{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T05:06:52Z","timestamp":1768453612850,"version":"3.49.0"},"reference-count":26,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T00:00:00Z","timestamp":1745452800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJERPH"],"abstract":"<jats:p>The COVID-19 pandemic intensified concerns about misinformation, sparking interest in the field of infodemiology, which examines the spread and impact of information on public health perceptions. This research examines how geographic location influenced COVID-19 discourse across 10 Italian cities by analyzing geographically tagged Twitter data. Our network analysis of 4792 high-degree nodes identifies key information spreaders and community structures, while spatiotemporal mapping reveals regional variations in information patterns and influential narratives. Results demonstrate significant geographic and cultural influences on public discourse. In Milan and Rome, economic and political narratives dominated, suggesting targeted messaging about economic recovery and government transparency. Southern regions like Naples require trust-building through community-led initiatives addressing cultural health beliefs. The study identified a clear dichotomy among influencers: established public figures provided evidence-based information, while another group cultivated followings through conspiracy theories, creating echo chambers for skeptical views. This research informs strategies for location-specific information campaigns, helping public health agencies combat misinformation more effectively. Findings emphasize the need for context-specific interventions that consider geographic, cultural, and socioeconomic factors to enhance community resilience during health emergencies.<\/jats:p>","DOI":"10.3390\/ijerph22050668","type":"journal-article","created":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T09:26:58Z","timestamp":1745486818000},"page":"668","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Mapping Infodemic Responses: A Geospatial Analysis of COVID-19 Discourse on Twitter in Italy"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7271-0250","authenticated-orcid":false,"given":"Gabriela","family":"Fernandez","sequence":"first","affiliation":[{"name":"Metabolism of Cities Living Laboratory, Center for Human Dynamics in the Mobile Age, Department of Geography, San Diego State University, 5500 Campanile Drive, San Diego, CA 92182, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6878-1138","authenticated-orcid":false,"given":"Siddharth","family":"Suresh-Babu","sequence":"additional","affiliation":[{"name":"Metabolism of Cities Living Laboratory, Center for Human Dynamics in the Mobile Age, Department of Geography, San Diego State University, 5500 Campanile Drive, San Diego, CA 92182, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7659-9361","authenticated-orcid":false,"given":"Domenico","family":"Vito","sequence":"additional","affiliation":[{"name":"Metabolism of Cities Living Laboratory, Center for Human Dynamics in the Mobile Age, Department of Geography, San Diego State University, 5500 Campanile Drive, San Diego, CA 92182, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cinelli, M., Quattrociocchi, W., Galeazzi, A., Valensise, C.M., Brugnoli, E., Schmidt, A.L., Zola, P., Zollo, F., and Scala, A. 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