Twitter Homophily: Network Based Prediction of User’s Occupation

Jiaqi Pan, Rishabh Bhardwaj, Wei Lu, Hai Leong Chieu, Xinghao Pan, Ni Yi Puay


Abstract
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.
Anthology ID:
P19-1252
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2633–2638
Language:
URL:
https://aclanthology.org/P19-1252
DOI:
10.18653/v1/P19-1252
Bibkey:
Cite (ACL):
Jiaqi Pan, Rishabh Bhardwaj, Wei Lu, Hai Leong Chieu, Xinghao Pan, and Ni Yi Puay. 2019. Twitter Homophily: Network Based Prediction of User’s Occupation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2633–2638, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Twitter Homophily: Network Based Prediction of User’s Occupation (Pan et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1252.pdf
Software:
 P19-1252.Software.zip
Code
 jqnap/Twitter-Occupation-Prediction