Scheda di dettaglio – i prodotti della ricerca
| Dato | Valore |
|---|---|
| Title | A Framework for Perception Analysis of Social Media Data During Disease Outbreaks: Uncovering Patterns of Resentment Towards Bats |
| Abstract | 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. |
| Source | 57th Hawaii International Conference on System Sciences, Hawaii, 3-6/1/2024 |
| Keywords | batsdiseasesentiment analysis |
| Year | 2024 |
| Type | Contributo in atti di convegno |
| Authors | Okpala, Izunna and Romera Rodriguez, Guillermo and Han, Chaeeun and Meierhofer, Melissa and Mammola, Stefano and Halse, Shane and Kropczynski, Jess and Johnson, Joseph |
| Text | 491190 2024 bats disease sentiment analysis A Framework for Perception Analysis of Social Media Data During Disease Outbreaks Uncovering Patterns of Resentment Towards Bats Okpala, Izunna and Romera Rodriguez, Guillermo and Han, Chaeeun and Meierhofer, Melissa and Mammola, Stefano and Halse, Shane and Kropczynski, Jess and Johnson, Joseph University of Cincinnati, Penn State University, University of Helsinki, National Research Council 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. Proceedings of the 57th Hawaii International Conference on System Sciences Published version https //hdl.handle.net/10125/106683 57th Hawaii International Conference on System Sciences Hawaii 3 6/1/2024 Internazionale Contributo Published version 2024_OKPALA ET AL.pdf Contributo in atti di convegno stefano.mammola MAMMOLA STEFANO |