ArticleHeliyon2023
Detection of opinion leaders: Static vs. dynamic evaluation in online learning communities.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Understanding Social Support and Opinion Leaders in a Tuberculosis-Related Online Community in China: Content and Network Analyses.JMIR infodemiology · 2026Article
- Three-Stage Cascade Information Attenuation for Opinion Dynamics in Social Networks.Entropy (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Opinion leaders play a critical role in maintaining the functioning of online communities. This study aims to detect opinion leaders in online learning communities by evaluating the influence of users within the community. We use Baidu Post Bar's Python learning community as an example and employ the catastrophe progression method to statically evaluate the influence of users in three dimensions: user creativity, user posting quality, and user online interaction. Based on this, we introduce the dual-incentive control line to dynamically evaluate users' influence from 2016 to 2020 regarding speed change characteristics, thus scientifically detecting opinion leaders in online learning communities. Compared to the static evaluation method, the results show that our proposed dynamic evaluation method can more effectively reveal the dynamic development trend of users' influence, thus accurately detecting opinion leaders. Moreover, this "invisible" development trend is fully reflected in the setting of the dual-incentive control line.
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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.