Recent advances in generative AI have introduced AI-extended characters, which refer to AI-generated personas grounded in pre-existing human or fictional referents. While prior research focuses on direct social interaction, their capacity to foster parasocial interaction (PSI) in media remains underexplored. We analyzed 1,460 audience comments from 299 AI talk show videos to investigate this gap. Our findings identify three distinct objects of PSI within AI-extended characters: referents, AI proxies, and blended characters. Although referents remain the primary focus, PSI toward AI proxies and blended characters suggests that audience engagement with AI media may extend beyond the original referents. We further found that humanlikeness and AI awareness appeared as recurring themes in how audiences interpreted these relationships. This work provides a preliminary understanding of human engagement with AI-extended characters and offers design implications for future AI-mediated creative media content.

Social Virtual Reality (VR) offers immersive, interactive, and engaging mechanisms for collaborative activities within virtual environments. However, interpersonal communication in social VR remains constrained by existing mediums and channels. To address this limitation, we introduce an impact-caption-inspired approach to facilitate real-time conversations in social VR. Impact captions are a type of typographic visual effects commonly employed in videos to convey verbal messages and non-verbal cues simultaneously for enhancing viewer engagement. Starting with an exploration of the design space of impact captions, we subsequently developed a proof-of-concept system, SpeechCap, that enables users to communicate through speech-driven impact captions in VR. Using the system, we conducted a user study (N=14) to assess the effectiveness of the visual and interaction design of our approach, revealing the strengths in enhancing interactivity and integrating of verbal and non-verbal information. We conclude by discussing key findings related to visual rhetoric, interactivity of communication mediums, and ambiguity, and offer design implications aimed at improving interpersonal communication in social VR.

Online video platforms have gained increased popularity due to their ability to support information consumption and sharing and the diverse social interactions they afford. Danmaku, a real-time commentary feature that overlays user comments on a video, has been found to improve user engagement, however, the use of Danmaku can lead to toxic behaviors and inappropriate comments. To address these issues, we propose a proactive moderation approach inspired by Impact Captions, a visual technique used in East Asian variety shows. Impact Captions combine textual content and visual elements to construct emotional and cognitive resonance. Within the context of this work, Impact Captions were used to guide viewers towards positive Danmaku-related activities and elicit more pro-social behaviors. Leveraging Impact Captions, we developed DanModCap, an moderation tool that collected and analyzed Danmaku and used it as input to large generative language models to produce Impact Captions. Our evaluation of DanModCap demonstrated that Impact Captions reduced negative antagonistic emotions, increased users' desire to share positive content, and elicited self-control in Danmaku social action to fostering proactive community maintenance behaviors. Our approach highlights the benefits of using LLM-supported content moderation methods for proactive moderation in a large-scale live content contexts.

Discussing research-sensemaking questions on Community Question and Answering (CQA) platforms has been an increasingly common practice for the public to participate in science communication. Nonetheless, how users strategically craft research-sensemaking questions to engage public participation and facilitate knowledge construction is a significant yet less understood problem. To fill this gap, we collected 837 science-related questions and 157,684 answers from Zhihu, and conducted a mixed-methods study to explore user-developed strategies in proposing research-sensemaking questions, and their potential effects on public engagement and knowledge construction. Through open coding, we captured a comprehensive taxonomy of question-crafting strategies, such as eyecatching narratives with counter-intuitive claims and rigorous descriptions with data use. Regression analysis indicated that these strategies correlated with user engagement and answer construction in different ways (e.g., emotional questions attracted more views and answers), yet there existed a general divergence between wide participation and quality knowledge establishment, when most questioning strategies could not ensure both. Based on log analysis, we further found that collaborative editing afforded unique values in refining research-sensemaking questions regarding accuracy, rigor, comprehensiveness and attractiveness. We propose design implications to facilitate accessible, accurate and engaging science communication on CQA platforms.

Prewriting is the process of discovering and developing ideas before writing a first draft, which requires divergent thinking and often implies unstructured strategies such as diagramming, outlining, free-writing, etc. Although large language models (LLMs) have been demonstrated to be useful for a variety of tasks including creative writing, little is known about how users would collaborate with LLMs to support prewriting. The preferred collaborative role and initiative of LLMs during such a creative process is also unclear. To investigate human-LLM collaboration patterns and dynamics during prewriting, we conducted a three-session qualitative study with 15 participants in two creative tasks: story writing and slogan writing. The findings indicated that during collaborative prewriting, there appears to be a three-stage iterative Human-AI Co-creativity process that includes Ideation, Illumination, and Implementation stages. This collaborative process champions the human in a dominant role, in addition to mixed and shifting levels of initiative that exist between humans and LLMs. This research also reports on collaboration breakdowns that occur during this process, user perceptions of using existing LLMs during Human-AI Co-creativity, and discusses design implications to support this co-creativity process.

As a popular form of online media, videos have been widely used to communicate scientific knowledge on video-sharing platforms. These science knowledge videos take advantage of rich and multi-modality information which has the potential to provoke public engagement with science knowledge and promote self-learning. However, how communicators strategically make science knowledge videos to engage viewers, and how specific communication strategies correlate with viewer engagement remain under-explored. In this paper, we first established a taxonomy of communication strategies currently used in science knowledge videos on Bilibili and then examined the correlations between communication strategies and viewers’ behavioral, emotional, and cognitive engagements measured by post-video comments. Our findings revealed the landscape of rich science communication strategies in science knowledge videos and further uncovered the correlations between these strategies and viewer engagements. We situated our results within prior research on science communication and HCI, and provided design implications for video-sharing platforms to support effective science communication.

Internet of vehicles(IOV) is an application of Internet of things in Intelligent Transport System, and has attracted high attention of researchers. IOV brings network connectivity to traditional vehicles, while also introduces security risks. This paper presents experimental analysis on the security of vehicles with Internet connections and propose an approach to Controlling a Car Through OBD Injection. In the experiments, we successfully penetrated several types of cars in a wireless way. We also put out a multi-level safety model of cars, which divides cars into different groups and gives analysis and explanations of each group. All of these things are done for indicating a point of view that traditional cars are not safe enough on information security. It is surely risky to put a car without the ability to resist the attack of informational ways into the Internet of vehicles.