Fig 1.
Hypothesis model of factors influencing communication effectiveness.
Table 1.
Basic information of 20 science and technology journal Douyin accounts.
Table 2.
Variable coding table and statistical results for short video dissemination effectiveness of scientific journals.
Table 3.
Entropy method weight calculation results for communication effectiveness metrics.
Fig 2.
Distribution Histogram and Normal Q-Q Plot of the Dissemination Effectiveness Index ().
Note: The left figure shows that the exponential distribution approximates a standard normal distribution (bell-shaped curve); the correct figure shows that the observed values closely align with the quantiles of the theoretical normal distribution, validating the effectiveness of the logarithmic transformation.
Table 4.
Variance component analysis results for the zero model of dissemination effectiveness index.
Table 5.
Descriptive statistics and normality characteristics of continuous variables (N = 4422).
Table 6.
Non-parametric tests results for path variables in science journal short video center.
Table 7.
Non-parametric tests results for peripheral route variables in science journal short videos.
Table 8.
Analysis of factors influencing the dissemination effectiveness of science and technology journal accounts (N = 18).
Fig 3.
The Moderating Effect of Follower Size on the Dissemination Effectiveness of Science Popularization Content.
Note: The figure displays results from a Simple Slope Test. Solid line (orange): Represents accounts with high follower counts (Mean + 1SD). The slope is significantly positive (Slope = 2.45, t = 4.12, p < 0.001), indicating that increasing the proportion of science communication content is associated with a sharp rise in dissemination among large accounts. Dotted line (blue): Represents accounts with low follower counts (Mean – 1SD). The slope is relatively flat and marginally significant (Slope = 1.12, t = 1.85, p = 0.08), indicating that smaller accounts face significant constraints on content reach.
Fig 4.
Moderating Effect of Account Work Count on “Industry Trends” Content.
Note: Interaction effect diagram. Solid line (green): High Work Count accounts. The steep and significant regression slope (Slope = 3.20, t = 3.56, p < 0.01) validates the trust endorsement effect from content accumulation—dotted line (red): Low Work Count accounts. The regression slope is gentle (Slope = 1.05, t = 1.22, p > 0.1), indicating that publishing industry updates without historical content accumulation struggle to gain significant algorithmic recommendation weight.
Fig 5.
Correlation heatmap of account operational strategies and dissemination effectiveness.
Note: The analysis is based on aggregated data from 18 journal accounts. The color intensity represents the strength of the Pearson correlation coefficient. “Ln(Followers)” shows the strongest positive correlation with dissemination effectiveness.
Table 9.
Comparison of original OLS regression and bootstrap verification (based on model 1: main effect of strategy).
Table 10.
Empirical results table.