  Stance detection is a challenging task that aims to identify public opinion
from social media platforms with respect to specific targets. Previous work on
stance detection largely focused on pure texts. In this paper, we study
multi-modal stance detection for tweets consisting of texts and images, which
are prevalent in today's fast-growing social media platforms where people often
post multi-modal messages. To this end, we create five new multi-modal stance
detection datasets of different domains based on Twitter, in which each example
consists of a text and an image. In addition, we propose a simple yet effective
Targeted Multi-modal Prompt Tuning framework (TMPT), where target information
is leveraged to learn multi-modal stance features from textual and visual
modalities. Experimental results on our five benchmark datasets show that the
proposed TMPT achieves state-of-the-art performance in multi-modal stance
detection.
