Research

Police violence against Black Americans is a persistent crisis characterized by volatile cycles of public attention, often sparked by high-profile incidents. Sustained focus is essential for systemic change, yet a fragmented media landscape overlooks most incidents. This study investigates the mechanisms behind this volatility by analyzing how the use of victims' names facilitates symbolic prototyping and alters news framing dynamics. Replicating and extending prior research, we examine coverage from 2019–2024 using GDELT's Global Knowledge Graph. Results show pronounced, short-lived surges in news volume and thematic framing following the deaths of George Floyd and Daunte Wright. Although these events triggered a thematic shift, framing was anchored almost exclusively to their names, suggesting that they functioned as symbolic prototypes for subsequent incidents. Our results prompt a reconsideration of the issue-attention cycle in a digital-first media environment. They also challenge previous conceptualizations of thematic framing and key events.

Drawing from the humanities, cognitive science, and computer science, visual communication research has historically lacked a unified synthesis of its disparate intellectual roots. By providing the first systematic citation analysis of the field, this study maps this heterogeneous evolution, offering a framework to guide future inquiries into both human- and AI-generated imagery. Specifically, we reveal the intellectual structure of visual communication research (1975–2024) using a systematic review and co-citation network analysis of 17,304 articles from Communication journals. We leverage the Web of Science Core Collection to analyze the citation patterns of core references, classifying them by disciplinary origin to reveal the field's interdisciplinary composition. The analysis identifies seventeen distinct intellectual communities, including Semiotics & Multimodality, Visual Framing, Deep Learning, and Virtual Reality & Presence. Our results indicate that visual communication is a converged constellation of intellectual clusters, but still largely segregated. Qualitative (humanities-based) and quantitative (computation-based) approaches largely remain homogeneous, with few central nodes bridging these distinct theoretical and methodological traditions, perhaps undermining the field's ability to forge the integrated approaches required to study a multimodal media landscape increasingly driven by AI-generated content.

TikTok has emerged as a key platform for discussing polarizing topics, including climate change. Despite its growing influence, there is limited research exploring how content features shape emotional alignment between video creators and audience comments, as well as their impact on user engagement. Using a combination of pretrained and fine-tuned textual and visual models, we analyzed 7,110 TikTok videos related to climate change, focusing on content features such as semantic clustering of video transcriptions, visual elements, tonal shifts, and detected emotions. Our findings reveal that positive emotions and videos featuring factual content or vivid environmental visuals exhibit stronger emotional alignment. Furthermore, emotional intensity and tonal coherence in video speech are significant predictors of higher engagement levels, offering new insights into the dynamics of climate change communication on social media. Our preference learning analysis reveals that comment emotions play a dominant role in predicting video shareability, with both positive and negative emotional responses acting as key drivers of content diffusion. We conclude that user engagement — particularly emotional discourse in comments — significantly shapes climate change content shareability.