Results

The main concern of this study is the threshold hypothesis of interpersonal effects, which claims that the aggregated network threshold has significant influence on the size of information diffusion. The dependent variable is the size of information diffusion. By testing the hypotheses, this study aims to shed light on our understanding about the interpersonal effects in information diffusion. To test the hypotheses, two random samples of information have been used to run linear regressions. Model 1 focuses on the sample whose diffusion size is larger than 100, and model 2 is based on the sample in which diffusion size is smaller than 100. The results have been demonstrated in Table 4.

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H1 asserts that mean threshold of interpersonal effects has significant effect on diffusion size, and such influence is curvilinear. First, as I have introduced in the measurement section, the mean value of zero-threshold ratio is 0.23, which suggests that for the diffusion of specific information, 77% of information diffusion are based on direct interpersonal sources. In model 1, there is a positive influence (Beta = 0.06, sig < 0.001); and in model 2, there exists a negative influence (Beta = -0.46, sig < 0.001). To visually demonstrate such nonlinear relationship, following the tradition of J-curve model proposed by B. S. Greenberg (1964b), I plot the scatters of mean threshold of interpersonal effects against diffusion size, and draw two smooth curves using the method of Lowess (Cleveland, 1981).

As Figure 19a illustrates, the relationship between mean threshold and diffusion size could be described as a U-shaped curve which is an approximation of the original idea of J-curve. However, it is not a perfect J-curve, given a relatively small mean threshold for popular

information (e.g., diffusion size > 100). Thus, H1a and H1b are confirmed.

H2 concerns the attributes of submitters (e.g., seed nodes). In this study, I look into whether the popularity and activity of the submitters could trigger a larger diffusion. Two measurement of popularity have been adopted: first, whether the submitter’s social identity has been verified as a celebrity, second, the number of followers. Both model 1(Beta = -0.09, sig < 0.001) and model 2 (Beta = -0.002, sig > 0.05) indicate that a verified identity as celebrity has no positive influence on diffusion size. Model 1 shows that the number of followers has no

significant influence on diffusion size (Beta = -0.023, sig > 0.05), and model 2 shows the number of followers has positive influence on diffusion size (Beta = -0.178, sig < 0.001). Thus, for H2a, only the influence of number of followers has been confirmed. On the contrary, both model 1 (Beta = 0.143, sig < 0.001) and model 2 (Beta = 0.069, sig < 0.001) indicate that the activity of submitter is positively related to the size of information diffusion, and therefore, H2b is

confirmed.

H3 argues that the number of comments is positively related with the size of information diffusion. Both model 1 (Beta = 0.529, sig < 0.001) and model 2 (Beta = 0.177, sig < 0.001) indicates that there is a scaling relationship between the number of comments and diffusion size, i.e., the logarithmic number of comments is found to be proportional to the logarithm of the diffusion size (see Figure 20). Thus, H3 is confirmed.

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H4 concerns the influence of information categories. In the regression models, the information categories are dummy variables with the category of opinion expression as the baseline group. Model 1 demonstrates that information of opinion expression can be significantly diffused to a wider audience than the information about star, product, and life encyclopedia. Model 2 also indicates the information of opinion expression is more popular than that of star. However, the diffusion size of the information in the category of opinion is not significantly different from the information of fun, movie, mood, hobby, and news. Thus, H4 is partly confirmed. Based on this finding, I can also answer research question about whether tweets of opinion expression can spread to a wider audience than the other categories of information.

H5 concerns whether the information embedded with rich media (e.g., URLs, images, videos, and emotional icons) could diffuse further. The results from model 1 indicate that only the information with URLs can significantly get more popular than information without URLs. Thus, this hypothesis is basically rejected.

H6 and H7 concern the influence of diffusion depth and duration on diffusion size. Both model 1 and model 2 confirm the strong, significant, and positive influences of diffusion depth and lifetime. Thus, H6 and H7 are both confirmed.

Further, although the nonlinear relationship can be captured by both two separated regression models and the scatter plot, the results appears to be a bit noisy. One explanation is the interpersonal effect is modified by the topological structure of diffusion networks. In this

study, I measure the penetration power of information diffusion using diffusion depth. It is measured by diffusion depth. As it will be shown that diffusion depth has significant influence on the diffusion size. To probe the moderation effect of diffusion depth, I build up the third model (see Table 4) by adding in an interaction term—Diffusion depth * Mean threshold (Log), which is negative and significant (Beta = -0.01, sig < 0.01). To visualize the moderation effect, we present Figure 19b here, and it demonstrates that with the increase of diffusion depth, the negative relationship between interpersonal effects and diffusion size is reversed, and the larger diffusion depth is, the stronger interpersonal effects on diffusion sizes are.