With appealing social and entertainment media only a swipe away, many students and knowledge workers report issues with procrastination, i.e., irrational task delay favoring short-term gratification over long-term goals. For example, scrolling social media is often more appealing than studying and watching TikTok may be preferable in the short term over doing the dishes. This can have negative implications for academic or work performance and well-being. Thus, many users aim to regain control over their digital media use, employing digital disconnection by temporarily abstaining from or at least reducing the use of digital technologies.

Accordingly, the present study aims to investigate whether digital disconnection is an effective form of self-regulation. We propose that engaging in disconnective behavior can reduce procrastination. Particularly in situations in which a goal conflict – a discrepancy between a short- and long-term goal – arises, disconnective behavior should become more relevant for reducing procrastination. Based on prior research, we further assume that those high in trait self-control should be better able to avoid goal conflicts occurring in the first place. However, when goal conflicts do occur, those high in trait self-control should have a higher capacity to regulate this conflict and effectively disconnect.

To test these assumptions, we conducted a two-week pre-registered (https://osf.io/46rmg/?view_only=e8f4606d16604b2bbddbb434b923483d) experience sampling study with up to five probes per day. For the study, 230 young adults answered 12,160 situational probes. We assessed digital disconnection from different aspects of technology use, specifically at the device, application, feature, interaction, and message level.

Assessing digital disconnection and procrastination in-situ helps to elucidate the role of self-regulatory mechanisms (i.e., self-control and goal conflict) for effective digital disconnection. Furthermore, understanding how goal conflicts involving digital media use occur and how disconnection can be helpful in those situations provides practical guidance for learners and workers who struggle with procrastination.

As digital technologies become increasingly ubiquitous, concerns about their negative effects on well-being have fueled growing discourses of technology resistance. Recent scholarship has identified three dominant metaphors shaping these discussions: digital media as a “drug,” with “addictive” and “disordered” properties, as a “demon” that tempts and forms habits, and as a “donut” requiring self-control. These metaphors largely emphasise individual responsibility for managing digital media use and disconnection, although there is an increasing debate over the roles of tech companies, governments, and employers in mitigating perceived or actual harms of digital technologies.

To provide insights into evolving discourses on digital technology resistance and digital well-being we conduct a computational analysis of communication on two social media platforms, both of which mirror public discourse: Twitter and Reddit. We investigate 1) how individuals discuss the effects of digital technologies, 2) the responsibilities they assign to different stakeholders, and 3) how these narratives have changed over time.

We analyse a large dataset fromReddit (228,981 posts and comments from the r/nosurf subreddit) and Twitter (1,522,164 tweets using #digitaldetox and related keywords) from 2011 to 2022. Using a mixed-methods approach, we combine unsupervised topic modeling via BERTopic with qualitative content analysis. BERTopic enables us to identify key topics, stakeholders, and patterns in the discourse present within these communities, while qualitative analysis of representative documents ensures a nuanced interpretation of the findings. Additionally, we operationalise the “drug,” “demon,” and “donut” metaphors as analytical frames to investigate how digital well-being narratives are constructed and vary across platforms and time.

This research builds upon existing analyses of discourses in popular media, shedding light on the narratives underlying digital technology resistance. It contributes to broader debates on digital well-being, personal agency, and the shifting responsibilities of institutions in managing the societal impacts of digital media.

A large body of research in the field of media psychology has linked the use of media entertainment to self-regulation, both in the form of short-term effects, such as mood management or stress recovery as well as more long-term benefits in the form of coping resources and resilience (Reinecke & Rieger, 2021). Temporarily extending the boundaries of the self (TEBOTS, Slater et al., 2014) has been proposed as a central mechanism connecting entertainment to self-regulation: According to TEBOTS, narratives provide relief from the everyday limitations of the self through narrative engagement, thus facilitating the restoration of resources. The model proposes that individuals facing adversity and self-threat show an increased preference for narrative content (H1) and a higher level of narrative engagement (H2). By facilitating boundary extension, narrative engagement is further linked to the restoration of psychological well-being (H3). Experimental research provides preliminary support for these assumptions (Johnson et al., 2021). Yet, even though TEBOTS emphasizes the constraints and stressors of everyday life, the model has predominantly been tested in experimentally induced forced-exposure situations. Whether narrative engagement contributes to self-regulation of media users’ everyday life therefore remains largely unclear.

Addressing this research gap, the present study tested H1-H3 in a three-week diary study with N = 540 participants and 3586 day-level observations. A student sample provided daily self-report measures of experienced adversity, media use, narrative engagement (transportation, identification and entertainment experiences) and well-being over three separate weeks at the beginning, middle, and end of the semester.

To the best of our knowledge, this study is the first to provide an in-situ test of basic assumptions of TEBOTS, both on the between- as well as the within-person level. We thus expect the results to provide unique insights into the mechanisms linking media entertainment to self-regulation in daily life.

The prevalence of smartphone use has sparked public concerns about its effects on sleep. While a substantial body of research suggests negative impacts, current longitudinal studies and studies utilizing tracking data do not replicate the results of earlier cross-sectional and self-report studies. This inconsistency may stem from differences in the constructs assessed, particularly the distinction between within-person and between-person effects, and measures that consider content beyond screen time.

With this study, we aim to address concerns about self-reports, such as recall and common method bias, by supplementing self-reports with tracking data to investigate the effects of prolonged in-bed use of social media, messaging, and video apps on both subjective and objective sleep outcomes. Because research indicates that subjective assessments of sleep can be profoundly affected by individuals' attitudes and beliefs about media use, we further hypothesize that the effects on subjective outcomes will be mediated by daily reappraisal. For instance, social media mindsets can shape user experiences and overall well-being, with concerns about smartphone’s harmful effects on sleep amplifying these beliefs. Similar mechanisms have been identified in sleep research, where positive attitudes toward sleep correlated with better sleep outcomes. Considering current research highlights mostly between-person effects, we further predict that negative mindsets toward smartphone use will be associated with increased negative daily reappraisal, and we will investigate whether these mindsets can explain discrepancies between subjective and objective sleep outcomes.

To test our hypotheses, we conducted a two-week daily diary study (pre-registration: https://osf.io/6qjtd/?view_only=3c87cc1777464b94b8a207e44d804f4b) with 90 participants. Daily questionnaires measured self-reported sleep parameters, smartphone use reappraisal, mood and stress and were supplemented by smartphone and Fitbit tracking data. Overall, the study extends prior research by employing a longitudinal design, combining self-reports with objective data, and assessing the role of mindsets in the psychological process that may link mobile media use to sleep.

In his book “The Anxious Generation,” Jonathan Haidt argues that young people, especially girls in the Western world, are facing a mental health crisis marked by rising depression rates (Haidt, 2024). Haidt attributes this development to social media use, suggesting that it causes (1) social deprivation, (2) sleep deprivation, (3) attention fragmentation, and (4) addiction.

The book quickly became a bestseller, and several countries have since enacted laws to limit social media use among youth, often citing Haidt’s work (Reuters, 2024). However, his claims have faced criticism, particularly from communication scholars, who argue that he overstates the risks, overlooks positive aspects of social media, and selectively represents the literature (Dienlin, 2024; Fried, 2024; Odgers, 2024).

Although there is much literature on the effects of social media use on mental-health (Hancock et al., 2022; Meier & Reinecke, 2021; Orben, 2020), Haidt’s claims have not been analyzed comprehensively. So far, most data come from older populations, with high quality data on young users still being rare. Therefore, using a large-scale dataset from Austrian high schools this study empirically examines five of Haidt’s central claims:

1. Well-being, particularly depression, is deteriorating.

2. Social media negatively impacts youth well-being.

3. Social networking sites are more harmful than messaging apps.

4. Effects are more harmful for girls.

5. Effects are more harmful for younger users.

The dataset, collected over the past two years, includes responses from more than 20.000 participants (Mean age = 14 years; 56% girls). It measures well-being (depression symptoms, satisfaction across seven life domains), media use (social networking, messaging, or streaming), and control variables (sociodemographics, family background). Regression analyses will be used to examine potential effects on well-being while accounting for third variables. To qualify as relevant, effects need to be significantly larger than beta = .05 (Funder & Ozer, 2019).