A Framework for Perception Analysis of Social Media Data During Disease Outbreaks: Uncovering Patterns of Resentment Towards Bats

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dc.contributor.authorOkpala, Izunna
dc.contributor.authorRomera Rodriguez, Guillermo
dc.contributor.authorHan, Chaeeun
dc.contributor.authorMeierhofer, Melissa
dc.contributor.authorMammola, Stefano
dc.contributor.authorHalse, Shane
dc.contributor.authorKropczynski, Jess
dc.contributor.authorJohnson, Joseph
dc.date.accessioned2023-12-26T18:38:37Z
dc.date.available2023-12-26T18:38:37Z
dc.date.issued2024-01-03
dc.identifier.doi10.24251/HICSS.2023.301
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.othera6ffd3f4-e9b6-4247-acd4-4d6eec07efc9
dc.identifier.urihttps://hdl.handle.net/10125/106683
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData Analytics, Data Mining, and Machine Learning for Social Media
dc.subjectbats
dc.subjectdecision-making tools
dc.subjectnatural language processing
dc.subjectperception analysis
dc.subjecttext analysis
dc.titleA Framework for Perception Analysis of Social Media Data During Disease Outbreaks: Uncovering Patterns of Resentment Towards Bats
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractDespite the growing number of natural language processing (NLP) tools developed for decision-makers to leverage social media for public perception evaluation during crises, a more robust framework is needed. This study explores a domain-specific machine learning framework for perception analysis using tweets about bats during disease outbreaks as a case study. Zoonotic disease outbreaks such as COVID-19 and Ebola are often attributed to bats and have resulted in unnecessary culling of wildlife; therefore, this is a case where perception is meaningful to a species. Analysis of 15,968 tweets showed a pattern in which tweets with anti-bat perceptions were most common during the early phases of an outbreak but declined over time while remaining negative, with 87.6% reliability of the framework according to manual coding of 300 randomly selected tweets. The framework can help stakeholders understand trends in public perception in near real-time and guide responses to spreading misinformation.
dcterms.extent10 pages
prism.startingpage2475

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