The anatomy of a suggestion: what triggers public to express suggestive opinions on pre-release policies on social media?

Xiaodong Feng et al.

Aslib Journal of Information Management2026https://doi.org/10.1108/ajim-05-2025-0302article
AJG 1
Weight
0.50

What the paper says

Purpose Collecting online opinions, particularly suggestive opinions on pre-release policies, is crucial for the government's informed policy-making. Grounded in social cognition theories, this study explores how public social attributes, cognitive experience and environmental factors influence the expression of suggestive opinions on such pre-release policies. Design/methodology/approach We collected a dataset with 37,087 online comments on 12 selected pre-release policies and 1,059,019 microblogs by commenting users on Sina Weibo in China. Using text mining techniques, we extracted relevant features and applied a two-stage regression analysis alongside explainable machine learning to identify factors influencing suggestive opinion expression. Findings Females, individuals possessing more extensive social experience, and users who frequently convey negative sentiments exhibit a higher propensity to post suggestive opinions. Environmental factors, particularly existing suggestive opinions, exert a significant promoting effect on the subsequent expression of suggestions. Moreover, there exists a time decay effect in the expression of suggestive opinions, and these effects are more prominent among females compared to males. Originality/value While prior research has explored determinants of general online information behaviors and examined sentiment analysis, this study seeks to uniquely investigate the factors driving suggestive opinion expression, as opposed to other opinion types.

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https://doi.org/https://doi.org/10.1108/ajim-05-2025-0302

Or copy a formatted citation

@article{xiaodong2026,
  title        = {{The anatomy of a suggestion: what triggers public to express suggestive opinions on pre-release policies on social media?}},
  author       = {Xiaodong Feng et al.},
  journal      = {Aslib Journal of Information Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/ajim-05-2025-0302},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.