Additional file 1 of Domain-relevance of influence: characterizing variations in online influence across multiple domains on social media
Creators: name: Shi, Bowen
name: Xu, Ke
name: Zhao, Jichang
Publisher: name: figshare
Year: 2024
Subjects: subject: Information Systems
subject: FOS: Computer and information sciences

description: Additional file 1: Table S1. Variations in the attributes of some representative samples of users across domains. All refers to considering all domains as a whole. Lower rankings are omitted to highlight the user’s cross-domain capability. Table S2. Description and examples of user groups. Table S3. The precision, recall and F-measure of the cross-validation. All refers to considering all domains as a whole. Table S4. Distribution of the number of users across user groups and domains. All refers to considering all domains as a whole. Table S5. Means and standard deviations of independent variables across user groups. All refers to considering all user groups as a whole. Table S6. Z-test of the influence (CI rank) of specialists and generalists across domains. Level of significance: ***p &lt; 0.001; **p &lt; 0.01; *p &lt; 0.05. All refers to considering all domains as a whole. Table S7. Multiple regression to predict influence (CI rank) based on cross-domain capability across domains. Level of significance: ***p &lt; 0.001; **p &lt; 0.01; *p &lt; 0.05. Lower value is better in CI rank. All refers to considering all domains as a whole. Figure S1. Probability distribution of in-degree of each domain-oriented retweet network. All refers to considering all domains as a whole. Figure S2. Retweet network of various users in the military domain. The threshold of the edge weight is set to 10 for better visualization, and the size of the node is related to its number of retweeters. We color each node by its verified type, i.e., blue represents the media, green represents enterprises, red represents the government, orange represents celebrities and gray represents others. Note that the color of the edge is the same as that of the source node. Figure S3. Influence scores (in-degree) of the top-k influentials in each domain-oriented retweet network. Figure S4. Proportion of users reached by the top-k influentials in each domain-oriented retweet network. Figure S5. Proportional distribution of specialists and generalists in the influence ranking across domains. The threshold of each single domain is set to 200 and each ranking interval is 20. All refers to considering all domains as a whole. Figure S6. Proportional distribution of specialists and generalists in the influence ranking across domains. The threshold of each single domain is set to 100 and each ranking interval is 10. All refers to considering all domains as a whole. Figure S7. Percentages of specialists and generalists across user groups. Figure S8. Variations in the influence rank index of some enterprise users across domains. All refers to considering all domains as a whole. In each single domain, we calculate the influence rank index by subtracting the user ranking from the maximum ranking value of the selected elites for better visualization. Consequently, those with a higher rank index are more influential. Figure S9. Comparison of influence (CI rank) between specialists and generalists across domains. Lower value is better in CI rank. All refers to considering all domains as a whole.