Abstract
Millions of users are active on social media. To allow users to better showcase themselves and network with others, we explore the auto-generation of social media self-introduction, a short sentence outlining a user’s personal interests. While most prior work profiling users with tags (e.g., ages), we investigate sentence-level self-introductions to provide a more natural and engaging way for users to know each other. Here we exploit a user’s tweeting history to generate their self-introduction. The task is non-trivial because the history content may be lengthy, noisy, and exhibit various personal interests. To address this challenge, we propose a novel unified topic-guided encoder-decoder (UTGED) framework; it models latent topics to reflect salient user interest, whose topic mixture then guides encoding a user’s history and topic words control decoding their self-introduction. For experiments, we collect a large-scale Twitter dataset, and extensive results show the superiority of our UTGED to the advanced encoder-decoder models without topic modeling.- Anthology ID:
- 2023.findings-acl.722
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 11387–11402
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.722
- DOI:
- Cite (ACL):
- Chunpu Xu, Jing Li, Piji Li, and Min Yang. 2023. Topic-Guided Self-Introduction Generation for Social Media Users. In Findings of the Association for Computational Linguistics: ACL 2023, pages 11387–11402, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Topic-Guided Self-Introduction Generation for Social Media Users (Xu et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2023.findings-acl.722.pdf