#HowYouTagTweets: Learning User Hashtagging Preferences via Personalized Topic Attention

Yuji Zhang, Yubo Zhang, Chunpu Xu, Jing Li, Ziyan Jiang, Baolin Peng


Abstract
Millions of hashtags are created on social media every day to cross-refer messages concerning similar topics. To help people find the topics they want to discuss, this paper characterizes a user’s hashtagging preferences via predicting how likely they will post with a hashtag. It is hypothesized that one’s interests in a hashtag are related with what they said before (user history) and the existing posts present the hashtag (hashtag contexts). These factors are married in the deep semantic space built with a pre-trained BERT and a neural topic model via multitask learning. In this way, user interests learned from the past can be customized to match future hashtags, which is beyond the capability of existing methods assuming unchanged hashtag semantics. Furthermore, we propose a novel personalized topic attention to capture salient contents to personalize hashtag contexts. Experiments on a large-scale Twitter dataset show that our model significantly outperforms the state-of-the-art recommendation approach without exploiting latent topics.
Anthology ID:
2021.emnlp-main.616
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7811–7820
Language:
URL:
https://aclanthology.org/2021.emnlp-main.616
DOI:
10.18653/v1/2021.emnlp-main.616
Bibkey:
Cite (ACL):
Yuji Zhang, Yubo Zhang, Chunpu Xu, Jing Li, Ziyan Jiang, and Baolin Peng. 2021. #HowYouTagTweets: Learning User Hashtagging Preferences via Personalized Topic Attention. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7811–7820, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
#HowYouTagTweets: Learning User Hashtagging Preferences via Personalized Topic Attention (Zhang et al., EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.616.pdf
Code
 polyusmart/personalized-hashtag-preferences