Problematic social media use (PSMU) refers to excessive uncontrolled use of social media which impacts upon daily functioning (Blackwell et al., 2017). Self-regulation is central to the development and experience of PSMU, and conceptually interrelates with individual usage motivations (Reinecke et al., 2022). While there is a growing body of research on social media use motivations, how usage motivations and self-regulation combined influence PSMU is not well understood. There are also persistent questions around the effectiveness of addiction-based measures of PSMU. The quantitative component of this nested mixed-methods study (N = 607) employed hierarchical regression and structural equation modelling, principally identifying that impulsive social media usage mediates the pathway between perceived executive/attentional functioning and the Bergen Social Media Addiction Scale (BSMAS, Andreassen et al., 2012, 2016), a popular tool used to measure PSMU. In contrast, social-engagement motivations had a negative influence on the BSMAS. The qualitative component, comprising interview/open-ended questionnaire, explored individual experiences self-regulating social media use. Participants (N = 24) were recruited from the survey study, based on meeting screening criteria for executive dysfunction (Adult Self-Report ADHD Scale, Kessler et al., 2005), with sub-groups defined by top and bottom quartile BSMAS scores (evenly grouped). Thematic analysis found that most individuals with attention dysregulation, regardless of their BSMAS category, perceive self-regulation of social media use as highly challenging and effortful, describing broadly problematic relationship with social media. They also described rich combination of motivations and context of using social media, and strategies for managing use. This research questions the effectiveness of the BSMAS as a measure of general PSMU (lacking a formed self-regulation component), especially in individuals with attentional dysregulation. Future research investigating self-regulation strategies and focusing on characteristics of positive social media use is needed.

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This article presents a new interactive visualization for exploring large hierarchical structures by providing visual cues on a node link tree visualization. Our technique provides topological previews of hidden substructures with three types of visual cues including simple cues, tree cues and treemap cues. We demonstrate the visual cues on Degree-of-Interest Tree (DOITree) due to its familiar mapping, its capability of providing multiple focused nodes, and its dynamic rescaling of substructures to fit the available space. We conducted a usability study with 28 participants that measured completion time and accuracy across five different topology search tasks. The simple cues had the fastest completion time across three of the node identification tasks. The treemap cues had the highest rate of correct answers on four of the five tasks, although only reaching statistical significance for two of these. As predicted, user ratings demonstrated a preference for the easy to understand tree cues followed by the simple cue, despite this not consistently reflected in performance results.