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        <title>Cross-media User Profiling with Joint Textual and Social User Embedding</title>
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        <namePart type="given">Jingjing</namePart>
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    <abstract>In realistic scenarios, a user profiling model (e.g., gender classification or age regression) learned from one social media might perform rather poorly when tested on another social media due to the different data distributions in the two media. In this paper, we address cross-media user profiling by bridging the knowledge between the source and target media with a uniform user embedding learning approach. In our approach, we first construct a cross-media user-word network to capture the relationship among users through the textual information and a modified cross-media user-user network to capture the relationship among users through the social information. Then, we learn user embedding by jointly learning the heterogeneous network composed of above two networks. Finally, we train a classification (or regression) model with the obtained user embeddings as input to perform user profiling. Empirical studies demonstrate the effectiveness of the proposed approach to two cross-media user profiling tasks, i.e., cross-media gender classification and cross-media age regression.</abstract>
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        <date>2018-08</date>
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%0 Conference Proceedings
%T Cross-media User Profiling with Joint Textual and Social User Embedding
%A Wang, Jingjing
%A Li, Shoushan
%A Jiang, Mingqi
%A Wu, Hanqian
%A Zhou, Guodong
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F wang-etal-2018-cross
%X In realistic scenarios, a user profiling model (e.g., gender classification or age regression) learned from one social media might perform rather poorly when tested on another social media due to the different data distributions in the two media. In this paper, we address cross-media user profiling by bridging the knowledge between the source and target media with a uniform user embedding learning approach. In our approach, we first construct a cross-media user-word network to capture the relationship among users through the textual information and a modified cross-media user-user network to capture the relationship among users through the social information. Then, we learn user embedding by jointly learning the heterogeneous network composed of above two networks. Finally, we train a classification (or regression) model with the obtained user embeddings as input to perform user profiling. Empirical studies demonstrate the effectiveness of the proposed approach to two cross-media user profiling tasks, i.e., cross-media gender classification and cross-media age regression.
%U https://aclanthology.org/C18-1119
%P 1410-1420

Markdown (Informal)

[Cross-media User Profiling with Joint Textual and Social User Embedding](https://aclanthology.org/C18-1119) (Wang et al., COLING 2018)

• Cross-media User Profiling with Joint Textual and Social User Embedding (Wang et al., COLING 2018)

ACL

• Jingjing Wang, Shoushan Li, Mingqi Jiang, Hanqian Wu, and Guodong Zhou. 2018. Cross-media User Profiling with Joint Textual and Social User Embedding. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1410–1420, Santa Fe, New Mexico, USA. Association for Computational Linguistics.