Toward Socially-Infused Information Extraction:Embedding Authors, Mentions, and Entities
Table 2: The average entity-driven similarity results for the networks.
['Network', '[ITALIC] sim( [ITALIC] i↔ [ITALIC] j)', '[EMPTY]']
As shown, sim(i↔j) is substantially higher than on all three social networks, indicating that socially connected individuals clearly tend to mention more similar entities than disconnected individuals. Note that is approximately equal to the same base rate defined by the average entity-driven similarity of all pairs of users, because the vast majority of user pairs are disconnected, no matter how to define the network. Among the three networks, Retweet offers slightly higher sim(i↔j) than Follower and Mention. The results verify our hypothesis of entity homophily, which forms the basis for this research. Note that all social relation data was acquired in March 2016; by this time, the authorship information of 22.1% of the tweets in the NEEL-train dataset was no longer available, because the tweets or user accounts had been deleted.