ArticlePeerJ2025
Exploring the potential of online social listening for noncommunicable disease monitoring.
Article in PeerJ, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Addressing misleading medical information on social media: a scoping review of current interventions.BMJ evidence-based medicine · 2025Article
- Social Listening as a Tool to Understand Nutrition-Related Information Needs: A Case Study in Inflammatory Bowel Disease.Journal of human nutrition and dietetics : the official journal of the British Dietetic Association · 2025Article
- Exploring the potential of online social listening for noncommunicable disease monitoring.PeerJ · 2025Article
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
No grant is acknowledged in the PubMed record.
Abstract
Noncommunicable diseases (NCDs) are a significant global health challenge, claiming about 41 million lives annually. Early establishment of healthy habits is vital as childhood behaviors often persist into adulthood, affecting long-term well-being. However, pervasive health misinformation on social media exacerbates the challenge of addressing NCDs. The vast online information exposes individuals to misinformation, leading to uninformed health decisions. Countering this misinformation is crucial to promote accurate understanding and preventive strategies for NCDs, improving public health outcomes. To address this, the study proposes a system using online social listening (OSL) to collect and analyze social media data, focusing on children's nutrition, physical exercise, sleep patterns, and related NCD risk factors. This platform aids healthcare professionals in recognizing and responding to online misinformation, facilitating informed decision-making. Collaboration with parents, teachers, and healthcare providers aims to instill healthy habits in children from an early age. Utilizing the Twitter Application Programming Interface (API), the study collected data on NCDs, their risk factors, and their impact on children. Despite challenges from recent Twitter API policy changes, the methodology remains adaptable. Additionally, the study integrates diverse data sources, including traditional news outlets like PressReader, providing comprehensive coverage of health issues. Analysis comparing data from PressReader and Twitter underscores differences in discussion frequency and nature, emphasizing the need to leverage insights from various sources. The results highlight the effectiveness of the OSL system in identifying prevalent health topics, benefiting healthcare professionals. This collaborative approach positions the system as a valuable tool for addressing NCDs and promoting well-being. The study lays a foundation for future research, suggesting expansions to include additional platforms and languages, as well as advanced features like sentiment analysis.
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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.