Papers › Twitter Homophily: Network Based Prediction of User's Occupation
Twitter Homophily: Network Based Prediction of User's Occupation
Jiaqi Pan, Rishabh Bhardwaj, Wei Lu, Hai Leong Chieu, Xinghao Pan, Ni Yi Puay
In this paper, we investigate the importance of social network information compared to content information in the prediction of a Twitter user{'}s occupational class. We show that the content information of a user{'}s tweets, the profile descriptions of a user{'}s follower/following community, and the user{'}s social network provide useful information for classifying a user{'}s occupational group. In our study, we extend an existing data set for this problem, and we achieve significantly better performance by using social network homophily that has not been fully exploited in previous work. In our analysis, we found that by using the graph convolutional network to exploit social homophily, we can achieve competitive performance on this data set with just a small fraction of the training data.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections