This study examines how social media users interpret and respond to two transparent interface nudges designed to encourage more careful engagement with news content. Drawing on dual-process theory, social norm theory, and nudge theory, the study developed an interactive Facebook-style mobile prototype with an accuracy prompt triggered during sharing and a persistent social-norm message above posts. Using an exploratory, qualitatively driven Design Science Research approach, data were collected from 24 participants through click-stream logs, postsimulation survey responses, and seven semistructured interviews with intervention-group participants. The findings show that participants often described themselves as capable of identifying misinformation while relying in practice on emotional salience, familiarity, social cues, and low-effort forms of engagement. The social-norm message was more consistently noticed, whereas the share-triggered accuracy prompt had limited reach because sharing was infrequent. Participants generally perceived the nudges as nonintrusive, and some reported moments of hesitation or reconsideration. However, the study does not provide evidence of improved accuracy discernment, belief change, or direct System 2 processing. Its contribution lies in showing how established drivers of misinformation engagement shape the reception and limits of interface nudges in a simulated mobile-feed setting. The study offers design implications for developing transparent, autonomy-preserving interventions that better align with passive and socially cued forms of platform engagement.
In recent decades, artificial intelligence (AI) has become central to debates on technology, society, and media, particularly with the rise of generative AI agents, such as AI companions. These systems exhibit adaptive capacities, dynamic interactions, and the potential to influence human relationships, raising ethical, communicational, and regulatory concerns. This article develops a diagnostic critique within media and cultural studies, using the film Her (2013) as a cultural text to examine human–AI interaction and its contemporary relevance. Adopting a multiperspectival approach, the study integrates textual, thematic, and sociohistorical analyses of the film, connecting its fictional representations to emerging AI systems, such as ChatGPT. The findings indicate that AI agents can reproduce patterns of algorithmic emotional governance, including emotional manipulation and affective dependence, similar to those depicted in Her , exposing human vulnerabilities in algorithmic interactions and highlighting regulatory gaps in Europe under the AI Act and in Brazil under Bill 2338/2023. Examining these frameworks as emerging responses to algorithmic influence, the study identifies the European AI Act as the first comprehensive global regulatory model and a key reference for the Brazilian bill. By bridging media culture, technology, and regulation, the study contributes to communication and social change debates, providing analytical lenses to understand AI agents as communicative partners that influence users cognitively, emotionally, and socially, reinforcing the need for public policies and critical frameworks attentive to the ethical, affective, and governance dimensions of AI systems.
Generative AI reorganizes communication by turning reception into a resource for subsequent production. This recursive structure concentrates interpretive power in institutions that can learn from users while leaving users little access to how their conduct shapes later outputs. This editorial uses recursive asymmetry as an organizing lens for interpreting that unequal relation. The nine articles show how feedback becomes embedded in generative infrastructures, how recipients are converted into informational inputs, and how corrective labor is displaced downstream after harm occurs. Responsiveness may therefore expand without corresponding reciprocity, while verification and repair fall to actors with limited authority over the learning process. The resulting governance problem extends beyond disclosure and output quality. Accountable generative communication depends on whether the processes that convert response into future communication remain legible and contestable to those whose conduct sustains them.
Perceived salience of dataveillance—the collection and analysis of digital traces by corporate and state actors—can increase expectations of negative consequences from digital communication, leading to online self-inhibition. This process, known as the chilling effects of dataveillance, can limit participation in today's digital society, where being online is a need and norm. The uses and gratifications (U&G) approach and social norms theories suggest that users are motivated to engage in digital communication based on felt needs and perceived norms, which would reduce their susceptibility to chilling effects. This study investigates whether such need- and norm-based motives for free digital communication mitigate chilling effects. Using a representative survey sample from the Swiss-German internet population ( N = 898), we conducted mediation and moderation analyses to test this assumption across three prevalent digital communication behaviors: searching for information, expressing opinions, and disclosing personal information. Results revealed that perceived salience of dataveillance and expected negative consequences from digital communication were overall associated with self-inhibition. In contrast, need- and norm-based motives did not mitigate these associations, suggesting that chilling effects of dataveillance do not depend on stronger motives driving free digital communication in this study. By introducing U&G and social norms approaches into chilling effects research, this study advances our limited understanding of chilling effects’ boundary conditions. It offers representative, behavior-specific evidence aligning with the theoretical process of chilling effects, highlighting the potential gravity of being exposed to dataveillance in everyday life.
Generative artificial intelligence (AI) tools such as ChatGPT are increasingly integrated into many realms of society and everyday life, generating both promise and concern. This underscores the need to examine how they are represented on social media, where their meanings, uses, and implications are publicly negotiated. Drawing on a snapshot of top results returned by TikTok's algorithmic ranking system, we examined 4458 TikTok videos with English-language descriptions related to ChatGPT, combining topic modeling with interpretive multimodal content analysis. We identified six overarching themes ranging from informational content, including tutorials and sociotechnological discussions, to entertainment-oriented content such as playful interactions with AI and memes. Multimodal analysis showed that creators blend visual, aural, and textual resources in distinct ways to construct meanings and assessments across themes. Engagement analyses revealed that entertainment-oriented content attracted higher interaction than informational content, although informational content was more prevalent. With a focus on TikTok, this research illustrates how public communication about emerging technologies is shaped through platform-specific multimodal conventions and engagement dynamics. The findings provide insights for stakeholders who seek to promote responsible AI communication on video-sharing platforms where algorithms shape which content is prioritized.
Large Language Models (LLMs) have rapidly moved from novelty tools to routine infrastructures for producing and analyzing communication, intensifying the shift of communication and media studies from a “computational turn” to a “generative turn.” Yet field-wide evidence on how LLMs are selected, deployed, and evaluated remains limited. This systematic review maps LLM use across communication scholarship, distinguishing LLMs used as a method from LLMs studied as a subject/topic, and synthesizing task-specific benefits and limitations. We searched Web of Science and Scopus for English-language journal articles related to LLMs and communication and media studies published after the release of ChatGPT. All collected articles were analyzed by mixed methods. We observed that most subfields of communication and media studies showed similar model-use features, with only a small number of robust associations. Publications were dominated by high-income affiliations suggesting an inequality. Model deployment was strongly task-contingent. Transparency and validation practices remain uneven, with only about three-fifths of GenAI-based studies disclosing concrete prompts and just over half reporting human evaluation of model outputs. Across tasks, reported risks concentrated on validity threats and bias. We conclude by proposing a 3M framework to support more transparent, reliable, and context-sensitive LLM-enabled communication and media studies. Theoretically, our findings clarify how the “generative turn” reshapes research by shifting LLMs from purely analytic instruments to sociotechnical infrastructures that also generate communicative content and participate in communicative processes.
Novel communication technologies provide opportunities for more effective persuasion. Drawing on theories of attribution and perspective taking, this study examines how different perspectives (first-person vs. third-person) and modalities (text vs. video) influence individuals’ causal attributions for flood disasters and their subsequent beliefs about climate change. In particular, face-swapping technology was employed to facilitate first-person perspective taking. The results showed that modality had main effects on both behavioral attribution (attribution to individuals playing near a river) and climate change attribution (attribution to changes in global weather patterns). Moreover, behavioral attribution mediated the relationship between modality and climate change beliefs, and this indirect effect was significant only among participants in the first-person perspective condition. Lastly, an indirect effect of modality on protective behavioral intention through climate change attribution was observed. These findings suggest that the personalized first-person video condition implemented through face-swapping may be more effective at indirectly increasing participants’ climate change beliefs.
The birth of Tilly Norwood in 2025 as a synthetic or AI-generated actress marks a provocative moment: a flashpoint at which Hollywood's workers, writers, producers, directors, artists, musicians, and institutions are being asked to reevaluate what it means to be a performer . This commentary contends that Tilly Norwood both crystallizes and accelerates structural shifts in the entertainment industry. Her existence exposes tensions around hegemony, cost, authorship, culture, and emotional resonance that synthetic talent may shift human agency away from human performers. To understand this new development in the media and entertainment industry, we used the meaningful work conceptual framework. This framework hypothesizes that when people consider their work as meaningful, it leads to a positive outcome in their general life. In addition, we discussed potential concerns in the Hollywood industry related to regulation and co-optation to this emerging phenomenon. We concluded by proposing a new line of research in the media and entertainment industry that examines the growing tension between AI and human creativity.
This commentary argues that the artificial intelligence (AI) boom is not immaterial but relies on energy- and resource-intensive infrastructures. While “Green AI” scholarship has advanced model-level efficiency metrics and reporting, it has largely overlooked the material circuits that enable these models. We explain how efficiency-first framings are reinforced by an ideological blend of cybertarianism and techno-nationalism, which together reframe environmental and labor externalities as acceptable costs of competition. We propose three priorities for a more adequate agenda: (1) adopt the “trash metaphor” to highlight the afterlives and externalities of AI infrastructures; (2) implement structural solutions that oversee and regulate the entire material circuits of AI; and (3) foster green citizenship through public information rights, participatory siting, and ongoing civic oversight to advocate for and sustain the necessary structural changes. Overall, we call for aligning AI's claimed climate benefits with demonstrable compliance to ecological budgets rather than aspirational efficiency narratives.
Following the 2022 adoption of the European Union's Digital Services Act, holding intermediaries accountable for identifying and handling harmful content or services online, many of the major platforms of the adult entertainment industry made changes in their governance structures and compliance standards. These platforms’ heightened moderation eroded creators’ trust and disrupted audiences’ engagement with content created by anyone not representative of the pornographic mainstream, necessitating laborers’ negotiation and subversion of platform constraints to achieve their personal, social, and professional goals. The deregulation of labor relations and the erosive individualization of social protections have weakened the power of (sex) workers on the internet, amplifying labor competition and ushering in novel forms of exploitation. Tracing the implications of intersecting international legislation, capitalist imperatives, and marginalized laborers’ movements of resistance, this study explores emerging capitalist and cooperative adult entertainment platforms announced in the wake of the adoption of the Digital Services Act. The concurrent movements of corporate platform capitalism and worker cooperativism are vying for laborers’ support, and this study charts the solutions they promise to provide to the problems characteristic of the contemporary platform economy.
Since its initial release in 2019, the c-drama Chén Qíng Lìng (陈情令 / The Untamed) has resonated well-beyond its intended Chinese audience. Current research has delved into the series evolution and dissemination from a variety of angles, such as translation, queer studies, and intercultural reception. The present study aims to contribute to this growing work of research by conducting an exploratory study of emerging themes among the Spanish-speaking fandom. Considering the size and diversity of this community, as well as the increasing dissemination of Chinese popular culture as a form of soft power, the intersection of both themes presents itself as a site ripe for academic discourse. To better understand how fans engage and respond to The Untamed , web scraping tools were first used to download comments from WeTV's Spanish YouTube channel and conduct a systematic qualitative analysis of their content. The main emergent themes then formed the basis for a questionnaire which guided twelve semi-structured in-depth interviews with Latin American fans of the series and a professional translator. Findings indicate a diverse international community eagerly engaging (often for the first time) with Chinese fantasy, notwithstanding language barriers and cultural (dis)connections. Streaming platform access and translation quality emerged as significant themes, along with a range of personal tactics to overcome initial confusion with the material, and diverging stances on queer content representation. These observations align the consumption of Chinese dramas by Spanish-speaking fans with the notion of pop cosmopolitanism as a viable path to encourage global consciousness.
The rapid advancement of generative artificial intelligence (AI) has transformed digital marketing, redefining how brands create, personalize, and deliver content to engage consumers. As organizations increasingly rely on AI-driven systems for customer interaction, understanding how these technologies influence customer engagement has become a critical area of inquiry. This study systematically reviews research published between 2022 and 2025 to evaluate the impact of generative AI on customer engagement, distinguishing between affective outcomes (e.g., satisfaction, trust, and commitment) and behavioral outcomes (e.g., click-throughs, shares, purchases, and retention). Following PRISMA guidelines, a comprehensive search across major databases (Scopus, Web of Science, and Google Scholar) identified 528 records, of which 64 articles were assessed for eligibility, and 33 studies met the final inclusion criteria for qualitative synthesis. Findings indicate that generative AI tools, particularly large language models (LLMs) for conversational marketing and generative adversarial or diffusion models for visual content, generally associated with positive behavioral engagement outcomes, such as improved attention, interactivity, and conversion rates, although these effects vary depending on context, platform, and implementation. However, affective engagement outcomes remain mixed; while personalization and novelty foster satisfaction and delight, authenticity concerns often hinder trust and emotional connection. Two moderating factors, AI content disclosure and human-in-the-loop (HITL) oversight, emerged as critical influences. Transparent disclosure can enhance credibility in some contexts but evoke skepticism in others, while human oversight consistently reinforces brand authenticity, ethical quality, and consumer confidence. Practical implications include guidance on effective AI disclosure strategies, when and how to be transparent about AI generation and the importance of maintaining a human touch in AI-augmented marketing. The review also identifies key research gaps, including long-term effects on brand loyalty, cross-cultural differences, and ethical and legal implications, and proposes a future research agenda to advance knowledge in this rapidly evolving field.
This article analyzes generative AI as a creative medium—examining its specific properties, affordances, and constraints rather than its social or ethical implications. Drawing on media theory and art history, it identifies six key properties of the AI medium. First, probabilistic generation creates a fundamental trade-off between variability and control. Second, the medium offers an unprecedented range of degrees of freedom in creative output. Third, it privileges the conventional logic of our familiar world—including aesthetic conservatism rooted in training data—making it the structural opposite of historical avant-garde practices. Fourth, built-in cognitive capacity makes generative AI the first artistic medium that itself thinks. Fifth, its encyclopedic knowledge of art history and media techniques constitutes what this article calls “media cognition.” Sixth, the entanglement of style and content is a structural feature of how models encode visual knowledge. Together, these properties define what the AI medium makes possible—and what it fundamentally resists.
Increasingly, the global public relies on social media for information gathering and opinion formation but the spread of misinformation is a major global risk. Though this is not necessarily a new problem, the prevalence of misinformation and disinformation and their speed in dissemination via social media represents an important challenge for democracies. With the rapid rise of short-form video platforms, our study explores the fact-checking efforts by both fact-checking organizations and influential individuals on TikTok. Findings reveal the diversity of fact-checking content strategies applied by professionals and the public through the application of TikTok features and affordances. The study highlights how fact-checking content adapts to a platform's unique affordances and user culture highlighting the dynamic, interactive, and diverse efforts to address misinformation using short-form video.
The birth of Tilly Norwood in 2025 as a synthetic or AI-generated actress marks a provocative moment: a flashpoint at which Hollywood's workers, writers, producers, directors, artists, musicians, and institutions are being asked to reevaluate what it means to be a performer . This commentary contends that Tilly Norwood both crystallizes and accelerates structural shifts in the entertainment industry. Her existence exposes tensions around hegemony, cost, authorship, culture, and emotional resonance that synthetic talent may shift human agency away from human performers. To understand this new development in the media and entertainment industry, we used the meaningful work conceptual framework. This framework hypothesizes that when people consider their work as meaningful, it leads to a positive outcome in their general life. In addition, we discussed potential concerns in the Hollywood industry related to regulation and co-optation to this emerging phenomenon. We concluded by proposing a new line of research in the media and entertainment industry that examines the growing tension between AI and human creativity.
Extensive research has explored the application of artificial intelligence (AI) in the journalism industry, particularly in Western contexts. However, limited research has examined how journalists in nonwestern contexts, like China, use AI and the factors impacting its usage in journalism practice. Guided by technology acceptance model (TAM) and the theory of planned behavior (TPB), this study conducted a national survey of Chinese journalists ( N = 652) to reveal the psychological factors influencing the adoption of AI among Chinese journalists. Results showed that perceived usefulness, attitudes towards journalistic AI, and subjective norm contributed to the AI technology adoption in journalistic work. Specifically, this study found the critical role of social norms and individual psychological perception of AI technology in shaping journalists’ willingness to integrate AI into their professional routines. This suggests that the successful implementation of AI in journalism cannot rely solely on technological capabilities but must also take into account the broader social environment and journalists’ cognitive evaluations of the technology. This study not only provides empirical evidence for the applicability of the TAM and the TPB in understanding AI adoption but also extends these frameworks to the specific context of journalism in China.
The articles in this issue examine how artificial intelligence (AI) transforms both the practices of information actors and the structures of media and cultural industries. For the information actors, contributions focus on fact-checkers, journalists, platform users, and linguistic communities, showing how AI reshapes professional routines and everyday information work. For the media and cultural industries, contributions explore how AI reconfigures creative production, entertainment, sports broadcasting, and journalism, revealing how it reorganizes authorship, labor, audience engagement, and business models. Together, these studies move the conversation beyond questions of AI adoption toward a more pressing concern: how AI can be deployed responsibly within the social, professional, and industrial contexts in which it operates.
Generative AI (GenAI) is rapidly establishing itself as a key topic in societal, political, and scholarly debates. From a news and information perspective, relatively little attention has been devoted to how GenAI is framed in reporting by influential news outlets, with most coming from globally recognized English-language titles. This study operationalizes a framing analysis of 516 articles published by a quality newspaper in Belgium between 2020 and 2024. Findings reveal that the launch of ChatGPT in late 2022 signified a notable shift in the quantity and ways in which GenAI was approached and framed, extending the scope beyond technological stories to focus more on regulatory and societal ramifications. We establish a typology of 12 different positive, neutral, and negative frames in how GenAI is positioned and discussed in reporting. Combined, they highlight the complex nature and practice of reporting on an emerging technology that is taking the world by storm, while also retaining traditional journalistic values and tenets intact. We extend the body of research on AI news framing beyond the English language and to quality news outlets specifically.
Platformization is significantly transforming media markets such as film and music. While media and communication research has acknowledged these developments, its focus often is on a few very large platforms of big tech giants, overlooking emerging platforms and those serving niches. To address this research gap, this study examines the German market of journalism platforms by drawing on the spaces of negotiation framework, and applying a two-step methodological approach that combines market analysis with 17 qualitative semi-structured expert interviews. Our findings reveal that the platform market in journalism is highly dynamic and comprises more than just the well-known very large platforms. The dependencies between publishers and platforms are reciprocal, particularly with smaller platforms that are currently emerging, indicating that publishers have a degree of counter-power and can strategically shape platforms. Our study contributes to media and communication studies by offering a more nuanced understanding of the publisher-platform relationship and the related negotiation spaces.
This study examines the influence of artificial intelligence (AI)-driven media platforms on South Africa's indigenous languages—Setswana, Tshivenda, and Xitsonga—in the country's diverse linguistic environment. It examines how algorithmic biases, often rooted in colonial linguistic hierarchies, diminish the visibility and vitality of these languages in digital media. Using a mixed-methods approach, the study combines algorithmic audits of AI platforms (e.g., social media and natural language processing tools) to evaluate content visibility and translation accuracy, interviews with AI developers and media practitioners to assess linguistic diversity in design, and focus groups with rural communities to gather user experiences. Results indicate that limited training data and a focus on dominant languages, such as English, marginalize these indigenous languages, with audits revealing error rates of 30% to 42% in translation and voice recognition for these languages. Nonetheless, community-driven innovations demonstrate potential for creating inclusive AI solutions. The study proposes a decolonial framework for designing AI technologies that prioritize African linguistic rights and epistemologies, contributing to a nuanced understanding of AI's role in Africa's media landscape. This study is among the first to integrate algorithmic audits with community ethnography to reveal how AI systems shape Africa's linguistic diversity and to propose decolonial design principles for inclusive AI futures.
