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\" A wide ranging, interdisciplinary exploration of media time and mediated temporalities. The chapters explore the diverse ways in which time is articulated by media technologies, the way time is constructed, represented and communicated in cultural texts, and how it is experienced in different social contexts and environments.\"-- Provided by publisher.

Background This study explores associations between screen use profiles, body image perception, and dieting behaviour among Polish adolescents. Excessive screen time, particularly on social media, may contribute to internalisation of unrealistic appearance ideals, body dissatisfaction, and unhealthy weight-control behaviours. Material and methods Data were collected online in 2021/22 as part of the HBSC study. The sample included 5322 adolescents aged 13, 15, and 17. Cluster analysis based on daily time spent on gaming, social media, videos, and browsing identified five screen use profiles: moderate users (C1, 40.1%), game-oriented (C2, 21.1%), social media-oriented (C3, 20.3%), intensive game/social media users (C4, 11.1%), and fully intensive users (C5, 7.4%). Body image, BMI, and dieting were self-reported. Results Adolescents in the most intensive screen use clusters (C3-C5) showed the highest prevalence of body dissatisfaction and dieting behaviour. For example, over half of users in C3, C4, and C5 perceived themselves as “too fat,” and active dieting reached 22.8% in C5. Regression analysis confirmed that higher media consumption-especially combined use of gaming and social media-was significantly associated with both negative body perception and dieting intentions (C5 OR for “too fat” = 2.06; OR for dieting = 1.81), independent of BMI. Conclusions Distinct screen use patterns are strongly linked to adolescent body image and dieting behaviours. Public health interventions should address not only screen time volume but also content type and usage patterns, with attention to age and gender differences. Key messages • Screen use patterns vary by gender and age and are linked to body image concerns. Tailored interventions must address not only time, but also content type and user profile to reduce dieting risks. • Intensive screen use, especially social media and gaming, is linked to body dissatisfaction and dieting among adolescents, independently of BMI, highlighting the need for content-aware interventions.

\"Media events\" generate conditions of shared attention as many users simultaneously tune in with the dual screens of broadcast and social media to view and participate. We examine how collective patterns of user behavior under conditions of shared attention are distinct from other \"bursts\" of activity like breaking news events. Using 290 million tweets from a panel of 193,532 politically active Twitter users, we compare features of their behavior during eight major events during the 2012 U.S. presidential election to examine how patterns of social media use change during these media events compared to \"typical\" time and whether these changes are attributable to shifts in the behavior of the population as a whole or shifts from particular segments such as elites. Compared to baseline time periods, our findings reveal that media events not only generate large volumes of tweets, but they are also associated with (1) substantial declines in interpersonal communication, (2) more highly concentrated attention by replying to and retweeting particular users, and (3) elite users predominantly benefiting from this attention. These findings empirically demonstrate how bursts of activity on Twitter during media events significantly alter underlying social processes of interpersonal communication and social interaction. Because the behavior of large populations within socio-technical systems can change so dramatically, our findings suggest the need for further research about how social media responses to media events can be used to support collective sensemaking, to promote informed deliberation, and to remain resilient in the face of misinformation.

Time distortion is a hallmark feature of addictive behaviors including excessive technology use. It has clinically significant implications for diagnosis and treatment. Additional information on such distortions after prolonged abstinence from technology use is needed. We seek to examine differences in the effects of several days of abstinence on time-distortion in two groups: social media users who are at-risk and those who are at low risk for social media “addiction.” To examine this, we employed a randomized, two group, pre (t1) - post (t2) design. Both groups completed survey tasks that cued social media use at t1 and at t2. Between t1 and t2, the treatment group (n = 294) abstained from social media use for up to one week (less if they “broke” and decided to resume use), and the control group (n = 121) did not. Results indicated that low-risk individuals in both the treatment and control groups presented downward time bias at t1; at-risk individuals presented non-significant upward bias. After abstinence, both low- and at- risk individuals in the treatment group presented upward time distortion. This effect did not take place in the control group; low-risk users still presented significant downward bias at t2. The post-abstinence increase in time distortion was significantly more pronounced in at-risk users. These differences between pre- and post-abstinence time distortion patterns in normal and at-risk-for-“addiction” social media users can be used for adjusting and interpreting self-reports related to addictive uses of technologies.

In 2009, influenza A H1N1 caused the first pandemic of the 21st century. Although a vaccine against this influenza subtype was offered before or at the onset of the second epidemic wave that caused most of the fatal cases in Europe, vaccination rates for that season were lower than expected. We propose that the contradiction between high risk of infection and low use of available prevention measures represents a pandemic public health paradox. This research aims for a better understanding of this paradox by exploring the time-dependent interplay among changing influenza epidemiology, media attention, pandemic control measures, risk perception and public health behavior among five European countries (Czech Republic, Denmark, Germany, Spain and the UK). Findings suggest that asynchronicity between media curves and epidemiological curves may potentially explain the pandemic public health paradox; media attention for influenza A H1N1 in Europe declined long before the epidemic reached its peak, and public risk perceptions and behaviors may have followed media logic, rather than epidemiological logic.

Understanding media discussions on artificial intelligence (AI) is crucial for shaping policy and addressing public concerns. The purpose of this study was to understand sentiment regarding AI in the media and to discover how the discussion of topics changed over time in technology-related media outlets. The study involved three overall steps: data curation and cleaning to obtain a high-quality, timely dataset from a list of relevant technology-news-oriented websites; sentiment analysis to understand the emotion of the articles; and Latent Dirichlet Allocation (LDA) to uncover the topics of discussion. The study curated and analyzed 22,230 articles from technology-focused media outlets between the period 2006 and July 2024, split into three time periods. We found that discussion on AI-related topics has increased significantly over time, with sentiment generally positive. However, since 2022, both negative and positive sentiment proportions within articles have risen, suggesting growing emotional polarization. The introduction of ChatGPT 3.5 in November 2022 notably influenced media narratives. Machine learning remained a dominant topic, while discussion on business and investment, as well as governance and regulation, has gained prominence in recent years. This study demonstrates the impact of technological advancements on media discourse and highlights increasing emotional polarization regarding AI coverage in recent years.

Firms that do not fit into established business categories tend to be overlooked, but new markets often form around these \"misfits.\" Because being seen as part of a growing population makes new populations seem real, counting them is important to mainstreaming new markets. Yet, if firms outside the mainstream are overlooked, how can they be counted? Extending the embeddedness perspective to social cognition about markets, this research exposes the media's central role in market formation. Using a new method for extracting data about market networks from media coverage, this study demonstrates that early entrants benefit from inviting coverage that makes a few-but not too many-links to other entrants, thus helping audiences perceive an emerging category. As the market matures, however, references to rivals become unhelpful. These findings illustrate the value of a linguistic turn to empirical studies of meaning construction and the reification of social structure.

The COVID-19 pandemic reshaped social dynamics, fostering reliance on social media for information, connection, and collective sense-making. Understanding how citizens navigate a global health crisis in varying cultural and economic contexts is crucial for effective crisis communication. This study examines the evolution of citizen collective sense-making during the COVID-19 pandemic by analyzing social media discourse across Italy, the United Kingdom, and Egypt, representing diverse economic and cultural contexts. A total of 755,215 social media posts from X (formerly Twitter) were collected across 3 time periods: the virus' emergence (February 15 to March 31, 2020), strict lockdown (April 1 to May 30, 2020), and the vaccine rollout (December 1, 2020 to January 15, 2021). In total, 284,512 posts from Italy, 261,978 posts from the United Kingdom, and 209,725 posts from Egypt were analyzed using the latent Dirichlet allocation algorithm to identify key thematic topics and track shifts in discourse across time and regions. The analysis revealed significant regional and temporal differences in collective sense-making during the pandemic. In Italy and the United Kingdom, public discourse prominently addressed pragmatic health care measures and government interventions, reflecting higher institutional trust. By contrast, discussions in Egypt were more focused on religious and political themes, highlighting skepticism toward governmental capacity and reliance on alternative frameworks for understanding the crisis. Over time, all 3 countries displayed a shift in discourse toward vaccine-related topics during the later phase of the pandemic, highlighting its global significance. Misinformation emerged as a recurrent theme across regions, demonstrating the need for proactive measures to ensure accurate information dissemination. These findings emphasize the role of cultural, economic, and institutional factors in shaping public responses during health crises. Crisis communication is influenced by cultural, economic, and institutional contexts, as evidenced by regional variations in citizen engagement. Transparent and culturally adaptive communication strategies are essential to combat misinformation and build public trust. This study highlights the importance of tailoring crisis responses to local contexts to improve compliance and collective resilience.

The respiratory virus known as human metapneumovirus (hMPV) is linked to seasonal outbreaks and primarily affects elderly people and young children. Infodemiology, which uses digital data sources, including social media, online news, and search trends, is a useful substitute for monitoring public concerns and risk perceptions because surveillance gaps and underreporting impede public health interventions despite their clinical value. To assess public search interest, we analyzed global search behavior between June 1, 2024, and June 1, 2025, and examined over 1.3 million tweets collected during the peak outbreak period from January to March 2025. Our findings show a sharp rise in public interest following official reports of HMPV outbreak in China, with simultaneous search peaks across both hemispheres regardless of season. Search activity expanded to 177 countries and revealed sustained interest in Australia, Thailand, the United Kingdom, and the United States. Regional differences in terminology and platform usage were also observed, with non-English-speaking countries favoring the abbreviation “HMPV” and English-speaking regions more often using the full term. Additionally, discrepancies between search activity and social media engagement in some countries point to distinct patterns of public information-seeking behavior. These results underscore the importance of adapting health communication strategies to local language norms and preferred digital platforms. They also highlight the need for real-time monitoring and proactive responses to misinformation. Together, search and social media data offer a valuable lens for understanding public sentiment and improving the reach, accuracy, and impact of global outbreak communication.

Social media analysis offers valuable insights into public health-related behaviours, such as attitudes toward vaccination and adherence to non-pharmaceutical interventions (e.g., mask-wearing), as well as determinants like trust in institutions and perceived risk. These digital traces help identify disinformation trends, public doubts, information needs, and behavioural signals - elements that could be integrated into behavioural models. The University of Pisa developed a structured social media monitoring approach through the RISP project (2023-2024), based on the WHO and UNICEF Infodemic Insights Reporting methodology. This system combines automated dashboards with manual narrative analysis, applied monthly to vaccine-related content. As an example, we will present insights from flu vaccine monitoring during the 2023-2024 season, which showed a rise in discouraging stances as institutional communication declined, alongside recurring questions on eligibility and safety. This monitoring approach forms the basis for ongoing work in the BEHAVE_MOD project, where social media data will be used to explore and model health-related behaviours across diseases such as flu, RSV, and Dengue. To make social media monitoring more directly usable in modelling frameworks, we are working to go beyond qualitative narrative identification by developing automated tools for stance classification. Specifically, we are building a framework that uses natural language processing (NLP) and large language models (LLMs) to classify vaccine stance at scale. This enables the generation of structured behavioural indicators-such as stance proportions over time-that can be quantified and integrated into mathematical models. While social media analysis cannot replace traditional behavioural surveillance systems, it can complement them by providing a low-cost, scalable, and near real-time data stream to support public health monitoring, communication, and modelling in routine and emergency settings.