Communication, Digital Conversation, and Media Technologies

Permanent URI for this collectionhttps://hdl.handle.net/10125/112446

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Gatekeepers in an Open Market? The Case of Contemporary NFT Marketplaces
(2026-01-06) Kotha, Tejas; Bhatnagar, Kushagra; Chandra Kruse, Leona; Rossi, Matti
NFTs (non-fungible tokens) promised the interaction of artists/creators directly with their collectors without the need for any intermediaries, but the realisation was quick that such a technology, instead of getting rid of intermediaries, reintroduced new intermediaries in the form of NFT marketplaces. These marketplaces exhibit diverse features and cater to different user groups. A wide array of governance strategies, such as curation and gatekeeping, are used to steer creativity and interactions in the marketplace, informed by the marketplace's strategy. We examined this diversity by identifying the 'ideal types' of marketplaces based on these strategies alongside the motivations of the creators to make sense of the growing NFT market and constructed a typology that distinguishes four kinds of NFT marketplaces: Avant-garde, Canonical, Mass Culture, and Coterie. The article also offers practical implications for creators and collectors looking to make informed choices when deciding to participate in a particular marketplace.
Hidden by Design: Disaffordances and User Communication on WeChat
(2026-01-06) Sun, Yinan; Suthers, Dan
This qualitative study examines how the intentional omission of certain features can limit users’ communication practices on WeChat. Drawing on data from 31 semi-structured in-depth interviews, follow-up discussions, observations, and voluntary post-surveys, analyzed through grounded theory, the findings reveal seven intentional design choices and their associated disaffordances. These include the omission of features of online status, read recipient, group invitation rejection, mutual deletion, post editing after publishing, comment deletion, and the hidden text-only posting feature. This study provides insights into the factors that contribute to these designs, and how users adopt, resist, and navigate them. It also demonstrates the power dynamics between platform design, cultural norms, and user agency, offering valuable theoretical contributions to affordance theory and empirical implications for researchers and practitioners interested in social media communication and design.
Channel Expansion Theory: A Comprehensive Meta-analysis, Literature Review, and Examination of Boundary Conditions
(2026-01-06) Ou, Min; Carlson, John; Hu, An; Weng, Qin
Channel Expansion Theory (CET) has served as a foundational framework for understanding communication media selection and use since its introduction in the mid-1990s. While CET posits that individuals’ experiences with channel, partner, topic, and organizational context can expand perceptions of media richness, even for nominally “lean” media, empirical results have often been mixed across settings and constructs. This study presents a comprehensive literature review and meta-analysis of 31 quantitative studies applying CET across organizational and broader social contexts. The findings reveal when and how different forms of knowledge-building experience influence perceived media richness, media attitudes, and use behaviors, and discover important moderating effects of media synchronicity, communication setting, and power distance. This study advances our understanding of CET, resolves prior empirical inconsistencies, and provides directions for future research on technology-mediated communication and practical implications for media selection and media platform design.
Evaluating Large Vision-Language Models for Visual Framing Analysis in News Imagery: A Theory-Driven Benchmark
(2026-01-06) Lu, Linqi; Wan, Zihan; Kwon, Hyerin; Kim, Sang Jung; Kang, Jiwon; Abbas, Laila; Liu, Jiawei; Mcleod, Douglas
This study evaluates and compares the effectiveness of multiple large vision-language models (LVLMs) for automated visual framing analysis in the context of news imagery about social movements. Specifically, we evaluate LVLMs (Gemma3-27B, GPT-4.1, InternVL3-14B, InternVL3-38B, and Qwen2.5-VL-72B) against human-annotated ground truth data, using both baseline prompts and a range of Chain-of-Thought (CoT) prompting strategies with increasing complexity (i.e., from simple to detailed to expert). Model performance is assessed across visual framing categories: conflict, peace, and solidarity, using standard evaluation metrics including F1-score and Cohen’s kappa. Our findings show that (1) CoT prompting improves model alignment with human annotations across most framing categories, especially for complex social cues like solidarity; (2) expert-level CoT prompts show the highest agreement with human coders; and (3) model performance varies by the specific model in focus, with InternVL3-38B consistently outperforming others. This study provides a scalable and theory-driven framework for applying LVLMs to visual content analysis in social science research.
Acceptability of Chatbot Support for Older Adolescents Involved in Cyberbullying
(2026-01-06) Campos-Castillo, Celeste; Minchuk, Yevgenia; Laestadius, Linnea
Chatbots may be effective tools to address cyberbullying among adolescents, but little research assesses their acceptability. To address this gap, we conducted 12 focus groups with U.S. adolescents (15-18 year-olds) to determine the acceptability of a hypothetical chatbot providing support for adolescents experiencing cyberbullying. We conducted qualitative content analysis using categories from the theoretical framework for acceptability. We find adolescents generally described the chatbot as acceptable, with the idea of such an intervention conjuring positive affect and expectations that it would be effective for perpetrators and victims and reduce the burden for seeking help. However, we also find evidence adolescents would hesitate to use such a chatbot due to ethical concerns, including whether the financial interests of the chatbot developers align with the wellbeing interests of adolescents. Chatbot-driven interventions for cyberbullying appear acceptable to adolescents, but it will be important that they be developed to prioritize wellbeing over other interests.
“Is This News?”: How Audiences Recognize, Expect, and Evaluate Emerging News Genres in Short-Form Video Social Media
(2026-01-06) Mckinnon-Crowley, Jocelyn; Lua, Kian; Crowston, Kevin; Henderson, Keren
Local broadcasters are moving into short-form video (SFV) social media spaces, like TikTok or Instagram Reels, producing content in an emerging genre. While a news genre is established for broadcast TV, the news genre is not yet established on SFV platforms and is undergoing the negotiation process between what is expected, tolerated, or skipped. In this work, we investigate what audiences recognize as news on these platforms and how algorithmically fragmented audiences negotiate these emerging genres. We then conducted in-depth interviews with typical audience members using news videos adapted to the SFV platform to learn how audiences understand what news forms are recognized and what forms news takes in this medium. We found that audiences have detailed form and function expectations of videos to match their genre expectations in line with their personalized algorithms. News was rarely recognized as a distinct genre but subsumed into the “informational” genre. To a great extent, personal taste determined the fi ttingness of the stimuli videos to audience’s expectations. These data have implications for current and future newsroom production strategies.
Exploring the Association between Livestreamers’ Self-identified Gender and Their Viewers’ Linguistic Behavior
(2026-01-06) Chae, Seung Woo; Lee, Sung Hyun
This study explores how the gender of streamers on the livestreaming platform Twitch is associated with their viewers’ linguistic behavior. Based on prior literature, we specifically examine how Twitch viewers’ use of two contrasting language types—polite words and swear words—varies by their streamer’s gender. We identified eight gender-diverse Twitch streamers who played a video game together and livestreamed their game play separately on their own channels. From these eight channels, 8,296 chat messages were collected and analyzed using automated text analysis. Our results showed that, based on self-identified gender, men streamers’ viewers employed more swear words than women streamers’ viewers. Meanwhile, women streamers’ viewers used more polite words than men streamers’ viewers. Additionally, we found that the use of both polite and swear words differs significantly between viewers of cisgender and non-binary streamers, which underscores the need for researchers to collect self-identified gender information in social media research.
Thanking the Algorithm: Discovering Prosocial Communities through YouTube Music Recommendation Pathways
(2026-01-06) Gazan, Rich
What pathways through algorithmic music recommendations help users discover prosocial comment communities? Building on algorithmic awareness and music discovery literature, this exploratory, naturalistic study follows four user personas on YouTube through four music genre seed queries and ten layers of recommendation depth, to analyze the frequency and nature of the prosocial comment communities they encounter. Our results suggest that prosocial communities are accessed more frequently by personas who defy algorithmic classification in their use patterns, and that within prosocial communities users express not just awareness of the recommendation algorithm, but gratitude directed explicitly toward it. This exploratory, context-bound study contributes to understanding how users and algorithms co-construct musical meaning and community, offers methodological insights for studying algorithmic experience and recommendation pathways, and reflects on the ephemerality of prosocial communities within black-boxed discovery platforms.
Machine Heuristic at Work: User Evaluations and Folk Theories of Weather News in ‘Immersive Mixed Reality’ Video
(2026-01-06) Chen, Jin; Peng, Rachel
Immersive mixed reality (IMR) technologies are transforming news reporting by enhancing audience engagement and improving the visualization of meteorological phenomena. While these technologies offer benefits for communicating public issues through news media, their complexity may pose challenges to audience understanding and trust in the information presented. This study adopted a mixed-methods approach to investigate how users evaluate news videos produced using IMR techniques compared to the traditional text-based format, and how individual differences in machine heuristics influence evaluations and folk theories. The immersive format is more effective in enhancing users’ sense of novelty and personal relevance. Machine heuristic significantly positively moderates the effect of news format on personal relevance. While a high machine heuristic is associated with positive perceptions and the data-driven nature of technology, low machine heuristic individuals form intuitive theories that emphasize AI involvement. Implications for the use and disclosure of immersive technology in news are discussed.
Reframing China in Digital Discourse: U.S. Representations across Platforms after the TikTok Ban
(2026-01-06) Wan, Xiaotan
This study treats TikTok as an unintended arena of Chinese public diplomacy and examines how China’s image is constructed in U.S. discourse surrounding the TikTok ban. Using qualitative framing analysis, it compares elite newspapers (The New York Times, The Wall Street Journal), tech blogs (Wired, Gizmodo), and TikTok user-generated videos. Cross-platform analysis identifies six recurring frames: elite newspapers reproduce state-centric and threat-based portrayals; tech blogs introduce reflexive, platform-oriented critiques; TikTok users contribute emotionally resonant, culturally connective, and satirical expressions that diversify China’s image. The findings suggest that national image construction is becoming increasingly decentralized. In this fragmented discursive ecology, China’s public diplomacy must adapt by emphasizing affective engagement and incorporating non-state narratives.
Digitizing Intangible Cultural Heritage: An Ethnographic Exploration of Dabenqu’s Transformation into Online Engagement in Dali, China
(2026-01-06) Zhao, Liang; Liu, Huimin
This ethnographic research examines the digitalization of Dabenqu, a traditional Bai ethnic group folk singing practice, through the lens of the Zhao family’s adaptation to the opportunies posed by digital transformation. It explores how the Zhao family, as inheritors of this intangible cultural heritage, have navigated the intersection of tradition and digital innovation. The study traces the evolution of Dabenqu from its traditional village performances to its adaptation and dissemination via multiple digital platforms, including WeChat, Kuaishou, and Douyin, encompassing online performance videos, recorded rituals and ceremonies, and live streaming. The paper delves into the implications of digital media in preserving and transforming Dabenqu, highlighting both the opportunities and challenges in maintaining cultural integrity while reaching a global audience. This research offers new insights into the role of digital technologies in sustaining the vitality of this folk tradition and contributes to the broader understanding of the digitalization of intangible cultural heritage.
Inspiration Contagion Effects: Elevated Thoughts and Transcendent Emotions
(2026-01-06) Chang, Chingching; Cho, Mu-Jung
This study investigates the phenomenon of inspiration contagion on Reddit’s GetMotivated subreddit, where inspirational posts may trigger similarly inspirational responses in user comments. Extending prior work on emotional contagion, the study disentangles thought contagion (the transfer of thematic ideas) from emotional contagion (the transfer of specific feelings), with a focus on self-transcendent emotions (e.g., admiration, gratitude, optimism) and motivation-related emotions (e.g., curiosity, desire, realization). Using topic modeling and mixed-effects models, our results show strong evidence for both forms of contagion. Thought contagion was strongest when comments thematically aligned with the original post, particularly in narratives about "overcoming struggles and finding motivation''. For emotional contagion, gratitude demonstrated the most powerful same-emotion transfer effect. Motivation-related emotions did not show direct transfer, but posts expressing curiosity were found to elicit admiration in comments. These findings clarify the distinct mechanism by which inspiration spreads in online communities.
You can’t really ‘untag’: The Material Addressability of X and the Undermining of Journalistic Authority
(2026-01-06) Geboers, Marloes; Dodds, Tomás; Boukes, Mark; Abdul Rahman, Eirliani
Social media’s attention-based economy and design allegedly spur mistrust and attacks against media workers. X (formerly “Twitter”) facilitates a discursive climate that is divisive and polarized. We present research on platform-afforded tagging practices as infrastructures that enact ‘subtle’ undermining of journalistic authority through piggybacking on press-critique. X does not allow effective “untagging” from other people’s tweets. This creates material addressability, amplifying harassment. The case zooms in on repetitive questioning of whether BBC journalist Laura Kuenssberg was ‘at the party’, alluding to Partygate, a scandal about meetings at 10 Downing Street during Covid-19 lockdowns. Repurposing linkages between hashtagged and @-tagged tweets allowed us to map a network of critique, assembling a community of ‘supercharged critical thinkers.’ While the ‘repetitive drum of suspicion’ might seem benign, tagged tweets are embedded within a networked ecology of vitriol and misogyny through hashtags connecting creators to a wider network of distrust. Tagging controls are too rudimentary for journalists.
When Image Emotion Outpaces Text: The Role of Emotional Intensity Incongruity in Driving Engagement on Instagram
(2026-01-06) Abbasi Bastami, Asal; Salehi, Masoud
Drawing on Emotion As Social Information Theory, Cognitive Dissonance Theory, and the Elaboration Likelihood Model, we investigate how mismatched emotional intensity across visual and textual modalities impacts engagement. We analyze how the relative emotional dominance of the visual modality—measured as the raw difference in emotional intensity between image and text within a post—affects engagement. Our dataset includes 829,602 Instagram posts from 28,827 North American, English-speaking influencers, each annotated with probabilistic intensity scores across six basic emotions: anger, disgust, fear, joy, sadness, or surprise. The results demonstrate that when image-based emotional intensity exceeds textual expression—captured as the signed difference between the two—comment counts increase across most emotions, suggesting that engagement is sensitive not just to the magnitude, but to the direction of emotional imbalance. However, consistent with Cognitive Dissonance Theory, this positive effect diminishes and reverses beyond a critical threshold of emotional intensity incongruity between image and text.
The Emotional Rift: Evaluating Affective Polarization on TikTok during the 2024 U.S. Presidential Election
(2026-01-06) Mantz, Viva; Özdemir, Melisa; Stieglitz, Stefan
TikTok has become one of the most popular social media platforms. By focusing on short videos which users can comment on, emotionally charged communication is both encouraged and rewarded. However, it is unclear how affective polarization manifests within the TikTok infra-structure. Drawing on Social Identity Theory to explain intergroup dynamics, this mixed methods study applies a systematic, large-scale corpus analysis to comments (n=10,000) collected from the TikTok accounts @KamalaHarris and @DonaldTrump shortly before the 2024 U.S. presidential election. Overall, findings indicate that affective polarization TikTok-specific communication can be sorted into the following categories: 1) (explicit) affective polarization, 2) positive ingroup affirmation, and 3) inferred affective polarization. This study contributes to a toolkit for automated prediction models, offering the base for mitigating negative effects of affective polarization and fostering cohesive engagement on social media.
Introduction to the Minitrack on Communication, Digital Conversation, and Media Technologies
(2026-01-06) Lewis, Seth; Masullo, Gina; Kalman, Yoram

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