social media content creators

Abstract: Emotional image editing aims to change the emotion a scene conveys while preserving its essential content. Because the same scene can support different emotional interpretations, achieving an emotional goal requires visual changes grounded in the source context. Motivated by the Component Process Model, which links emotional responses to event appraisal, we investigate neural representations as a reference for these editing decisions. Using TRIBE v2, an fMRI encoding model, we analyze 400 source-target pairs across eight emotion categories. Hidden states from EmoEdit, EmoEditor, and AffectDelta correspond more strongly to predicted target-image responses in visual regions than in affect-related regions. This imbalance motivates ROI-ReFT, which combines source visual responses as a content reference with a predicted affective response change as an editing direction. Two interacting branches use both conditions to guide a frozen editor throughout generation. Predicted responses of the generated image supervise affective change and constrain visual drift. On a post hoc selected subset of 400 editing requests, ROI-ReFT achieves 91.75% target-emotion accuracy and 83.25% joint success, with the same output both matching the target emotion and reaching source-output DINO-I of at least 0.80. Our code and dataset will be publicly released.

Abstract: Acquiring diverse perspectives and forming informed opinions on societal issues are essential for critical thinking and informed decision-making. Although observing debates between opposing viewpoints can promote perspective acquisition, conventional debates require substantial time and human resources, limiting their accessibility. This study investigates whether brief AI-generated rap battle-style debates can support perspective acquisition and opinion formation on societal issues. Rap battles are debate-style performances in which opposing viewpoints are expressed through rhymed verses, making them engaging while preserving the structure of argumentative dialogue. To conduct this investigation, we automatically generated rap battle-style debates using GPT-5.4-mini and presented them to participants with synthesized audio through a web-based viewing interface. We evaluated the educational effectiveness of this approach through a user study involving six participants who viewed 40 debates generated from topics on societal issues used in the All Japan Junior and Senior High School Debate Championships. The results showed that watching approximately 1.5 minute rap battle-style debates nearly doubled the number of opinions and discussion points identified by participants. Furthermore, participants reconsidered or changed their stances in 52.5% of the evaluated cases. These findings demonstrate that even brief AI-generated rap battle-style debates can effectively support perspective acquisition and opinion formation on societal issues.

Abstract: Personalized image generation has remained narrowly focused on conditional synthesis from curated visual exemplars, rather than capturing who a user is. In practice, however, a user's personal context is much richer, comprising reviews, posts, images, captions, and metadata accumulated over time. A truly personalized generator should leverage this history to produce images aligned with the user's lifestyle and aesthetic preferences. To this end, we introduce the first unified benchmark for personalized image generation from user histories. The benchmark comprises two complementary tasks and a multi-axis evaluation protocol that assesses target fidelity, visual quality, user distinguishability, semantic alignment with the user's history, and task-specific utility. Grounded in real-world e-commerce and social media settings, the benchmark includes: (1) Personalized Scene Generation, which places a given object in a scene that reflects a user's preferences and lifestyle, motivated by personalized product presentation; and (2) Personalized Creative Generation, which generates a novel image on a specified topic that is faithful to a user's aesthetic and visual identity, motivated by social media content creation. We further propose PEARL, which couples a multimodal reasoner with a frozen image generator in an interleaved reason-reflect loop optimized with differential data reward. Across both tasks, PEARL outperforms strong baselines, achieving an average improvement of 15% across personalization metrics.