Background: The fear of missing out (FoMO) on social media refers to the apprehension that online content and interactions from others are unseen and reacted to in a timely fashion. FoMO can become problematic, leading to anxiety, interrupted sleep, lack of concentration and dependence on social media to generate gratification. The literature has mainly focused on understanding the FoMO experience, factors contributing to it and its consequences. Method: In this paper, we build on previous research and develop a FoMO Reduction (FoMO-R) approach that embraces technical elements such as autoreply, filtering, status, education on how FoMO occurs and skills on how to deal with it; e.g., self-talk and checklists. We evaluate the method through focus groups and a diary study involving 30 participants who self-declared to experience FoMO regularly. Results: The results show that the method was accepted by the participants and helped them to manage their FoMO. They also show that a set of extra functionalities in social media design is needed so that users can manage FoMO more effectively. Conclusion: FoMO can be reduced through socio-technical approaches, joining both social and technical skills, and literacy on how social media are designed and how social interactions should happen on them.

Crowd-based requirements engineering (CrowdRE) could significantly change RE. Performing RE activities such as elicitation with the crowd of stakeholders turns RE into a participatory effort, leads to more accurate requirements, and ultimately boosts software quality. Although any stakeholder in the crowd can contribute, CrowdRE emphasizes one stakeholder group whose role is often trivialized: users. CrowdRE empowers the management of requirements, such as their prioritization and segmentation, in a dynamic, evolved style through collecting and harnessing a continuous flow of user feedback and monitoring data on the usage context. To analyze the large amount of data obtained from the crowd, automated approaches are key. This article presents current research topics in CrowdRE; discusses the benefits, challenges, and lessons learned from projects and experiments; and assesses how to apply the methods and tools in industrial contexts. This article is part of a special issue on Crowdsourcing for Software Engineering.

Within recent years there has been increasing societal concern around the compulsive and excessive use of digital and Internet-enabled devices, such as the use of social media or online video gaming, and associated psychological and physical harms. However, problematic use or addictive behaviours are not yet included as diagnosable mental health issues in any major diagnostic system in Western countries and the conceptualisations of the phenomena are still inconsistent. To address this issue, the present study reviewed the current conceptualisations of digital addiction used within the research literature and identified common features of the definition of digital addiction. Definitions of the phenomenon were extracted from 47 studies, and they were analysed using a content analysis approach. The initial process assessed definitions for features of digital addiction within Internet, gaming and smartphone addiction. Two higher-order themes were identified, which focused on the harm caused by the phenomenon and on the user?s behaviours associated with the phenomenon. It was also found that key constructs are not specific to the usage domain, i.e. whether it is related to gaming, Internet or smartphone use. Several core features were found across different conceptualisations of digital addiction within the literature; however, it was also noted that some features are subjective and inconsistently applied. If a decision is to be reached on whether the phenomenon is a mental health disorder, then clearer definitions must be created.

The rapid rise of ChatGPT has introduced a transformative tool that enhances productivity, communication, and task automation across industries. However, concerns are emerging regarding the addictive potential of AI large language models. This paper explores how ChatGPT fosters dependency through key features such as personalised responses, emotional validation, and continuous engagement. By offering instant gratification and adaptive dialogue, ChatGPT may blur the line between AI and human interaction, creating pseudosocial bonds that can replace genuine human relationships. Additionally, its ability to streamline decision-making and boost productivity may lead to over-reliance, reducing users' critical thinking skills and contributing to compulsive usage patterns. These behavioural tendencies align with known features of addiction, such as increased tolerance and conflict with daily life priorities. This viewpoint paper highlights the need for further research into the psychological and social impacts of prolonged interaction with AI tools like ChatGPT.