ImageArg: A Multi-modal Tweet Dataset for Image Persuasiveness Mining
This addresses the problem of limited multi-modal persuasive data for researchers in computational argumentation and social media analysis, but it is incremental as it extends existing text-based work to images.
The authors tackled the lack of datasets for mining image persuasiveness in tweets by creating ImageArg, a multi-modal dataset with annotations based on a persuasion taxonomy, and benchmarked it with existing methods, showing it as a useful resource with room for improvement.
The growing interest in developing corpora of persuasive texts has promoted applications in automated systems, e.g., debating and essay scoring systems; however, there is little prior work mining image persuasiveness from an argumentative perspective. To expand persuasiveness mining into a multi-modal realm, we present a multi-modal dataset, ImageArg, consisting of annotations of image persuasiveness in tweets. The annotations are based on a persuasion taxonomy we developed to explore image functionalities and the means of persuasion. We benchmark image persuasiveness tasks on ImageArg using widely-used multi-modal learning methods. The experimental results show that our dataset offers a useful resource for this rich and challenging topic, and there is ample room for modeling improvement.