@inproceedings{pan-etal-2019-twitter,
title = "{T}witter Homophily: Network Based Prediction of User{'}s Occupation",
author = "Pan, Jiaqi and
Bhardwaj, Rishabh and
Lu, Wei and
Chieu, Hai Leong and
Pan, Xinghao and
Puay, Ni Yi",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1252",
doi = "10.18653/v1/P19-1252",
pages = "2633--2638",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Twitter Homophily: Network Based Prediction of User’s Occupation
%A Pan, Jiaqi
%A Bhardwaj, Rishabh
%A Lu, Wei
%A Chieu, Hai Leong
%A Pan, Xinghao
%A Puay, Ni Yi
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F pan-etal-2019-twitter
%X 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.
%R 10.18653/v1/P19-1252
%U https://aclanthology.org/P19-1252
%U https://doi.org/10.18653/v1/P19-1252
%P 2633-2638
Markdown (Informal)
[Twitter Homophily: Network Based Prediction of User’s Occupation](https://aclanthology.org/P19-1252) (Pan et al., ACL 2019)
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.