# Microblog Hashtag Generation via Encoding Conversation Contexts

arXiv: https://arxiv.org/abs/1905.07584 · HTML: https://mlsanity.com/paper/1905.07584/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2019.

## Built on by (most cited first, 10 of 10)

- [Open Vocabulary Extreme Classification Using Generative Models](https://mlsanity.com/paper/2205.05812/) (2022)
- [Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations](https://mlsanity.com/paper/2106.09896/) (2021)
- [Understanding Social Media Cross-Modality Discourse in Linguistic Space](https://mlsanity.com/paper/2302.13311/) (2023)
- [Hashtag-Guided Low-Resource Tweet Classification](https://mlsanity.com/paper/2302.10143/) (2023)
- [Borrowing Human Senses: Comment-Aware Self-Training for Social Media Multimodal Classification](https://mlsanity.com/paper/2303.15016/) (2023)
- [RIGHT: Retrieval-augmented Generation for Mainstream Hashtag Recommendation](https://mlsanity.com/paper/2312.10466/) (2023)
- [News Meets Microblog: Hashtag Annotation via Retriever-Generator](https://mlsanity.com/paper/2104.08723/) (2021)
- [Attend and select: A segment selective transformer for microblog hashtag generation](https://mlsanity.com/paper/2106.03151/) (2021)
- [Abstractive Opinion Tagging](https://mlsanity.com/paper/2101.06880/) (2021)
- [Boosting Multi-modal Keyphrase Prediction with Dynamic Chain-of-Thought in Vision-Language Models](https://mlsanity.com/paper/2510.09358/) (2025)

## Abstract (arXiv)

Automatic hashtag annotation plays an important role in content understanding for microblog posts. To date, progress made in this field has been restricted to phrase selection from limited candidates, or word-level hashtag discovery using topic models. Different from previous work considering hashtags to be inseparable, our work is the first effort to annotate hashtags with a novel sequence generation framework via viewing the hashtag as a short sequence of words. Moreover, to address the data sparsity issue in processing short microblog posts, we propose to jointly model the target posts and the conversation contexts initiated by them with bidirectional attention. Extensive experimental results on two large-scale datasets, newly collected from English Twitter and Chinese Weibo, show that our model significantly outperforms state-of-the-art models based on classification. Further studies demonstrate our ability to effectively generate rare and even unseen hashtags, which is however not possible for most existing methods.