Citation

Jun I, Feng Z, Avanasi R, Brain RA, Prosperi M, Jiang B. Integr. Environ. Assess. Manag. 2023; ePub(ePub): ePub.

Copyright

(Copyright © 2023, Society of Environmental Toxicology and Chemistry)

DOI

10.1002/ieam.4777

PMID

37070476

Abstract

Synthetic pesticides are important agricultural tools that increase crop yield and help feed the world's growing population. These products are also highly regulated to balance benefits and potential environmental and human risks. Public perception of pesticide use, safety, and regulation is an important topic necessitating discussion across a variety of stakeholders from lay consumers to regulatory agencies since attitudes toward this subject could differ markedly. Individuals and organizations can perceive the same message(s) about pesticides differently due to prior differences in technical knowledge, perceptions, attitudes, and individual or group circumstances. Social media platforms, like Twitter, include both individuals and organizations and function as a townhall where each group promotes their topics of interest, shares their perspectives, and engages in both well-informed and mis-informed discussions. We analyzed public Twitter posts about pesticides by user group, time, and location to understand their communication behaviors, including their sentiments and discussion topics using machine learning-based text analysis methods. We extracted tweets related to pesticides between 2013 and 2021 based on relevant keywords developed through a 'snowball' sampling process. Each tweet was grouped into individual vs. organizational groups, then further categorized into media, government, industry, academia, and three types of non-governmental organizations. We compared topic distributions within and between those groups using topic modeling, and then applied sentiment analysis to understand the public's attitudes toward pesticide safety and regulation. Individual accounts expressed concerns about health and environmental risks, while industry and government accounts focused on agricultural usage and regulations. Public perceptions are heavily skewed towards negative sentiments, although this varies geographically. Our findings can help managers and decision-makers understand public sentiments, priorities, and perceptions, and provide insights into public discourse on pesticides.

Language: en

Keywords

natural language processing; pesticides perception; sentiment analysis; topic modeling; twitter