ArticleIntegrated environmental assessment and management2025
Content hubs, information flows, and reactions for pesticide-related discussions on Twitter/X.
Article in Integrated environmental assessment and management, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.