{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/unipoll-a-unified-social-media-poll","title":"UniPoll: A Unified Social Media Poll Generation Framework via Multi-Objective Optimization","arxiv_id":"2306.06851","date":"2023-06-12","proceeding":null,"authors":["Yixia Li","Rong Xiang","Yanlin Song","Jing Li"],"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.","url_abs":"https://arxiv.org/abs/2306.06851v2","url_pdf":"https://arxiv.org/pdf/2306.06851v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"unipoll-a-unified-social-media-poll","repo_url":"https://github.com/X1AOX1A/UniPoll","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"answer-generation","task_name":"Answer Generation"},{"task_slug":"poll-generation","task_name":"Poll Generation"},{"task_slug":"question-generation","task_name":"Question Generation"},{"task_slug":"rag","task_name":"RAG"},{"task_slug":"retrieval-augmented-generation","task_name":"Retrieval-augmented Generation"},{"task_slug":"synthetic-data-generation","task_name":"Synthetic Data Generation"}],"methods":[{"method_slug":"adafactor","method_name":"Adafactor"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"glu","method_name":"Gated Linear Unit"},{"method_slug":"inverse-square-root-schedule","method_name":"Inverse Square Root Schedule"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"sentencepiece","method_name":"SentencePiece"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"t5","method_name":"T5"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/answer-generation-on-weibopolls","task":"Answer Generation","dataset":"WeiboPolls","model":"UniPoll","rank_in_archive_order":1,"of":3,"metrics":{"BLEU-1":"37.87","BLEU-3":"25.74","ROUGE-1":"46.24","ROUGE-L":"43.34"},"uses_additional_data":false},{"leaderboard":"/sota/poll-generation-on-weibopolls","task":"Poll Generation","dataset":"WeiboPolls","model":"UniPoll","rank_in_archive_order":1,"of":3,"metrics":{"BLEU-1":"39.96","BLEU-3":"22.78","ROUGE-1":"47.92","ROUGE-L":"45.02"},"uses_additional_data":false},{"leaderboard":"/sota/question-generation-on-weibopolls","task":"Question Generation","dataset":"WeiboPolls","model":"UniPoll","rank_in_archive_order":1,"of":3,"metrics":{"BLEU-1":"42.04","BLEU-3":"19.83","ROUGE-1":"49.6","ROUGE-L":"46.71"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2306.06851","atlas_url":"https://app.syntology.ai/?focus=2306.06851","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}