Social media use and mental health

Interpretation of existing research into the association between SM use and mental health is complicated by the fact that extant studies have explored an array of SM constructs (from self-reported time spent on any SM site, to defined behaviours undertaken on specific platforms) and mental health constructs (from specific disorders, to general risk factors for psychopathology), which are likely to exhibit distinct patterns of association. In response, Meier and Reinecke (Reference Meier and Reinecke2020) developed two organising frameworks to ‘systematize conceptual and operational approaches’ (p. 1) to computer-mediated communication (CMC) (including SM use) and mental health research.

The first framework, which focused on CMC, defined four ‘channel-centred’ levels of analysis, which included the device (e.g. smartphone), type of application (e.g. SM), application brand (e.g. Facebook), and application/brand feature (e.g. private messaging), in addition to two ‘communication-centred’ levels of analysis, which included the nature of the interaction (e.g. active vs passive engagement) as well as the message/communication itself. The second framework, which focused on mental health, distinguished between psychopathology (which they broke down further into internalising and externalising psychopathology, e.g. anxiety and aggression, respectively), and psychological wellbeing.

With respect to the existing evidence base into the association between SM use and mental health/wellbeing, a great deal has focused on the level of application or application brand (channel-centred research), e.g. time spent on SM, frequency of use, or intensity of use (Meier and Reinecke, Reference Meier and Reinecke2020). Within this research, however, the majority of studies have drawn on single-platform data, such as studies of Facebook use or cross-platform data that do not differentiate between platforms, thereby precluding identification of differential effects based on platform brand, let alone brand features (Schønning et al., Reference Schønning, Hjetland, Aarø and Skogen2020).

Systematic reviews and meta-analyses of overall levels of SM use (i.e. application level analyses) are relatively consistent, however, with evidence for a weak association between higher usage and poorer mental health (Abi-Jaoude et al., Reference Abi-Jaoude, Treurnicht Naylor and Pignatiello2020; Orben, Reference Orben2020a), including symptoms of depression, anxiety and general distress (Abi-Jaoude et al., Reference Abi-Jaoude, Treurnicht Naylor and Pignatiello2020; Keles et al., Reference Keles, McCrae and Grealish2019; McCrae et al., Reference McCrae, Gettings and Purssell2017; Orben, Reference Orben2020a). In a meta-review of meta-analyses within the field, Meier and Reinecke (Reference Meier and Reinecke2020) found that individuals who used social network sites more intensely reported more internalising psychopathology, with an effect size in the range of r ≈ 0.05–0.2. Evidence therein, however, did not support associations between overall levels of social network site use and wellbeing/life satisfaction (as opposed to psychopathology), and no meta-analyses were found to have explored SM’s link to externalising psychopathology. See Valkenburg, Meier, and Beyens (n.d.) also.

With respect to directions of causality, although relatively rare (Orben, Reference Orben2020a), where longitudinal or experimental methods have been employed, the evidence is mixed, suggesting possible bidirectional/reciprocal effects; thus, whilst high levels of SM use may impact negatively on mental health, poorer mental health may also drive increased SM use (Aalbers et al., Reference Aalbers, McNally, Heeren, de Wit and Fried2019; Frison and Eggermont, Reference Frison and Eggermont2017; Hunt et al., Reference Hunt, Marx, Lipson and Young2018; Mosquera et al., Reference Mosquera, Odunowo, McNamara, Guo and Petrie2020; Orben and Przybylski, Reference Orben and Przybylski2019).

Putative mechanisms underlying the proposed harms of SM use

A number of mechanisms underlying associations between SM use and poorer mental health have been hypothesised. One common idea, known as the social displacement hypothesis, proposes that online activity competes for time that would otherwise be spent engaged in what is presumed to be healthier or more productive activities, such as sleeping, studying or socialising, the latter with potential implications for social development. However, evidence for the social displacement hypothesis is weak (Hall et al., Reference Hall, Kearney and Xing2019b; Valkenburg and Peter Reference Valkenburg and Peter2007), and it is likely that for some, SM actually facilitates social interactions, both online and offline (Valkenburg and Peter Reference Valkenburg and Peter2007); more on this below. Further, one study concluded that SM in fact displaces neutral or unpleasant activities rather than pleasurable or rewarding ones (Hall et al., Reference Hall, Johnson and Ross2019a).

In contrast, evidence for sleep disruption and/or displacement of sleep amongst heavy SM users (Scott et al., Reference Scott, Biello and Woods2019), and heavy digital screen users more generally, is relatively robust (Orben and Przybylski, Reference Orben and Przybylski2020b), although reported effect sizes are small (Orben and Przybylski, Reference Orben and Przybylski2020b). This is of potential interest here as sleep disturbance has been linked to a number of social difficulties (Gordon et al., Reference Gordon, Mendes and Prather2017), as well as a wide range of mental health disorders during adolescence (Tarokh et al., Reference Tarokh, Saletin and Carskadon2016). However, once again, experimental evidence indicates a potential bidirectional causal relationship (Bartel et al., Reference Bartel, Scheeren and Gradisar2019; Exelmans and Van den Bulck, Reference Exelmans and Van den Bulck2016; Tavernier and Willoughby, Reference Tavernier and Willoughby2014).

Perhaps the most commonly explored ‘communication-centred’ factors linked to negative outcomes are online social comparisons, and potentially relatedly, passive use. Thus, social comparisons theory (Festinger, Reference Festinger1954) proposes that our sense of self is derived, in part, from how we judge ourselves to be doing in comparison with others, and high levels of online upward social comparisons have been linked to poor self-esteem and symptoms of anxiety and depression (Kelly et al., Reference Kelly, Zilanawala, Booker and Sacker2018; Liu et al., Reference Liu, Zhou, Yang, Kong, Niu and Fan2017b; Schmuck et al., Reference Schmuck, Karsay, Matthes and Stevic2019; Tibber et al., Reference Tibber, Zhao and Butler2020; Vogel et al., Reference Vogel, Rose, Roberts and Eckles2014; Vogel et al., Reference Vogel, Rose, Okdie, Eckles and Franz2015; Wang et al., Reference Wang, Wang, Gaskin and Hawk2017), potentially in a causal manner (Vogel et al., Reference Vogel, Rose, Roberts and Eckles2014). Furthermore, in a meta-review by Meier and Reinecke (Reference Meier and Reinecke2020), the authors reported a small association between higher social comparisons on social network sites and symptoms of depression (r = 0.23; 95% CIs = 0.12, 0.34), which was greater for upward social comparisons (r = 0.33; 95% CIs = 0.20, 0.47), i.e. comparisons made with those perceived as better off than oneself.

With respect to passive SM use (e.g. scrolling or browsing), it has been proposed that this is linked to more negative outcomes than active use (e.g. self-disclosure and online exchanges with others), with the association potentially being causal (Frison and Eggermont, Reference Frison and Eggermont2016; Frison and Eggermont, Reference Frison and Eggermont2017; Kim and Lee Reference Kim and Lee2011; Shaw et al., Reference Shaw, Timpano, Tran and Joormann2015; Verduyn et al., Reference Verduyn, Lee, Park, Shablack, Orvell, Bayer, Ybarra, Jonides and Kross2015; Verduyn et al., Reference Verduyn, Ybarra, Résibois, Jonides and Kross2017; Wang Reference Wang2013; Wenninger et al., Reference Wenninger, Krasnova and Buxmann2014). In fact, the negative effects of passive use and social comparisons may be linked, as passive use is itself associated with higher levels of social comparisons and associated feelings of envy (Appel et al., Reference Appel, Gerlach and Crusius2016; Krasnova et al., Reference Krasnova, Widjaja, Buxmann, Wenninger and Benbasat2015; Tandoc et al., Reference Tandoc, Ferrucci and Duffy2015; Verduyn et al., Reference Verduyn, Lee, Park, Shablack, Orvell, Bayer, Ybarra, Jonides and Kross2015; Verduyn et al., Reference Verduyn, Ybarra, Résibois, Jonides and Kross2017).

Consistent with the validity of an active/passive distinction, in their meta-review Meier and Reinecke (Reference Meier and Reinecke2020) found that whilst active ‘interactions’ on social network sites (e.g. replying, commenting and liking) were related to positive wellbeing (r = 0.14, 95% CIs = 0.08, 0.2), more passive ‘content consumption’ (e.g. browsing, searching, monitoring) was linked to negative wellbeing (r = –0.14, 95% CIs = –0.2, –0.8). However, the authors reported that the evidence for associations between interaction level factors (including active vs passive use) and psychopathology (rather than wellbeing) was ‘scarce and inconsistent’ (p. 19). Relatedly, a recent paper reviewing the literature on active/passive use failed to find evidence for its association with wellbeing or mental health, leading the authors to conclude that such a distinction may be too coarse (Valkenburg, Driel and Beyens, n.d.). Other authors have similarly critiqued a simple active/passive dichotomy (Kross et al., Reference Kross, Verduyn, Sheppes, Costello, Jonides and Ybarra2021; Meier et al., Reference Meier, Gilbert, Börner and Possler2020), suggesting that precisely what is being actively/passively engaged with in such interactions may be crucial (Valkenburg, Driel and Beyens, n.d.).

Speaking to this, in their overview of the literature, Yang et al. (Reference Yang, Holden and Ariati2021) proposed that passive viewing of one’s own profile may actually increase self-esteem, most probably because of the heavily curated nature of online profiles (including one’s own), which means that the self is portrayed in a positive light (Gonzales and Hancock, Reference Gonzales and Hancock2011). In addition, they noted that even where passive use does lead to social comparisons, the nature of these social comparisons may be critical. Thus Yang et al. (Reference Yang, Holden and Ariati2021) suggested that whilst judgemental comparisons, e.g. comparing one’s attractiveness or wealth to others’, are typically associated with negative outcomes, non-judgemental comparisons, e.g. of opinions and perspectives for informational purposes, may actually lead to mental health benefits (Park and Baek, Reference Park and Baek2018). Thus, passive use may be problematic specifically in instances where it leads to judgemental social comparisons.

Another possible mechanism of interest driving the association between SM use and poor mental health, which has received relatively little research attention to date, involves the potential for the nature of online communication to amplify or exacerbate maladaptive cognitions and/or facilitate problematic behaviours associated with pre-existing mental health difficulties and/or specific personality structures (Chohan and D’Souza, Reference Chohan and D’Souza2020). For example, online symptom-checking may fuel health anxiety (Doherty-Torstrick et al., Reference Doherty-Torstrick, Walton and Fallon2016; McMullan et al., Reference McMullan, Berle, Arnáez and Starcevic2019), links have been made between SM use (particularly appearance-related interactions and upward comparisons) and body dissatisfaction/body image disturbance (Hogue and Mills, Reference Hogue and Mills2019; Holland and Tiggemann, Reference Holland and Tiggemann2016; Meier and Reinecke, Reference Meier and Reinecke2020; Sidani et al., Reference Sidani, Shensa, Hoffman, Hanmer and Primack2016), and exposure to online self-harm material may increase suicidality (Arendt et al., Reference Arendt, Scherr and Romer2019; Memon et al., Reference Memon, Sharma, Mohite and Jain2018).

Finally, SM users may also be exposed to a number of explicit online risks, including those of a criminal nature (El Asam and Katz Reference El Asam and Katz2018), such as fraud, identity-theft, abuse and harassment, grooming, exploitation, radicalisation as well as exposure to age-inappropriate material, reputational damage, and cyber-bullying (Baccarella et al., Reference Baccarella, Wagner, Kietzmann and McCarthy2018; Department for Education, 2019; Sheldon et al., Reference Sheldon, Rauschnabel and Honeycutt2020; Subrahmanyam and Šmahel, Reference Subrahmanyam and Šmahel2011d). However, whilst the online world has broadened opportunities for such exploitation and abuse, the relative risk and frequency of such risks on- and offline should be considered. For example, a meta-analysis found that traditional (i.e. offline) bullying was twice as common as online bullying (Modecki et al., Reference Modecki, Minchin, Harbaugh, Guerra and Runions2014).

Putative mechanisms underlying the proposed benefits of SM use

Whilst the benefits of SM use have typically received much less attention to date, in research as well as the popular media, a number of health benefits have been documented. Inverting some of the proposed harms of SM use, many of these have focused on SM’s potential to facilitate social developmental processes, e.g. through opportunities for identity experimentation, self-expression and social connection, etc., rather than displacement of such opportunities (Subrahmanyam and Šmahel, Reference Subrahmanyam and Šmahel2011a; Subrahmanyam and Šmahel, Reference Subrahmanyam and Šmahel2011c).

Much research has, in particular, focused on the potential for SM use (and the use of digital technologies more broadly) to lead to an accumulation of social resources or cultivation of a sense of connectedness. For example, with respect to ‘channel-centred’ studies focusing on analyses at the level of device and application, Meier and Reinecke (Reference Meier and Reinecke2020) showed in their meta-review that individuals who used SM more intensely reported higher levels of social capital and social support. Relatedly, in a meta-analysis, Liu et al. (Reference Liu, Ainsworth and Baumeister2016) reported a positive association between general social network site use and social capital (r = 0.32, 95% CIs = 0.27, 0.37). Despite this, surprisingly few studies have, to date, extended these findings to directly test a mediating role for such SM-driven accumulation of social capital in mental health and wellbeing (Lomanowska and Guitton, Reference Lomanowska and Guitton2016). There are exceptions, however; for example, in a study of 300 Korean adults, increases in social connectedness were found to mediate the effects of SM use on subjective wellbeing (Ahn and Shin, Reference Ahn and Shin2013).

Despite the relative robustness of findings as to the potential for SM use to result in the accumulation of social capital/social support, for a number of reasons the association is unlikely to be straightforward. First, it is likely underpinned by multiple (potentially interacting) pathways, with the potential for bi-directional causality. For example, whilst feelings of disconnection may drive greater SM use, engagement with SM sites may itself cultivate feelings of connection, such that seemingly contradictory findings may emerge, even within the same dataset (Sheldon et al., Reference Sheldon, Abad and Hinsch2011).

Second, individual-level factors, i.e. attributes of the user, may moderate such associations. For example, whilst the social enhancement (or ‘Rich Get Richer’) theory proposes that individuals who are popular and/or socially resourceful offline build their popularity and social connections further through online interactions, the social compensation (or ‘Poor Get Richer’) theory suggests that individuals who are less popular and/or less socially resourceful offline compensate for this through their online interactions (Zywica and Danowski, Reference Zywica and Danowski2008). The social compensation theory may be particularly relevant to the field of mental health. Thus, there is evidence to suggest that individuals who struggle socially and/or with their mental health, including those who are low in self-esteem (Ellison et al., Reference Ellison, Steinfield and Lampe2007), socially anxious (Indian and Grieve, Reference Indian and Grieve2014), socially isolated or excluded (Andrade and Doolin, Reference Andrade and Doolin2016; Khosravi et al., Reference Khosravi, Rezvani and Wiewiora2016), lacking in social/familial support (Keresteš and Štulhofer, Reference Keresteš and Štulhofer2020), or else struggle with social communication (Mazurek, Reference Mazurek2013), may stand to benefit the most from online engagement in terms of improved wellbeing and/or the accumulation of social capital. For these individuals, SM may provide opportunities for connection and support, self-disclosure and identity experimentation that would otherwise be unattainable or else feel unsafe/unmanageable in the offline world (Bonetti et al., Reference Bonetti, Campbell and Gilmore2010).

Third, social capital is a broad, multifaceted construct; for example, as a minimum Putnam (Reference Putnam2001) and others have distinguished between bridging social capital (i.e. ‘weak ties’ between distantly connected people) and bonding social capital (i.e. ‘strong ties’ between close family or friends), and these may differ in their strength and/or patterns of association with SM use (Liu et al., Reference Liu, Ainsworth and Baumeister2016). For example, Yang et al. (Reference Yang, Holden and Ariati2021) mapped a complex pattern of associations between SM use and wellbeing that was dependent on whether engagement was with ‘strong ties’ or ‘weak ties’, and whether associated engagement was active in nature, or passive. Furthermore, they distinguished between two forms of active SM engagement: (i) active interaction/directive communication, in which the user directly interacts or communicates with others, and (ii) active broadcasting, which they define as ‘actively producing or sharing texts, photos, and videos to an unspecified audience’ (p. 5). Reviewing the evidence, Yang et al. (Reference Yang, Holden and Ariati2021) concluded that benefits to mental health are most likely when individuals use SM specifically in order to engage with close associates in an active/interactive manner (Seo et al., Reference Seo, Kim and Yang2016), a pattern that they suggested is likely to be mediated by increased social support. Thus, being active online may not suffice; instead, active and purposeful interaction may be optimal for benefits to be accrued.

Examples of active/interactive patterns of engagement that have been linked to social capital and/or wellbeing include the use of SM to meet new people or stay connected with offline contacts (Ellison et al., Reference Ellison, Steinfield and Lampe2007), self-disclosure (Gonen and Aharony, Reference Gonen and Aharony2017; Liu et al., Reference Liu, Ainsworth and Baumeister2016) and active replying to others (Liu et al., Reference Liu, Ainsworth and Baumeister2016). For example, one study had participants increase their posting behaviour on Facebook over the course of a week; relative to a control group that received no instructions, at the end of the study the experimental group reported reductions in loneliness that were driven by a greater sense of connection with friends (große Deters and Mehl, Reference große Deters and Mehl2013). Furthermore, increased perceived online support has been found to mediate effects of active Facebook use on (less) depressed mood and lower levels of loneliness (Frison and Eggermont, Reference Frison and Eggermont2016; Seo et al., Reference Seo, Kim and Yang2016). One possibility is that such active social engagement facilitates the cultivation of intimacy and building of connectedness (Lomanowska and Guitton, Reference Lomanowska and Guitton2016).

Finally, as a counter to SM’s potential to increase exposure to tangible risks and harms, SM use may also provide opportunities for access to concrete rewards and benefits, including (amongst other things) opportunities for learning (Bruguera et al., Reference Bruguera, Guitert and Romeu2019), financial revenue and career opportunities (Tang et al., Reference Tang, Gu and Whinston2012), access to news (Nielsen and Schrøder, Reference Nielsen and Schrøder2014) and entertainment, peer support and specialist knowledge (Naslund et al., Reference Naslund, Aschbrenner, Marsch and Bartels2016). Relatedly, SM may also play a supportive or curative role for some with pre-existing mental health difficulties, e.g. through mental health initiatives, online support and provision of specialist information (Luxton et al., Reference Luxton, June and Fairall2012; Moorhead et al., Reference Moorhead, Hazlett, Harrison, Carroll, Irwin and Hoving2013; Naslund et al., Reference Naslund, Aschbrenner, Marsch and Bartels2016; Subrahmanyam and Šmahel, Reference Subrahmanyam and Šmahel2011c).

Uses and Gratifications theory

The Uses and Gratification theory (U&G) seeks to explain how and why people engage with media (Katz et al., Reference Katz, Blumler and Gurevitch1973), with a greater focus on the human side of the human–technology interaction. Aside from being well-established, with a substantial and wide research base to support it (Lev-On, Reference Lev-On2017), U&G is inherently empowering and of clinical utility, as it posits that users are (primarily) active agents (rather than passive recipients) of information that are motivated to seek out specific media in goal-directed ways in order to satisfy (i.e. gratify) needs and desires. [See LaRose and Eastin (Reference LaRose and Eastin2004), however, for details of how SM habits may also be integrated into a U&G-based model of technology use.]

U&G also proposes that such motivations will vary as a function of inter-individual differences in a range of social and psychological factors (Kircaburun et al., Reference Kircaburun, Alhabash, Tosuntaş and Griffiths2020), making it well suited to an idiographic – i.e. case formulation – approach that highlights personal agency and focuses on the workability of use for the individual. For example, findings from one study of college students suggested that individuals who are more anxious in face-to-face interactions and find offline socialising less rewarding may turn to SM as a ‘compensatory’ strategy, i.e. to connect with others and satisfy social needs (Papacharissi and Rubin, Reference Papacharissi and Rubin2000). In contrast, individuals who are less anxious in offline (face-to-face) interactions and find these more rewarding may be drawn to SM more for non-social purposes, e.g. for entertainment and information-seeking.

Critically, U&G also distinguishes between gratifications sought, meaning the intended purpose(s) for which the media is used (e.g. to connect with others), which we will use interchangeably with the term ‘motivations’, and gratifications obtained, meaning the actual consequences of use (e.g. increased feelings of loneliness and disconnection) (Palmgreen, Reference Palmgreen1984), which we will use interchangeably with the term ‘consequences’. For this reason, the U&G is also well suited to a functional contextual approach that emphasises the consequences of use in determining helpful versus unhelpful patterns of use (see ‘The interpersonal–connections–behaviour framework’ and ‘Types of behaviours and their maintaining role’ sections below). For example, research has shown that self-reported satisfaction with social network site use is high when gratifications obtained exceed gratifications sought, i.e. when SM use effectively satisfies the individual’s needs (Bae, Reference Bae2018).

Although not specific to adolescents or social media, U&G has been used to conceptualise young people’s SM use in a number of studies. For example, Cheung et al. (Reference Cheung, Chiu and Lee2011) showed that amongst adolescents/emerging adults, the primary motivations that predict an intention to use Facebook are social in nature, including a desire for social connectivity, social enhancement and social presence, suggesting that young people turn to SM, predominantly in order to gratify social needs. Seen through a developmental lens, such social motivations for SM use may be particularly pertinent for young people (Ciranka and van den Bos, Reference Ciranka and van den Bos2019) because they map onto key pre-occupations and developmental challenges faced, e.g. those relating to peer acceptance, social connectedness, romantic and sexual exploration, and social identity formation (Ciranka and van den Bos, Reference Ciranka and van den Bos2019; Sebastian et al., Reference Sebastian, Viding, Williams and Blakemore2010; Spies Shapiro and Margolin, Reference Spies Shapiro and Margolin2014). Furthermore, the SM environment may afford opportunities for agency and experimentation in relation to such development needs and desires (Šmahel and Subrahmanyam, 2011e) that are more difficult to access in the offline world because of constraints imposed by parents, college and other systemic factors, as well as perceived barriers relating to the self (Valkenburg and Peter, Reference Valkenburg and Peter2008). However, as noted, the SM environment also brings a number of risks and challenges, such that gratifications sought and gratifications obtained need not be perfectly aligned (Bae, Reference Bae2018).

With respect to associations between motivations and mental health outcomes, existing research in the area is scant (particularly in young people), and has traditionally relied on cross-sectional data that preclude causal inferences (Yang et al., Reference Yang, Holden and Ariati2021). Nonetheless, there is some evidence to suggest that interpersonal/relational motivations for SM use, such as maintaining relationships and a sense of connectedness, may be linked to benefits in terms of reduced psychopathology, including lower levels of depression (Wright et al., Reference Wright, Rosenberg, Egbert, Ploeger, Bernard and King2013) and anxiety (Reichelt, Reference Reichelt2019), as well as improved wellbeing, including higher levels of social support satisfaction (Wright et al., Reference Wright, Rosenberg, Egbert, Ploeger, Bernard and King2013), lower levels of loneliness (Teppers et al., Reference Teppers, Luyckx, Klimstra and Goossens2014), positive social adjustment (Yang and Brown, Reference Yang and Brown2013) and life satisfaction (Adnan and Mavi, Reference Adnan and Mavi2015). It is likely that this is because social benefits are more likely to be accrued when SM use is purposefully engaged with for this purpose.

Such interpersonal/relational motivations may be contrasted to non-social (or even socially avoidant) motivations, several of which have been linked to poorer outcomes. For example, in a single population sample of adults, Perugini and Solano (Reference Perugini and Solano2021) showed that whist the use of SM to maintain relationships and seek information was associated with better well-being, its use to pass the time or for exhibitionism (arguably linked to social comparisons through a heightened awareness of relative status) was associated with poorer wellbeing. Relatedly, Yang et al. (Reference Yang, Holden and Ariati2021) distinguished between two classes of SM motivations: (i) enhancement motivations, such as for maintaining existing relationships or seeking entertainment, which function to enhance positive or neutral states, and (ii) compensation motivations, such as meeting new people online, which (may) function to avoid negative affective states and compensate for perceived deficits. Whilst Yang et al. (Reference Yang, Holden and Ariati2021) proposed that such enhancement and compensation motivations may be associated with positive and negative outcomes, respectively, they noted that evidence for this is weak, and further, may reflect socially anxious individuals being driven by compensation motivations, rather than the opposite direction of causality.

The transformation framework

The transformation framework was developed as a comprehensive framework to describe the implications of the SM environment for the establishment and maintenance of peer relationships in adolescence, integrating findings from across multiple disciplines within a clinical and developmental perspective (Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein2018a; Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein2018b). Whilst a comprehensive review of the literature underpinning the transformation framework lies beyond the remits of this paper, it is important to acknowledge – as the authors do – that the framework draws heavily upon (and synthesises) large bodies of research from within the fields of computer-mediated communication, media psychology, developmental psychology and organisational psychology, some of which we have signposted the reader to in the introduction; see Subrahmanyam and Šmahel (Reference Subrahmanyam and Šmahel2011e), Valkenburg (Reference Valkenburg2017) and Walther (Reference Walther1992, Reference Walther, Braithwaite and Schrodt2015) for example. The transformation framework builds on these ideas, however, by relating these processes to the specific challenges of adolescent development and peer relationships, in both dyadic (Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein2018a) and group (Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein2018b) contexts [see Karahanna et al., (Reference Karahanna, Xu, Xu and Zhang2018) also].

According to the transformation framework the SM environment represents a unique and novel social context, distinct from face-to-face interactions, with a number of consequences for the nature of cognitive, behavioural and interpersonal processes that it affords (Moreno and Uhls, Reference Moreno and Uhls2019; Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein2018a; Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein2018b). Thus, whilst online and offline worlds interact, the online world does not merely reflect offline processes, but has the potential to transform it, offering a distinct set of opportunities and risks (Nesi et al., Reference Nesi, Telzer and Prinstein2020). Specifically, the framework proposes that SM communication is characterised by a number of features, including temporal delays in social exchanges (asynchronicity), the creation of an indefinite trace or history of the communication (permanence), access and exposure to potentially large and/or open audiences (publicness), ease of access and engagement (availability), reduced availability of cues, including facial cues (cue absence), countable social metrics, such as likes and shares (quantifiable), and the potential to share photographic and video material (visualness). Different SM platforms will vary on a continuum with respect to each of these dimensions, and consequently, will afford different patterns of engagement (Karahanna et al., Reference Karahanna, Xu, Xu and Zhang2018; Moreno and Uhls, Reference Moreno and Uhls2019); see Eshraghian and Hafezieh (Reference Eshraghian and Hafezieh2017) for a review of the application of affordance theory to SM research. For example, one of the key appeals of Snapchat is that messages are only available for a defined/brief period of time (low permanence). Different tools or setting options within a given platform may also vary with respect to these feature dimensions. For example, Snapchat has now evolved to include longer-lasting and archival functionalities, including the ‘Snapchat Story’ feature, with putative impacts on the nature of user engagement (Cardell et al., Reference Cardell, Douglas, Maguire, Avieson and Giles2017).

Critically, the framework proposes that as a result of these characteristics, social interactions are transformed when they occur online, as noted, both with respect to dyadic and group communication. Specifically, they commonly: (i) change the frequency or immediacy of interactions (e.g. facilitation of high-speed messaging between individuals in a friendship group), (ii) amplify experiences and demands (e.g. intensification of an online dispute because of its public nature and the sheer volume of potential content), (iii) qualitatively change the nature of the experience (e.g. bullying may be perceived as harsher because of the anonymity of the perpetrator), (iv) facilitate compensatory behaviours, i.e. behaviours that may be more challenging in the offline world (e.g. social interactions may feel safer than offline interactions because of the asynchronicity in communication), and (v) provide opportunities for completely new behaviours and experiences (e.g. connection to specialist interest groups with people from far afield) (Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein2018a; Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein2018b).

The transformation framework does not imply that these effects are inherently positive or negative, but instead bring a range of potential risks and benefits. Nonetheless, and critical here with respect to impacts on mental health, Nesi and colleagues propose that, for some, a number of problematic interpersonal – or ‘depressogenic’ – processes may be amplified or increased in frequency within the context of online interactions (Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein2018a; Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein2018b). These include: social comparisons, co-rumination [defined as an excessive discussion of/rumination on problems and stressors with others (Rose, Reference Rose2002)], reassurance-seeking and feedback-seeking (Nesi and Prinstein, Reference Nesi and Prinstein2015). To take one example, (judgemental) social comparisons (and relatedly, negative self-image) may be facilitated online by the visualness, publicness, quantifiableness and availability of the SM environment, which essentially provides a highly detailed and easily accessible window onto others’ lives (Fardouly et al., Reference Fardouly, Pinkus and Vartanian2017; Marengo et al., Reference Marengo, Longobardi, Fabris and Settanni2018). Furthermore, the asynchronicity of the communication may also encourage heavy editing/engineering of photographs and self-representations (Kasch, Reference Kasch2013), such that the impression garnered is likely to be highly curated and idealised. Adolescents may be particularly vulnerable to such processes by virtue of their heightened sensitivity to peer influence (Ciranka and van den Bos, Reference Ciranka and van den Bos2019) and peer rejection (Sebastian et al., Reference Sebastian, Viding, Williams and Blakemore2010), as well as the fluid/developing nature of their identity (Spies Shapiro and Margolin, Reference Spies Shapiro and Margolin2014).

Conversely, several of the SM features explored may serve to afford more positive interactions and behaviours – both compensatory and novel (Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein2018a; Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein2018b). For example, the cue absence and asynchronicity of SM platforms may reduce the intensity of social interactions, and allow for communication to be slowed down, rehearsed and reflected upon (Bonetti et al., Reference Bonetti, Campbell and Gilmore2010; Harman et al., Reference Harman, Hansen, Cochran and Lindsey2005; Michikyan et al., Reference Michikyan, Subrahmanyam and Dennis2014; Valkenburg and Peter, Reference Valkenburg and Peter2008). This may be of particular benefit to individuals who struggle in more traditional face-to-face social settings.

As another example of how design features may facilitate positive behaviour and mental health, whilst the permanence, availability and quantifiable nature of many platforms may facilitate the spread and uptake of problematic behaviours such as self-harm (George, Reference George2019), it also has the potential to amplify prosocial interactions (Erreygers et al., Reference Erreygers, Vandebosch, Vranjes, Baillien and De Witte2019; Tsvetkova and Macy, Reference Tsvetkova and Macy2015). Indeed, there is evidence that with respect to online interactions amongst adolescents, prosocial behaviours may in fact be more common than antisocial ones (Erreygers et al., Reference Erreygers, Vandebosch, Vranjes, Baillien and De Witte2017; Lister, Reference Lister2007). This is crucial as we know that heightened sensitivity to peer influence and peer rejection during adolescence is true of prosocial as well as antisocial behaviour (Andrews et al., Reference Andrews, Ahmed and Blakemore2020). Thus, the very same features of online communication that carry risks also open up opportunities and potential benefits (Hollis et al., Reference Hollis, Livingstone and Sonuga-Barke2020; Livingstone and Helsper, Reference Livingstone and Helsper2010).

Extension of the transformation framework

In addition to the core features of SM platforms proposed by the transformation framework (Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein2018a; Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein2018b), which we have summarised above, we would suggest that SM platforms typically exhibit a number of additional design features, the primary function of which is to compete for users’ attention and maximise engagement (Alter, Reference Alter2017; Hill, Reference Hill2018; Lewis, Reference Lewis2017; Zuboff, Reference Zuboff2015), with potential implications for – and impacts on – the user’s ‘response state’, i.e. their cognitive, emotional and excitative state during engagement (Valkenburg and Peter, Reference Valkenburg and Peter2013), the nature of their engagement (i.e. online behaviour), and potentially, their mental health. These additional features include: (i) filtering algorithms, which selectively propagate information that is trending, (ii) intermittent status updates and notifications (Bosker, Reference Bosker2016; Lindström et al., Reference Lindström, Bellander, Schultner, Chang, Tobler and Amodio2021), and (iii) infinite scrolling and automatic play/replay content features (Noë et al., Reference Noë, Turner, Linden, Allen, Winkens and Whitaker2019).

In terms of the implications of these features for the response state of the SM user and their pattern of engagement, the nature of (i) filtering algorithms means that users are more likely to be exposed to information (e.g. posts and news stories) that they have previously accessed or else are trending within their network (Ciampaglia et al., Reference Ciampaglia, Nematzadeh, Menczer and Flammini2018), which by its very nature is likely to elicit strong emotional responses and high levels of arousal/excitation (amplifying and sensationalising). Thus, even if SM algorithms are not purposefully designed to amplify the emotional or excitative state of its users, the reality is that because emotional material increases attentional capture and user engagement (Brady et al., Reference Brady, Gantman and Van Bavel2020; Kozyreva et al., Reference Kozyreva, Lewandowsky and Hertwig2020; Tufekci, Reference Tufekci2013), there will be an algorithmic pull towards material that is controversial, extreme, polarised (Van Bavel et al., Reference Van Bavel, Rathje, Harris, Robertson and Sternisko2021), divisive and/or likely to elicit strong moral emotions such as outrage (Crockett, Reference Crockett2017; Goldenberg and Gross, Reference Goldenberg and Gross2020). For example, messages that elicit fear, disgust and surprise or include ‘moral-emotional’ words are more likely to spread (Brady et al., Reference Brady, Wills, Jost, Tucker, Van Bavel and Fiske2017; Vosoughi et al., Reference Vosoughi, Mohsenvand and Roy2017). Furthermore, immoral acts are more likely to be encountered online and elicit stronger moral outrage than similar acts learnt about in person or through other more traditional forms of media (Brady et al., Reference Brady, Wills, Jost, Tucker, Van Bavel and Fiske2017).

With respect to (ii) intermittent status updates and notifications (Bosker, Reference Bosker2016; Lindström et al., Reference Lindström, Bellander, Schultner, Chang, Tobler and Amodio2021), because of the uncertainty and unpredictability of social interactions, these will typically follow a variable-ratio reward schedule that will reinforce levels of engagement, i.e. increase usage, through basic operant conditioning processes (reinforcing). Finally, with respect to (iii) infinite scrolling, and relatedly, automatic play/replay content (Noë et al., Reference Noë, Turner, Linden, Allen, Winkens and Whitaker2019), these features maximise attentional capture and allow users to endlessly absorb material with minimal decision points and stopping cues, and hence minimal cognitive engagement with the decision process (absorbing) (LaRose, Reference LaRose2010; Noë et al., Reference Noë, Turner, Linden, Allen, Winkens and Whitaker2019).

In terms of the implications of these for mental health, the reinforcing and absorbing nature of the experience may increase the likelihood of automatic/habitual patterns of engagement (Eyal and Hoover, Reference Eyal and Hoover2014), which are potentially linked to negative mental health outcomes through a number of casual pathways. Whilst these processes (and the links between them) are explained in greater depth in the ‘Consequences and feedback loops’ section, it may suffice to note at this point, that when attention and behaviour is strongly captured by the media itself (reinforcing and absorbing), because of the limited capacity of attentional resources, attention to other cues beyond the screen (both internal and external) will be diminished, including those that may signal a potential benefit to stopping engagement, e.g. feelings of anger or fear triggered by an online interaction. Engagement thus becomes automatic or habitual, conditions under which it is easy to see how problematic patterns of use may emerge (Aarts et al., Reference Aarts, Verplanken and Knippenberg1998; Bae, Reference Bae2018; LaRose, Reference LaRose2010; LaRose et al., Reference LaRose, Lin and Eastin2003; Verplanken et al., Reference Verplanken, Aarts and Van Knippenberg1997).

With respect to the amplifying and sensationalising nature of the experience, this may feed negative emotions and exacerbate cognitive distortions/information processing biases, such as negativity biases, which lay at the core of many mental health difficulties and feed unhelpful cycles of thoughts, feelings/sensations and behaviours (Abramson et al., Reference Abramson, Metalsky and Alloy1989; Riskind, Reference Riskind1997). Again, whilst we explore these processes in greater depth in the ‘Information processing biases’ section, as a simple example, exposure to trending material that is highly emotive and triggering, e.g. graphic details of violent crime, may serve to confirm the beliefs of an anxious individual as to the dangerous nature of the world, thereby reinforcing negativity biases and increasing perceived threat (see Vignette 1 for example).

These reinforcing, absorbing, amplifying and sensationalising processes are also likely to interact. Thus, in their Differential Susceptibility to Media Effects Model (DSMM), Valkenburg and Peter (Reference Valkenburg and Peter2013) propose that the effects of media (including impacts on behaviour and emotion, and by implication mental health) may be most pronounced and long-lasting when ‘cognitive, emotional and excitatory sliders, are high’ (p. 229). Such a state might be expected, we would argue, when the user is highly absorbed and engaged in the technology, and further, consuming and/or interacting (in a relatively uncritical way) with material that is highly emotionally evocative. Once again, there may be an algorithmic pull towards such states, although this is likely to vary across platforms and platform utilities.

The interpersonal–connections–behaviour framework

The interpersonal–connections–behaviour framework proposes a simple but powerful idea: that SM use is (in large part) beneficial or harmful to the individual to the extent that it promotes or frustrates core desires for acceptance and belonging, respectively (social gratifications obtained) (Clark et al., Reference Clark, Algoe and Green2018) [see Verduyn et al. (Reference Verduyn, Ybarra, Résibois, Jonides and Kross2017) also]. Whilst the framework was not designed specifically with adolescence in mind, we would argue that it is particularly relevant to this age group. Thus, acceptance and belonging and avoidance of rejection act as powerful motivators for behaviour during adolescence and are linked to key developmental challenges (Eisenberger et al., Reference Eisenberger, Lieberman and Williams2003; Tomova et al., Reference Tomova, Andrews and Blakemore2021). Furthermore, as noted, social motivations for SM use (gratifications sought) appear to be particularly important in driving this age group’s intention to engage with the technology (Cheung et al., Reference Cheung, Chiu and Lee2011).

In their review of evidence supporting the interpersonal–connections–behaviour framework, Clark et al. (Reference Clark, Algoe and Green2018) suggested that negative outcomes are more likely when SM engagement is superficial and characterised by interaction patterns that are unlikely to cultivate genuine intimacy and long-term satisfaction of core social needs, e.g. ‘social snacking’ (p. 33), and/or patterns of use that are likely to cultivate feelings of disconnection, e.g. ‘lurking on stranger’s profiles’ (p. 33) (Clark et al., Reference Clark, Algoe and Green2018). Thus, they draw on a body of literature that highlights associations between passive patterns of use, e.g. scrolling, browsing and judgemental social comparisons, and poorer mental health/wellbeing. Conversely, Clark et al. (Reference Clark, Algoe and Green2018) highlight findings that link more active/interactive patterns of engagement, e.g. self-disclosure, online posting and direct exchanges with others, to better mental health/wellbeing, putatively through the accumulation of social capital and the cultivation of a greater sense of connectedness. We do not review these findings again here, but instead direct the reader back to the ‘Social media use and mental health’ section.

Integration of core theory

Integrating the evidence base reviewed and core theories/theoretical frameworks discussed within a developmental perspective, we propose that SM engagement may be particularly beneficial or helpful to the adolescent where use results in increased satisfaction of core needs (gratifications obtained), particularly those relating to developmentally pertinent needs of acceptance and belonging. Furthermore, we propose that such satisfaction of needs is most likely to occur when: (i) the individual’s motivations for use (gratifications sought) are primarily for social enhancement purposes, with a focus on cultivating and maintaining deep and genuine connections, (ii) the nature of the user’s online engagement is aligned with such enhancement motivations (e.g. engagement is active/interactive and non-judgemental), and relatedly, (iii) platforms used by the individual afford/support such patterns of engagement. Finally, SM may also be beneficial to mental health where it supports an amelioration of cognitive biases, e.g. negativity biases.

In contrast, SM engagement may be particularly harmful or unhelpful to the adolescent where use results in decreased satisfaction of core needs (gratifications obtained), particularly those relating to developmentally pertinent needs of acceptance and belonging, (e.g. increased feelings of loneliness or disconnection). Furthermore, we propose that this may be most likely when: (i) the individual’s motivations for use are (at least in part) social in nature (gratifications sought), but (ii) satisfaction of these needs is not supported by the individual’s actual online engagement (e.g. engagement is passive and judgemental, characterised in large part by social comparisons), and relatedly, (iii) the platforms which the individual uses do not afford helpful patterns of engagement, or else actively afford unhelpful patterns of engagement. SM engagement may also be harmful or unhelpful to the adolescent where the individual’s motivations for use (gratifications sought) directly conflict with satisfaction of core needs relating to belonging and acceptance, for example where SM use is (predominantly and persistently) motivated by compensatory motivations relating to escape or avoidance. Finally, SM may also be harmful where it feeds into existing cognitive biases that perpetuate unhelpful patterns of thoughts and feelings.

Overview

As noted, at the core of the conceptualisation lies a cross-sectional CBT cycle (McDonough et al., Reference McDonough, Duncan, Williams and House1997), which typically includes triggers, thoughts, feelings, sensations and behaviours (Fig. 1). For simplicity, feelings and sensations are collapsed, and the initial trigger/input to the model is defined as SM use. Other additions include: (i) a box to represent motivations for use, (ii) a box to represent information processing (David et al., Reference David, Miclea and Opre2004), which may be biased or relatively unbiased, and filters incoming (online and offline) information; (iii) a box to represent features and associated affordances of SM platforms (platform affordances), which may influence engagement with SM and how information is processed; (iv) a box to represent consequences or outputs of the model (i.e. gratifications obtained), with (v) feedback loops connecting consequences to motivations/SM use (as well as the user’s mode of engagement), and finally, (vi) a box to represent the mode of SM engagement, i.e. whether engagement is intentional/purposeful, or automatic/habitual.

Figure 1. The conceptualisation / model.

Considering unhelpful patterns of SM use first, the individual has various motivations for engaging with the technology, which are underpinned by needs, including social needs, that they hope to gratify. Incoming information derived from these online interactions (social media use) is filtered by information processing biases (Beck et al., Reference Beck, Rush, Shaw and Emery1979; Wells, Reference Wells1997), which drive and perpetuate unhealthy cycles of thoughts, feelings and behaviours, and ultimately, a number of negative consequences. To take a hypothetical – although relatively typical – example, consider a young SM user (let’s call her Yma) who goes online in order to connect with her peers (motivation) and reads a friend’s online post about a recent holiday. When filtered through a negativity bias, this might elicit negative automatic thoughts such as ‘other people’s lives are better than mine’ or ‘I’m not good enough’, driving a dip in mood, and as a result, a compulsion to socially withdraw and monitor others’ posts for confirmation/disconfirmation of this belief (avoidance behaviour) (Beck et al., Reference Beck, Rush, Shaw and Emery1979; Wells, Reference Wells1997). Whilst this cycle is reminiscent of offline interactions, it may be exacerbated online by the features and associated affordances of the SM platform, e.g. the fact that the material is highly visual, constantly accessible, and linked to social metrics such as likes and shares (availability, visualness, quantifiable). Furthermore, the reinforcing and absorbing nature of the experience, which is intended to hijack attention and maximise usage, may activate an automatic/habitual mode of engagement, which reduces Yma’s capacity to effectively self-monitor and critically evaluate presented material (e.g. recognise that the friend’s profile is likely to be heavily curated). As a result, cognition takes a ‘short-cut’ or uses a heuristic, information processing is biased, and the whole cycle is perpetuated.

Conversely, healthy or non-problematic cycles of thoughts, feelings and behaviours are enabled by relatively veridical information processing. Returning to our hypothetical example, imagine that Yma reads the same holiday post when she is in a very different state of mind, and is able to process the information in a relatively unbiased, and more critical, fashion, able to recognise, for example, the heavily curated nature of her friend’s profile. Relatively free of a negativity bias, thoughts triggered in this very different context might be neutral, or perhaps even linked to a positive memory of a similar childhood holiday with a family friend, such that a feeling of warmth or gratitude is elicited. This, in turn, drives Yma to reach out to the childhood friend (motivation), searching for them online and sending them a personalised message to invite them to join their network (approach behaviour). In this context, the affordances of SM, e.g. its availableness and publicness, will have facilitated this positive cycle, making it easier for Yma to locate and connect with her childhood friend. However, this requires intentional/purposeful engagement with the technology (mode), in the absence of which the pull will be towards a passive scrolling of online material, which has itself been linked to higher levels of (judgemental) social comparisons and associated feelings of envy (Appel et al., Reference Appel, Gerlach and Crusius2016; Krasnova et al., Reference Krasnova, Widjaja, Buxmann, Wenninger and Benbasat2015; Tandoc et al., Reference Tandoc, Ferrucci and Duffy2015; Verduyn et al., Reference Verduyn, Lee, Park, Shablack, Orvell, Bayer, Ybarra, Jonides and Kross2015; Verduyn et al., Reference Verduyn, Ybarra, Résibois, Jonides and Kross2017).

As a result of this cascade of thoughts, feelings/sensations and behaviours, a set of consequences follow, the nature of which ultimately determine whether, irrespective of the motivation for engagement, the original SM behaviour engaged in proves to be helpful or unhelpful. Thus, within the conceptualisation, aside from their impact on information processing biases (and associated exacerbation/amelioration of problematic patterns of thoughts, feelings, sensations and behaviours), patterns of SM use are associated with positive and negative mental health outcomes primarily as a function of their capacity to promote or frustrate (respectively) satisfaction of core needs for acceptance and belonging.

Returning to our previous example, in the unhelpful cycle the ultimate consequences of Yma’s social withdrawal may be a greater sense of loneliness and isolation (and loss of social capital), indicating a lack of alignment between gratifications sought and gratifications obtained. This loneliness is likely to feed into, and perpetuate, a cycle of unhelpful thoughts and feelings (e.g. negative thoughts about self, others and the world), and may set up a vicious cycle whereby feelings of disconnection brought about by SM use increase Yma’s motivation to engage with the technology further in order to seek connection and alleviate such feelings; see Perloff (Reference Perloff2014). This pattern may also be facilitated by negative reinforcement, particularly where SM use provides short-term distraction from such difficult feelings.

In contrast, in the second scenario/helpful cycle, the potential gains include an increased sense of connectedness and belonging, and an accumulation of social capital, indicating a good alignment between gratifications sought and gratifications obtained. This sense of connectedness is likely to feed into – and perpetuate – a cycle of helpful thoughts and feelings (e.g. positive thoughts about self, others and the world). These consequences may also reinforce patterns of SM use, through positive and/or negative reinforcement loops. Here this is unlikely to be problematic, as engagement is linked in this instance to positive consequences; in a small minority of cases, however, there is the possibility that this may topple over into problematic patterns of use (Wartberg et al., Reference Wartberg, Kriston and Thomasius2020).

Below we now go through each of the components of the model in greater depth, explaining how they interact with one another with reference to the relevant literature.

Key components

Assessment and formulation

Given the relative ubiquity of SM use, we think that, as a minimum, an exploration of service users’ online worlds should constitute an integral part of the assessment process, particularly for young people, for whom their online life may be as rich and vivid as their offline world. Whilst this should incorporate exploration of explicit online risks (particularly where impulsive or risky behaviour is indicated more generally, or where the individual is deemed vulnerable in some way), we hope that the conceptualisation will encourage clinicians to go further than this, and explore service users’ patterns of use, online relationships and interactions more generally, and think about how these might interact with existing difficulties to both exacerbate and/or ameliorate symptoms (see Table 1).

Table 1. Ideas for assessment and intervention based on a trans-diagnostic cognitive behavioural conceptualisation of the positive and negative roles of social media use in adolescents’ mental health and wellbeing

This includes (potential) areas for assessment, factors to consider within each of these areas, example questions that can be used to elicit relevant information within each of these areas/factors for consideration, in addition to ideas for possible interventions depending on information gathered. The interventions listed are drawn (in main part) from standard behavioural and cognitbehavive behavioural approaches and are informed by our own clinical practice. Note: the examples presented represent ideas for potential exploration and should be used flexibly depending on context, and in response to material emerging from sessions, rather than used prescriptively as an assessment pro forma or treatment protocol.

Where SM use is seen as playing a significant role in the individual’s life (e.g. as a precipitating, perpetuating or protective factor), these should be collaboratively integrated into an individualised conceptualisation, as per any CBT treatment. Thus, we are not proposing that the conceptualisation be used to inform a ‘CBT for SM use’ protocol, but that instead, the conceptualisation (or components of it where deemed relevant) be integrated into standard CBT formulations or case conceptualisations. At the core of the conceptualisation (Fig. 1) lies a standard cross-sectional CBT cycle, on to which all (or a subset of) the suggested additional components can essentially be ‘bolted on’ (motivations for use, information processing, consequences, platform affordances and feedback loops) to see if these increase the explanatory power/utility of the model. For example, building in an understanding of how SM platforms (platform affordances) can exacerbate cognitive biases and fuel problematic behaviour, e.g. social comparisons in depression, or online symptom checking in health anxiety and panic, may aid the individual’s understanding of their difficulties and what is maintaining them, and critically, suggest targets for intervention (see Vignettes for examples). Conversely, noting where SM represents an under-used resource for cultivation of social connection and identity exploration may highlight new directions for engagement.

Intervention

The primary function of a clinical model or formulation is to inform a treatment plan. Whilst, as we have noted, an individualised case conceptualisation should be constructed in collaboration with the young person, and the purpose of the proposed model is not to ‘treat’ (or pathologise) SM use, the model does suggest a number of potential likely foci for intervention that are associated with core SM-related precipitating, perpetuating and protective factors. These should be addressed at all stages of the treatment process, i.e. integrated into assessment, formulation, psychoeducation and intervention.

With respect to psychoeducation, using the conceptualisation that we have presented, standard cognitive behavioural psychoeducation (e.g. exploration of the role of various behaviours in the perpetuation of core maladaptive cycles), may be integrated with SM literacy training (Livingstone and Helsper, Reference Livingstone and Helsper2010; Torrent, Reference Torrent, Culver and Kerr2014) in order to help the young person understand how the technology interacts with their personal presenting difficulties (for better and for worse), and further, to help them develop ‘the technical and cognitive competencies users need to use social media in an effective and efficient way for social interaction and communication on the web’ (p. 4, Daneels and Vanwynsberghe, Reference Daneels and Vanwynsberghe2017).

According to Schreurs and Vandenbosch’s (2021) Social Media Literacy model (SMILE), such SM literacy serves as both a moderator in the association between SM use and mental health/wellbeing, as well as a predictor; see Valkenburg and Peter (Reference Valkenburg and Peter2013) also. With respect to its role as a moderator, SMILE proposes that the literate user is more aware of the potential negative impacts of their use, as well as certain online material, and as such, is better equipped to regulate their emotional and behavioural responses to it. SMILE also proposes that the literate user will be better equipped to ‘focus on the scarce positive aspects’ of otherwise negative online experiences (p. 324, Schreurs and Vandenbosch, Reference Schreurs and Vandenbosch2021), presumably thereby driving a more adaptive cycle of thoughts, feelings, sensations and behaviours. With respect to the role of SM literacy as a predictor of mental health/wellbeing, SMILE proposes that literate users are more likely to be exposed to more positive/less harmful online material through a more critical and intentional evaluation of who and what to engage with, as well as selective attention towards more positive material. Considered within our conceptualisation, such cultivation of SM/psychological literacy might be seen as equipping and incentivising the user to engage with the technology in a more critical and purposeful way (intentional/purposeful mode of engagement), with all the benefits that this entails, e.g. greater weighing of contextual information, greater control over attention and behaviour, more critical evaluation of material/amelioration of processing biases, etc.

Potential topics for exploration within such psychoeducation/SM literacy training include the heavily curated nature of the online world, i.e. the ‘highlights reel’ (Madden et al., Reference Madden, Lenhart, Cortesi, Gasser, Duggan and Smith2013) or ‘positivity bias’ as a phenomenon (Schreurs and Vandenbosch, Reference Schreurs and Vandenbosch2021), characteristics of SM platforms and their implications for the nature of online interactions (Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein2018a; Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein2018b), e.g. the high prevalence of low quality information/misinformation online (Agarwal et al., Reference Agarwal, Gupta and Kraut2008), and the nature (and implications) of well-documented online psychological phenomena such as online disinhibition (Suler, Reference Suler2004).

In terms of possible foci for active intervention, as with any CBT treatment protocol a good place to start is raising awareness of core processes identified in the conceptualisation, as we cannot begin to change that which we are not aware of. This might involve noticing and identifying typical patterns of SM use and tracking their impact, with a view (once again) to encouraging more mindful and critical engagement, e.g. journalling, thought recording, activity scheduling and behavioural experiments.

With respect to behaviours, values-based work might be introduced to explore where the individual’s SM use (and behaviour more generally) is in line with their needs, values and aspirations, and where discrepancies are identified, strategies incorporated to effect behavioural change (Hayes et al., Reference Hayes, Strosahl and Wilson2003). For example, the use of activity scheduling and graded exposure may be particularly useful to help the individual overcome cycles of avoidance and withdrawal, e.g. encouraging individuals to take (meaningful, values-congruent) risks in social interactions and cultivate social connections (see Vignette 2). The SM environment may be a particularly helpful starting place to initiate this work as its publicness and availableness means that small steps towards social engagement can be easily implemented. In addition, the relative asynchronicity of the online world (or much of it) allows social interactions to be slowed down, reflected upon, and brought to therapy sessions to discuss in a way that is not possible in real-time, face-to-face social interactions. Also, the young person may have interests, skills and resources that are specific to (or enhanced by) the online world, and which can be drawn upon in the service of their values.

With respect to specific platform affordances, where these are deemed to be interacting problematically with presenting issues, alternatives may be explored. As an example, for someone with low self-esteem that is triggered or perpetuated by judgemental social comparisons an alternative SM platform that is less public, visual, permanent, asynchronous and quantifiable, might be explored as a way of maintaining social connections, whilst minimising the potential risks of such interactions (see Vignettes 3 and 4 for examples).

With respect to consequences and reinforcement loops included in the conceptualisation, these may be helpfully explored through a functional analysis of the individual’s SM use. In this regard, drawing out the long-term consequences of usage patterns may highlight activity that is reinforced by ‘short-term gain’, at the cost of ‘long-term pain’ (Rizvi, Reference Rizvi2019). Furthermore, understanding what is driving/maintaining problematic engagement with SM (including motivations and reinforcement loops) may inform appropriate interventions. For example, if engagement is driven by a fear of missing out (Przybylski et al., Reference Przybylski, Murayama, Dehaan and Gladwell2013), cutting down on SM use might be helpful as it removes a major trigger, whereas if a need for connection is driving engagement, reducing use is unlikely to be helpful unless alternative means of socialising are devised.

Finally, given the proposed over-arching role of mindful/purposeful (versus mindless/habitual) modes of engagement within the conceptualisation, some introduction to mindfulness skills training might also be indicated. This would enable the individual to become more aware of relevant internal and external signals, including thoughts, feelings, sensations and behavioural intentions related to their SM use, track the consequences of use, and encourage more intentional/purposeful engagement.