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

Izunna Okpala, Guillermo Romera Rodriguez, Chaeeun Han, Melissa Meierhofer, Stefano Mammola, Shane Halse, Jess Kropczynski, Joseph Johnson

Forskningsoutput: Kapitel i bok/rapport/konferenshandlingKonferensbidragVetenskaplig

Sammanfattning

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.
Originalspråkengelska
Titel på värdpublikationProceedings of the 57th Hawaii International Conference on System Sciences
RedaktörerTung X. Bui
Antal sidor10
UtgivningsortHonolulu
FörlagHawaii International Conference on System Sciences
Utgivningsdatum3 jan. 2024
Sidor2475-2484
ISBN (elektroniskt)978-0-9981331-7-1
StatusPublicerad - 3 jan. 2024
MoE-publikationstypB3 Ej refererad artikel i konferenshandlingar
Evenemang57th Hawaii International Conference on System Sciences - Waikiki Beach, Förenta Staterna (USA)
Varaktighet: 3 jan. 20246 mars 2024

Publikationsserier

NamnProceedings of the Annual Hawaii International Conference on System Sciences
ISSN (tryckt)1530-1605
ISSN (elektroniskt)2572-6862

Vetenskapsgrenar

  • 1181 Ekologi, evolutionsbiologi

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