An Image is Worth 40.38 Words: Partisanship and Attention in Videos

Political news today is increasingly consumed in attention-scarce, video-based environments. Yet existing research on media partisanship remains text-centric and implicitly assumes long-form, attentive processing. This paper studies how partisanship operates in video news through the joint transmission of multimodal signals—images and text—under limited attention. I develop a multimodal measure of video partisanship to quantify partisan content in video news and decompose the contribution of each modality. I show that the informational strength of each channel depends on attention: images convey partisanship rapidly through affective cues, while text operates through substantive information that requires sustained exposure to accumulate. A survey experiment using real news footage shows how these properties shape viewers’ responses to political videos. Partisan images elicit immediate emotional responses even under brief exposure, whereas partisan text shifts policy attitudes only with sustained exposure. Overall, the results underscore that in low-attention video environments, images are a more efficient vehicle for partisan information, operating primarily through affective channels. Efforts to improve media quality must therefore account for the asymmetric roles of visual and textual content.