UniPoll: A Unified Social Media Poll Generation Framework via Multi-Objective Optimization

TL;DR

UniPoll is a novel framework that automatically generates engaging and contextually relevant social media polls by leveraging multi-objective optimization, RAG, and synthetic data, outperforming existing models across multiple datasets.

Contribution

The paper introduces UniPoll, a comprehensive framework that improves social media poll generation by integrating advanced NLG techniques, multi-objective optimization, and noise reduction methods, addressing challenges of informal data.

Findings

Outperforms T5, ChatGLM3, GPT-3.5 in coherence and relevance.

Effective across Chinese and English social media platforms.

Enhances user engagement through context-aware poll generation.

Abstract

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.