Topic-Guided Self-Introduction Generation for Social Media Users

• URL: http://arxiv.org/abs/2305.15138v1
• Date: Wed, 24 May 2023 13:35:08 GMT
• Title: Topic-Guided Self-Introduction Generation for Social Media Users
• Authors: Chunpu Xu, Jing Li, Piji Li, Min Yang
• Abstract summary: We explore the auto-generation of social media self-introduction, a short sentence outlining a user's personal interests. Here we exploit a user's tweeting history to generate their self-introduction. We propose a novel unified topic-guided encoder-decoder framework.
• Score: 34.41343865143143
• License: http://creativecommons.org/licenses/by/4.0/
• 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 profiles 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.

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