Conference Agenda

Social media platforms have become significant spaces where young people socialize and participate in public life. Young people face increasingly complex social, moral and ethical questions and dilemmas given the way interaction happens in these spaces. However, the rapid changes in technical functionalities of social media platforms, and evolving social norms within user communities, have increased the complexity and multiplicity of meaning-making and the digital literacies available within such environments. The study of digital culture in such contexts requires novel methods and techniques that can enable researchers to better examine the complexity of young people’s activities, experiences, and competencies. This paper explores the possibilities and affordances of a series of visual, narrative, and interactive methods to prompt young people to reflect on the nature of social media spaces and the norms that shape how they operate. This paper contributes to the growing volume of research on creative visual and arts-based methodologies for research on digital culture

This paper investigates AI-generated videos for children on YouTube through an exploratory study. Using a digital methods approach, we compiled two datasets of videos from 2022 to 2024, in English and Portuguese, containing references to both “AI” and “children.” From these, we manually selected and analyzed a subset of AI-generated children’s videos, describing their characteristics and discussing implications.

Our findings reveal a growing presence of AI-generated children’s videos, mainly featuring music and stories, with also educational and religious content. As a secondary finding, we have also identified numerous tutorial-style videos promoting the monetization of AI-generated content, reflecting broader trends in low-effort, engagement-driven video production. Despite AI’s increasing role in content creation, most AI-generated children’s videos did not achieve high view counts, with a few exceptions that raise questions about AI's contribution to engagement and virality.

The study also highlights the challenges of defining AI-generated content, as many videos exist on a spectrum of AI usage rather than being entirely machine-produced. Our findings highlight the need for further research on AI’s influence on children’s media consumption, the role of automation in shaping content, and the implications for the quality and diversity of children’s online media.

This paper introduces the innovative use of web detection to overcome a methodological challenge in visual media analysis within social media research and Internet studies. Traditional approaches often depend on manual coding of top-ranked images or examining individual images, lacking the integration of web-based knowledge. This study employs web detection methods utilizing image search ranking mechanisms and knowledge graphs for data retrieval and contextualization. This approach enables the identification of web entities linked to images, webpages, and image URLs that fully or partially correspond to the original image collection.

Findings reveal that web detection methods offer a novel framework for examining issue mapping and cross-platform visual vernaculars, as web entities and exact visual match outputs position an image collection within the freshest and most relevant web sources. Findings are organized into key themes characterizing the (1) contextual, (2) social perceptions, (3) ephemerality and (4) technological grammar of web entities and exact visual matches. A proof of methods is provided through a case study on ChatGPT. Methodological insights are supported by a four-year digital methods study, using three unchanged image datasets to analyze AI web detection outputs over time. We applied quali-quanti approaches that facilitate a deep understanding of web detection technologies within their operational logic and socio-technical contexts. This paper contributes to new, reproducible methods for contextual image collection analysis, including techniques for studying cross-platform visual vernaculars and reconceptualizing ‘operational images’ (Parikka, 2023) through web detection methods.

Concerns regarding online incivility's impact on democratic discourse have prompted inquiries into its perception and consequences. In this study, we propose a model that profiles online audiences based on their sensitivity to incivility, resulting in four typologies: congruent, high, low, and tone-deaf. We explore individual and message attributes driving these diverging perceptions through a nationally representative online survey experiment (N = 1,500), where participants were exposed to civil and uncivil comments and asked to rate them. Perception alignment scores were calculated to determine sensitivity profiles. We found that participants with congruent and high sensitivity were the largest groups of respondents, and that threats were generally recognized as uncivil across sensitivity profiles, compared to other uncivil messages. We also found that neighborhood type, age, income, political orientation, and prior exposure to incivility were significantly associated with specific sensitivity profiles. These findings demonstrate the utility of our proposed model in understanding the complex and nuanced nature of online incivility perception and highlight the importance of considering individual differences in efforts to mitigate its negative implications for deliberative democracy.