Topic-Guided Self-Introduction Generation for Social Media Users (2023)

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.

## introduction the irresistible popularity of social media results in an explosive number of users, creating and broadcasting massive amounts of content every day. although it exhibits rich resources for users to build connections and share content, the sheer quantities of users might hinder one from finding those they want to follow @xcite . to enable users to quickly know each other, many social platforms encourage a user to write a self-introduction, a sentence to overview their personal interests. a self-introduction is part of a self-described profile, which may else include locations, selfies, user tags, and so forth, and is crucial in online user figure 1 : twitter user u with a self-introduction on the top, followed by the previous tweets (user history). u exhibits a mixture of personal interests in delaware, invertebrates, paleontology, museum, and others. despite substantial efforts made in profiling users, most existing work @xcite @xcite focuses on extracting keywords from user history and producing tag-level user attributes (e.g., interests, ages, and personality), which may later charac-terize personalization and recommendation @xcite . however, taglevel attributes profile a user through a fragmented view, while human readers may find it difficult to read. on the contrary, we automate the writing of a sentence-level self-introduction via language generation, providing a more natural and easy-tounderstand way to warm up social interactions. it consequently will enable a better socializing experience and user engagement in social media. to practically train nlp models with capabilities in self-introduction writing, we collect a large-scale twitter dataset with 170k public users. each user presents a self-introduction (manually written by themselves) and previous tweets in their history, corresponding to a total of 10.2m tweets. for methodology design, we take advantage of cutting-edge practices using pre-trained encoderdecoder for language understanding and generation. however, in real-world practice, users may post numerous tweets exhibiting lengthy content, noisy writings, and diverse interests; these may challenge existing encoder-decoder models in capturing salient personal interests and reflecting them in the brief self-introduction writing. to illustrate this challenge, figure 1 shows the self-introduction of a twitter user u and some sampled tweets from u 's user history. u exhibits a mixture of interests varying in delaware, invertebrates, paleontology, museum, and others, scatteredly indicated in multiple noisy tweets. it presents a concrete challenge for models to digest the fragmented information, distill the introduction-worthy points, and condense them into a concise, coherent, and engaging self-introduction for further interactions. moreover, existing nlp models are ineffective in encoding very long documents @xcite , whereas popular users may post numerous tweets, resulting in a lengthy history to encode. consequently, we propose a novel unified topicguided encoder-decoder (utged) framework for self-introduction generation. first, a neural topic model @xcite clusters words by statistics to learn a mixture of latent topics in characterizing user interests underlying their lengthy history. then, we inject the latent topics into a bart-based encoder and decoder @xcite ; the encoder employs topic distributions as continuous prompts @xcite @xcite to guide capturing personal interest mixture, and the decoder adopts topic words to control the writing for personalized self-introduction. in experimental results, the comparison in both automatic and human evaluation show that utged outperforms state-of-the-art encoder-decoder models without topic guidance; and ablation studies indicate the individual contribution from topicguided encoder and decoder. then, we conduct parameter analyses on topic number and topic prompt length; they are followed by the study on model performance given users varying in historical tweet number, where utged consistently performs better. finally, a case study and an error analysis interpret utged's superiority and limitations. to the best of our knowledge, we present the first nlp study on self-introduction writing from user tweeting history, where we build the first dataset for its empirical studies and show the benefits from latent topics to the state-of-the-art encoder-decoder paradigm. below are details of our contributions. ## related work our work relates to user profiling (by task formulation) and topic modeling (by methodology). user profiling. this task aims to characterize user attributes to reflect a personal view. most previous work focuses on modeling a user's tweeting history @xcite and social network interactions @xcite @xcite @xcite to predict user attribute tags (e.g., ages and interests). however, most existing work focuses on classifying user profiles into fragmented and limited tags. different from them, we study sentence-level selfintroduction and explore how nlp handles such personalized generation, which initializes the potential to profile a user via self-introduction writing. ## twitter self-introduction dataset to set up empirical studies for social media selfintroduction, we build a large-scale twitter dataset. from september 2018 to september 2019. then, we extracted the user ids therein and removed the duplicated ones. next, we gathered users' tweeting history and self-introductions via twitter api foot_1 and filtered out inactive users with less than 30 published tweets. for users with over 100 published tweets, only the latest 100 ones were kept. at last, we maintained the tweet text in english and removed irrelevant fields, e.g., images and videos. ## our utged framework here we describe our utged (unified topicguided encoder-decoder) framework. its overview is in figure 3 : latent topics guide the plms to encode user history and decode self-introductions. ## conclusion additionally, we tested a sample of 10,000 users with similarity scores fall in the ranges of [0.3,0.4), [0.2,0.3), [0.1, 0.2), [0,0.1) and results of the best model oracle a +topic on low-similarity data samples are shown in