  Social media platforms are essential outlets for expressing opinions,
providing a valuable resource for capturing public viewpoints via text
analytics. However, for many users, passive browsing is their preferred mode of
interaction, leading to their perspectives being overlooked by text analytics
methods. Meanwhile, social media polls have emerged as a practical feature for
gathering public opinions, allowing post authors to pose questions with
pre-defined answer options for readers to vote on. To broaden the benefits of
polls for posts without them, this article explores the automatic generation of
a poll from a social media post by leveraging cutting-edge natural language
generation (NLG) techniques. However, existing NLG techniques, primarily
developed for general-domain texts, may be ineffective when applied to noisy
social media data, which often feature implicit context-question-answer
relations. To tackle these challenges, we enrich a post context with its
comments and propose a novel unified poll generation framework called UniPoll.
It employs prompt tuning with multi-objective optimization to bolster the
connection exploration between contexts (posts and comments) and polls
(questions and answers). Experimental comparisons on a large-scale Chinese
Weibo dataset show that UniPoll significantly outperforms T5, the
state-of-the-art NLG model, which generates question and answer separately.
Comprehensive qualitative and quantitative analyses further underscore the
superiority of UniPoll through various evaluation lenses.
