Research on the Authenticity of Online User Comment Expression Based on Meta Analysis

Zhang Xinzhong; Xu Jian; Zhang Wenxin; Wang Yuexuan; Li Wenrui; Sheng Qingquan

doi:10.13266/j.issn.2095-5472.2025.021

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Knowledge Management Forum ›› 2025, Vol. 10 ›› Issue (4) : 321-334. DOI: 10.13266/j.issn.2095-5472.2025.021 CSTR: 32306.14.CN11-6036.2025.021

Research on the Authenticity of Online User Comment Expression Based on Meta Analysis

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Abstract

[Purpose/Significance] With the rapid growth of online users, online comments increasingly influence users’ choices and behaviors on the internet. To address the insufficient assessment of authenticity in current online comment research, this study employs meta-analysis to quantitatively evaluate the authenticity of online comments, revealing the distribution patterns of online comments, revealing the distribution patterns of inauthentic comments, and providing a reference for data reliability judgment in related studies. [Method/Process] Given the limited research on factors related to comment authenticity, this study innovatively proposed to use the proportion of inauthentic comments as a differential variable for meta-analysis. Heterogeneity tests, publication bias tests, and random-effects models were applied, combined with visualization tools to analyze inauthenticity across different platforms and comment types. [Result/Conclusion] There are generally over 15% of untrue comments, and the proportion continues to rise. The results vary depending on factors such as platform type and domestic and international environment. Among the review platforms widely selected by academia, there are at least 15% of untrue reviews on third-party review platforms (such as Dianping, Yelp, etc.) that are independent of merchants and customers; There are many untrue reviews on seller platforms independent of users (such as JD.com, Amazon, etc.), accounting for about 20%; In user built platforms without commercial attributes (such as Weibo, blogs, etc.), the number of untrue comments is the highest, accounting for more than 25%, most of which are rumors and junk comments.

Key words

meta analysis / online comments / authenticity / fake review

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Zhang Xinzhong , Xu Jian , Zhang Wenxin , et al . Research on the Authenticity of Online User Comment Expression Based on Meta Analysis[J]. Knowledge Management Forum. 2025, 10(4): 321-334 https://doi.org/10.13266/j.issn.2095-5472.2025.021

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张鑫众：撰写论文，收集与分析数据，修改论文；

徐健：提出思路，修改论文；

张雯昕：收集与整理数据，撰写论文；

王玥瑄：收集与整理数据，撰写论文；

李文睿：收集与整理数据，撰写论文；

盛清泉：收集与整理数据，撰写论文。

Funding

Guangdong Provincial Natural Science Foundation Project titled "Research on Interdisciplinary Analogous Knowledge Discovery Based on Scientific and Technological Literature Big Data"(2024A1515011778)