A method for evaluating confidence of social media information based on time series analysis

Qiru Zi et al.

International Journal of Web Based Communities2025https://doi.org/10.1504/ijwbc.2025.145142article
ABDC C
Weight
0.50

What the paper says

In order to improve the accuracy of cross confidence assessment and shorten the time required for confidence assessment, this article proposes a social media information confidence assessment method based on time series analysis. Firstly, determine the evaluation indicators that affect the credibility of social media information. Then, quantify the evaluation indicators for the credibility of social media information. Finally, a confidence quantitative evaluation function is constructed using time series analysis, and a user information weight allocation matrix is used to configure the weight assignment scheme for each evaluation dimension. By quantitatively calculating the relative importance between various indicators in the comparison criteria layer, the user confidence is finally obtained. The experimental results show that the method proposed in this paper can effectively improve the accuracy and recall of confidence evaluation, with a FI value of 0.9, which verifies the effectiveness of the confidence evaluation method proposed in this paper.

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https://doi.org/https://doi.org/10.1504/ijwbc.2025.145142

Or copy a formatted citation

@article{qiru2025,
  title        = {{A method for evaluating confidence of social media information based on time series analysis}},
  author       = {Qiru Zi et al.},
  journal      = {International Journal of Web Based Communities},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijwbc.2025.145142},
}

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A method for evaluating confidence of social media information based on time series analysis

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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.