Stance-Driven Multimodal Controlled Statement Generation: New Dataset and Task

• URL: http://arxiv.org/abs/2504.03295v1
• Date: Fri, 04 Apr 2025 09:20:19 GMT
• Title: Stance-Driven Multimodal Controlled Statement Generation: New Dataset and Task
• Authors: Bingqian Wang, Quan Fang, Jiachen Sun, Xiaoxiao Ma,
• Abstract summary: We study the new problem of stance-driven controllable content generation for tweets with text and images.<n>We create the Multimodal Stance Generation dataset (StanceGen2024), the first resource explicitly designed for multimodal stance-controllable text generation in political discourse.<n>We propose a Stance-Driven Multimodal Generation framework that integrates weighted fusion of multimodal features and stance guidance to improve semantic consistency and stance control.
• Score: 14.63475566746729
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Formulating statements that support diverse or controversial stances on specific topics is vital for platforms that enable user expression, reshape political discourse, and drive social critique and information dissemination. With the rise of Large Language Models (LLMs), controllable text generation towards specific stances has become a promising research area with applications in shaping public opinion and commercial marketing. However, current datasets often focus solely on pure texts, lacking multimodal content and effective context, particularly in the context of stance detection. In this paper, we formally define and study the new problem of stance-driven controllable content generation for tweets with text and images, where given a multimodal post (text and image/video), a model generates a stance-controlled response. To this end, we create the Multimodal Stance Generation Dataset (StanceGen2024), the first resource explicitly designed for multimodal stance-controllable text generation in political discourse. It includes posts and user comments from the 2024 U.S. presidential election, featuring text, images, videos, and stance annotations to explore how multimodal political content shapes stance expression. Furthermore, we propose a Stance-Driven Multimodal Generation (SDMG) framework that integrates weighted fusion of multimodal features and stance guidance to improve semantic consistency and stance control. We release the dataset and code (https://anonymous.4open.science/r/StanceGen-BE9D) for public use and further research.

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