This chapter opens the book with a puzzle. It starts by delineating two tales about the Chinese internet – one emphasizing state control and the other digital innovation and the rapid growth of China’s tech industry. To make sense of these two seemingly contradictory stories of greater openness to support digitalization while also increasing political control – also referred to as digital dilemma – the chapter introduces the core elements of popular corporatism and looks at how it differs from the more familiar command and control that builds on strong hierarchically organized state-centered logics. It considers existing work on digital dilemma and posits that its digital governance approach to the Chinese model is based on the dynamic relationship between the state, platform firms, and citizens. Because voluntary participation of citizens is important for the state to obtain feedback about their policies as well as for the companies to develop their organizational and informational resources in the state–company partnership, it focuses on two examples of digital participatory spaces in this book – social media platforms and the social credit system (SCS) – to illustrate the Chinese model during Hu and Xi’s leaderships.

Steeves revisits empirical data about young people’s experiences on social media to provide a snapshot of what happens to the interaction between self and others when community is organized algorithmically. She then uses Meadian notions of sociality to offer a theoretical framing that can explain the meaning of self, other, and community found in the data. She argues that young people interact with algorithms as if they were another social actor, and reflexively examine their own performances from the perspective of the algorithm as a specific form of generalized other. In doing so, they pay less attention to the other people they encounter in online spaces and instead orient themselves to action by emulating the values and goals of this algorithmic other. Their performances can accordingly be read as a concretization of these values and goals, making visible the agenda of those who mobilize the algorithm for their own purposes.

Digital innovation has the potential to be transformative to both clinical practice and academic research related to mental health. Recent advances in research and consumer-grade technology, combined with society’s rapid and widespread adoption of digital technology, has created an emerging and dynamic field attracting the interest of clinicians, researchers, and service-users alike. In this chapter we summarise potential applications of digital technology to mental health research and clinical practice, including digital phenotyping, smartphone applications, virtual reality, and teletherapy. We summarise how digital technologies might be applied to enhance psychiatric assessment and treatment, as well as in research settings. In particular, we outline the potential benefits of digital technology as clinical and research tools. We also explore the challenges associated with digital innovation in mental health, including ethical concerns, methodological considerations when critiquing research in this field, and considerations from the service-user perspective.

The 10-item Photo Manipulation Scale (P-MaS; McLean et al., 2015) measures the frequency to which a person manipulates (digitally edits) their own appearance within photos prior to sharing on social media or other online platforms. Photo editing practices may include changing the size or shape of body parts, removing skin blemishes, and applying filters, which are easily performed through readily available phone apps. The P-MaS can be administered online or in-person to adolescents and adults and is free to use. This chapter first discusses the development of the P-MaS and then provides evidence of its psychometrics. While mixed evidence for its unidimensionality has been observed, the authors recommend the calculation of a single total score. Internal consistency reliability, test-retest reliability, convergent validity, discriminant validity, and test-criterion validity support the use of the P-MaS. Next, this chapter provides the P-MaS items in their entirety, instructions for administering the P-MaS to participants, item response scale, and scoring procedure. Links to known translations are provided. Logistics of use, such as permissions, copyright, and contact information, are provided for readers.

The 12-item Online Appearance-Relevant Peer Conversations Scale (OARPCS; Cai, Lam, Leong, & Jackson, 2020) assesses (1) physical appearance and (2) life stressors as foci of online peer communications. The OARPCS can be administered to adolescents and/or adults and is free to use in any setting. In this chapter, we first discuss the development of the OARPCS and then summarize evidence for its psychometrics. Specifically, the OARPCS has been found to have a two-factor structure within exploratory and confirmatory factor analyses that is invariant across samples of young women and men in China and Macau. As well, the OARPCS has internal consistency reliability, test-retest reliability, convergent validity, discriminant validity and predictive validity support. Subsequently, this chapter presents the OARPCS items in their entirety, instructions for its administration, the item response scale, and the scoring procedure. Finally, logistics of use, including permissions, copyright, and contact information, are provided for readers.

The 18-item Social Media Appearance Preoccupation Scale (SMAPS) has three subscales that measure social comparison (6 items), self-presentation (7 items), and general activity (5 items) related to appearance when engaging in visual online content, especially using social media. The subscales can form a total score or be used to focus on one or more of the three subscales of online appearance preoccupation, which allows for a shorter measure. The SMAPS was designed with five issues in mind: 1) image sharing activity, 2) investment and self-presentation, 3) social comparison, 4) active versus passive social media use, and 5) negative responses to social media. The SMAPS can be administered online or in-person and has been included in research with adolescents and young adults. The SMAPS is free to use. This chapter begins with a discussion of the development of the SMAPS from item generation to psychometric analyses, which is followed by sections with psychometric information, including the factor structure and invariance, and evidence of reliability and validity. Additional sections cover administration and scoring, the response scale, the items in their entirety, instructions for administration and scoring, and permissions, copyright and contact information are provided.

This chapter examines the evolution of freedom of expression doctrines in the context of digital media. It focuses on how the European Court of Human Rights has adapted traditional doctrines, rooted in Article 10 of the European Convention on Human Rights, to address challenges in the new media environment. The authors explore how freedom of expression, vital to democracy, must balance individual rights, societal interests, and the regulation of disinformation and hate speech in the digital age. The Court’s defence of democracy has evolved from responding to authoritarian threats in Europe’s past to addressing new risks in the digital realm. Principles such as proportionality, the prohibition of abuse of rights (Article 17), and preventing the misuse of democratic freedoms (Article 18) guide limitations on expression, ensuring they are justified and not misused for political suppression. Social media has expanded the concept of ‘public watchdogs’ beyond traditional media to include non-governmental organisations, bloggers, and activists. This evolution demands rethinking the liability and freedom of expression doctrines for platforms.

Chapter 5 explains the persistence of the impunity agenda. It argues that Trump’s resurgence in 2024 has already thrust the agenda back to the forefront and that it could even be self-executing after Trump eventually leaves office. Sections 5.1 and 5.2 show how the conservative media space and an emboldened right wing in Congress discourage advocates from backing away from publicly testing IHL. Section 5.3 traces the expanding coalition of the impunity movement, which now not only includes Fox News and Republican lawmakers but also lobbyist organizations and troops granted clemency by Trump. Section 5.4 describes how close-knit professional and social networks amplify the power of the impunity coalition, even more now given Trump’s re-ascendance to the White House and Pete Hegseth’s selection as Defense Secretary.

We delve into how the digital revolution has affected the violation of, fight for, and documentation of human rights across the world. The Internet has impacted human rights in both positive and negative ways, and we discuss both direct and indirect ways this relationship has developed. The first part examines how the Internet has changed how people organize and speak up about human rights. Protesters use the Internet to organize their opposition, and new, transnational online actors are bringing together people from all over the world to campaign for climate justice, social reforms, or civil and political rights. The second part outlines ways in which the Internet is impacting media freedom and freedom of information. We present an example of how human rights movements have developed new tools to document human rights violations and to identify perpetrators to hold them accountable. In the third part we turn to the concept of digital repression – where governments use online tools to repress their citizen. We show how online censorship, surveillance, and propaganda are used to stabilize repressive governments, and present case studies on how hate speech and intrusive spyware have been linked to physical integrity violations.

Regardless of where you sit, digital is here to stay, and hiding from it won’t change that. Rather, understand it, learn, and accept the benefits and risks as inseparable entities. By understanding digital, we may just understand our patient a little more too. Stepping beyond the patient, and looking at it from a clinical perspective, the world of digital and its application to healthcare has so much more to offer. For mental health services in particular, there is a wide range of digital opportunities to support teams to work differently. This can range from apps, chatbots, online assessments, monitoring and therapies, virtual reality, virtual administration, electronic prescribing and much more. Obviously, it is paramount we consider issues such as safeguarding, cybersecurity and privacy, but the genie is out of the bottle and digital is here to stay!

Many of the psychological topics we have discussed have focused on the mind of the individual, but humans are a fundamentally social species. Recently, the nature of our social interactions has transformed, through our new abilities to connect with people online. This chapter discusses psychological principles of social networks, and how to quantify social networks via graph theory. The chapter examines the small-world phenomenon and the role of social ties via these graph theory measures. We then look at the case of online social networks, what can be learned about you from your profile, and how their use impacts psychological measures. The chapter concludes by showcasing findings on social network representations in the brain, and touching on ethical questions related to social media privacy concerns and AI-based social interactions.

Misinformation on social media is a recognized threat to societies. Research has shown that social media users play an important role in the spread of misinformation. It is crucial to understand how misinformation affects user online interaction behavior and the factors that contribute to it. In this study, we employ an AI deep learning model to analyze emotions in user online social media conversations about misinformation during the COVID-19 pandemic. We further apply the Stimuli–Organism–Response framework to examine the relationship between the presence of misinformation, emotions, and social bonding behavior. Our findings highlight the usefulness of AI deep learning models to analyze emotions in social media posts and enhance the understanding of online social bonding behavior around health-related misinformation.