Content hubs, information flows, and reactions for pesticide-related discussions on Twitter/X

被引:0
|
作者
Zhou, Tiancheng [1 ]
Huang, Yu [2 ]
Avanasi, Raghavendhran [3 ]
Brain, Richard A. [3 ]
Prosperi, Mattia [4 ]
Bian, Jiang [2 ]
机构
[1] Univ Florida, Dept Hlth Outcomes & Biomed Informat, Gainesville, FL 32611 USA
[2] Indiana Univ, Dept Biostat & Hlth Data Sci, Bloomington, IN USA
[3] Syngenta Crop Protect LLC, Greensboro, NC USA
[4] Univ Florida, Dept Epidemiol, Gainesville, FL USA
关键词
machine learning; pesticide perceptions; information dissemination; sentiment analysis; Twitter/X; SOCIAL MEDIA; COMMUNITY;
D O I
10.1093/inteam/vjaf032
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Pesticides are essential in modern agriculture for controlling pests and enhancing food production. However, concerns about their human and environmental health impacts have broadened discussions on their use, regulation, ethics, and sustainability. Scientific research, media coverage, and input from corporations, governments, and nongovernmental organizations (NGOs) shape public opinions and potentially influence regulatory decisions. This project analyzed pesticide-related discussions on Twitter/X from 2013 to 2022, focusing on information influence and propagation among individuals and organizations, advancing over prior research that looked at topic frequency, trends, and geography. Using a validated snowball sampling method, we collected over 3 million tweets from 1 million users and identified key network influencers, i.e., information hubs, analyzing their content, popularity, and characteristics. Machine learning and a tailored information flow score were used to explore the dynamics of information flow and sentiment across hubs. Our analysis revealed that organizational hubs, particularly NGOs and media, were more active and had higher follower-to-following ratios than individual influencers. Media and NGOs also dominated the pesticide-related discourse, while individual influencers had a lesser role. Information sources were unevenly distributed, with a dominance of retweets, news, and media posts, and a low prevalence of scientific sources. Information flow was high through NGOs, academia, and individuals, but poor from government accounts. Pesticide-focused hubs were more active and targeted in their information dissemination, with public sentiment largely negative. By delving deeper into the dynamics of information dissemination and influence networks, this study provides insights that emphasize (1) the need for better communication strategies to integrate diverse stakeholder perceptions and values, and (2) prioritizing the dissemination of credible scientific information, while also addressing sectoral disparities. Together, they can help policymakers and industry stakeholders build trust, promote transparency, and advance sustainable pesticide regulation. Key points NGOs and media organizations were the most influential hubs in pesticide-related discussions on Twitter/X, dominating the discourse with high engagement and follower-to-following ratios.Information dissemination was most effective through NGOs, academia, and individuals, while government accounts exhibited limited engagement and influence within the network.Retweets, news articles, and media posts were the primary sources of information, with less representation of scientific literature, highlighting a gap in the dissemination of credible research-based content.Public sentiment toward pesticide-related discussions was predominantly negative, with media and individual influencers facing the highest negative reception compared to other groups.
引用
收藏
页数:11
相关论文
共 14 条
  • [1] Evaluating the perceptions of pesticide use, safety, and regulation and identifying common pesticide-related topics on Twitter
    Jun, Inyoung
    Feng, Zheng
    Avanasi, Raghavendhran
    Brain, Richard A.
    Prosperi, Mattia
    Bian, Jiang
    INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT, 2023, 19 (06) : 1581 - 1599
  • [2] CONSUMER FOOD SAFETY CONCERNS AND INTEREST IN PESTICIDE-RELATED INFORMATION
    BRUHN, CM
    DIAZKNAUF, K
    FELDMAN, N
    HARWOOD, J
    HO, G
    IVANS, E
    KUBIN, L
    LAMP, C
    MARSHALL, M
    OSAKI, S
    STANFORD, G
    STEINBRING, Y
    VALDEZ, I
    WILLIAMSON, E
    WUNDERLICH, E
    JOURNAL OF FOOD SAFETY, 1992, 12 (03) : 253 - 262
  • [3] Ice Flavor-Related Discussions on Twitter: Content Analysis
    Galimov, Artur
    Vassey, Julia
    Galstyan, Ellen
    Unger, Jennifer B.
    Kirkpatrick, Matthew G.
    Allem, Jon-Patrick
    JOURNAL OF MEDICAL INTERNET RESEARCH, 2022, 24 (11)
  • [4] Content Analysis of Idiopathic Pulmonary Fibrosis-Related Information on Twitter
    Ong, S. R.
    Koo, J.
    Johannson, K. A.
    Ryerson, C. J.
    Goobie, G. C.
    AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE, 2022, 205
  • [5] Content Analysis of Idiopathic Pulmonary Fibrosis-related Information on Twitter
    Ong, Shaun
    Koo, John
    Johannson, Kerri A.
    Ryerson, Christopher J.
    Goobie, Gillian C.
    ATS SCHOLAR, 2022, 3 (04): : 576 - 587
  • [6] Development of a new methodology to harmonize information on pesticide-related poisoning exposures and to support comparable reporting from EU member states
    Settimi, Laura
    Orford, Rob
    Duarte-Davidson, Raquel
    Hague, Charlotte
    Andrew, Erik
    Mathieu-Nolf, Monique
    Dragelyte, Gabija
    Badaras, Robertas
    Pelclova, Daniela
    Zakharov, Sergey
    Desel, Herbert
    Ebbecke, Martin
    Davanzo, Franca
    Sesana, Fabrizio
    Adams, Richard
    CLINICAL TOXICOLOGY, 2014, 52 (04) : 426 - 426
  • [7] Identifying Health-Related Discussions of Cannabis Use on Twitter by Using a Medical Dictionary: Content Analysis of Tweets
    Allem, Jon-Patrick
    Majmundar, Anuja
    Dormanesh, Allison
    Donaldson, Scott, I
    JMIR FORMATIVE RESEARCH, 2022, 6 (02)
  • [8] Does user activity promote gambling-related content on Twitter/X?
    Lerma, Marcos
    Pfund, Rory A.
    Whelan, James P.
    COMPUTERS IN HUMAN BEHAVIOR, 2024, 158
  • [9] Content analysis of multi-annual time series of flood-related Twitter (X) data
    Veigel, Nadja
    Kreibich, Heidi
    de Bruijn, Jens A.
    Aerts, Jeroen C. J. H.
    Cominola, Andrea
    NATURAL HAZARDS AND EARTH SYSTEM SCIENCES, 2025, 25 (02) : 879 - 891
  • [10] Description of Weight-Related Content and Recommended Dietary Behaviors for Weight Loss Frequently Reposted on X (Twitter) in English and Japanese: Content Analysis
    Oono, Fumi
    Matsumoto, Mai
    Ogata, Risa
    Suga, Mizuki
    Murakami, Kentaro
    JOURNAL OF MEDICAL INTERNET RESEARCH, 2025, 27