Topic-Guided Self-Introduction Generation for Social Media Users
This work addresses the need for more natural and engaging user profiling on social media, though it is incremental as it builds on existing encoder-decoder methods by incorporating topic modeling.
The paper tackles the problem of automatically generating social media self-introductions from users' tweeting histories, proposing a topic-guided encoder-decoder framework that outperforms advanced baseline models without topic modeling in experiments on a large-scale Twitter dataset.
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.