self-Reports to Predict Twitter User Demographics](https://mlsanity.com/paper/2005.00635/) (2020)
- [Emojis Decoded: Leveraging ChatGPT for Enhanced Understanding in Social Media Communications](https://mlsanity.com/paper/2402.01681/) (2024)
- [Pandemic Culture Wars: Partisan Differences in the Moral Language of COVID-19 Discussions](https://mlsanity.com/paper/2305.18533/) (2023)
- [Sustained Online Amplification of COVID-19 Elites in the United States](https://mlsanity.com/paper/2009.07255/) (2020)
- [More than Meets the Tie: Examining the Role of Interpersonal Relationships in Social Networks](https://mlsanity.com/paper/2105.06038/) (2021)
- [Where on Earth Do Users Say They Are?: Geo-Entity Linking for Noisy Multilingual User Input](https://mlsanity.com/paper/2404.18784/) (2024)
- [COVID-19 Vaccine and Social Media: Exploring Emotions and Discussions on Twitter](https://mlsanity.com/paper/2108.04816/) (2021)

## 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.