UniPoll: A Unified Social Media Poll Generation Framework via Multi-Objective Optimization
This addresses the issue of low engagement from passive users on social media platforms, offering a tool to enhance inclusivity, though it appears incremental as it builds on existing NLG techniques with optimizations.
The paper tackles the problem of generating polls from social media posts to engage passive users, introducing UniPoll, which outperforms models like T5, ChatGLM3, and GPT-3.5 in generating coherent and contextually appropriate question-answer pairs across Chinese and English datasets.
Social media platforms are vital for expressing opinions and understanding public sentiment, yet many analytical tools overlook passive users who mainly consume content without engaging actively. To address this, we introduce UniPoll, an advanced framework designed to automatically generate polls from social media posts using sophisticated natural language generation (NLG) techniques. Unlike traditional methods that struggle with social media's informal and context-sensitive nature, UniPoll leverages enriched contexts from user comments and employs multi-objective optimization to enhance poll relevance and engagement. To tackle the inherently noisy nature of social media data, UniPoll incorporates Retrieval-Augmented Generation (RAG) and synthetic data generation, ensuring robust performance across real-world scenarios. The framework surpasses existing models, including T5, ChatGLM3, and GPT-3.5, in generating coherent and contextually appropriate question-answer pairs. Evaluated on the Chinese WeiboPolls dataset and the newly introduced English RedditPolls dataset, UniPoll demonstrates superior cross-lingual and cross-platform capabilities, making it a potent tool to boost user engagement and create a more inclusive environment for interaction.