Computational Communication Research - Current Issue

This software demonstration article introduces transforEmotion, an open-source R package that addresses critical bottlenecks in communication research emotion analysis. Communication researchers currently face three key barriers: (1) modal fragmentation requiring separate tools for text/image/video analysis, (2) rigid emotion taxonomies that don't match theoretical frameworks, and (3) irreproducible workflows dependent on commercial APIs. transforEmotion removes these bottlenecks through unified multimodal processing, zero-shot classification with arbitrary labels, local inference capabilities, and seamless R integration. This enables systematic investigation of emotion dynamics across communication contexts and modalities that were previously technically difficult to analyze.

This study presents a theory-driven, three-stage computational framework for analyzing visual framing in protest imagery. Focusing on the Black Lives Matter movement, we examine how visual elements contribute to two well-established frames in protest media coverage: the protest paradigm and solidarity framing. Leveraging GPT-4o and OpenCV, our framework extracts denotative and semiotic features—such as police presence, contestation, solidarity actions, and color contrast—and links these features to higher-order frame classifications using interpretable logistic regression models. The framework includes: (1) feature definition and validation through generative AI and a feature extraction tool, supported by human coders; (2) model training; and (3) predictive application to unseen images. Results show strong alignment between human and machine annotations, as well as high predictive accuracy in identifying the protest paradigm or solidarity frame in BLM images. We also introduce an intra-prompt stability score for the generative AI model to help mitigate hallucination and enhance the reliability of its outputs. This study offers a scalable, replicable, and interpretable approach to visual framing analysis, bridging communication theory with advanced computational tools in the study of visual political communication.

Recently, platforms have been increasingly deploying generative AI (GenAI) to summarize user-generated content (UGC) into AI-generated summaries (AIGS). However, the potential bias in AIGS and its impact on the public remain inadequately examined. We used Weibo, a leading social media in China, as a case to investigate these important questions, focusing on public sentiments. Specifically, we explored whether AIGS are biased in representing emotions in UGC and whether such representation influences subsequent public sentiment. We empirically identify two sources of bias in the algorithmic processes underlying the production of AIGS from UGC: the sampling process, in which GenAI selects a subset of UGC, and the summarizing process, in which the summary is generated from the sampled content. Comparing emotions in AIGS, sampled UGC, and all UGC, we found evidence of bias in both processes. In our case, GenAI tends to favor positive UGC during the sampling process and produces summaries that further amplify this positivity, leading to an over-representation of positive sentiments in AIGS. Additionally, we utilized a Difference-in-Differences (DiD) design to explore AIGS in public sentiment dynamics. Findings suggest that AIGS alone are insufficient to influence public sentiment significantly. Overall, this study provides important implications for deploying GenAI in public online discussions.