Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
Despite 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.
Recommended Citation
Okpala, Izunna; Romera Rodriguez, Guillermo; Han, Chaeeun; Meierhofer, Melissa; Mammola, Stefano; Halse, Shane; Kropczynski, Jess; and Johnson, Joseph, "A Framework for Perception Analysis of Social Media Data During Disease Outbreaks: Uncovering Patterns of Resentment Towards Bats" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 4.
https://aisel.aisnet.org/hicss-57/dsm/data_analytics/4
A Framework for Perception Analysis of Social Media Data During Disease Outbreaks: Uncovering Patterns of Resentment Towards Bats
Hilton Hawaiian Village, Honolulu, Hawaii
Despite 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.
https://aisel.aisnet.org/hicss-57/dsm/data_analytics/4