Introduction

Social media (SM) platforms are defined as web-based communication platforms that allow users to easily connect with others and consume content (Aichner et al., 2021). These platforms are a ubiquitous presence in modern society with over 4.5 billion users worldwide, and projections suggest this will continue to increase rapidly (Statista, 2023a). Young adults are the most frequent users of these sites as 88% of 18–29-year-olds report social media use (SMU) in some capacity each day (Statista, 2023b, c). Users are also spending an increasing number of hours on these sites, with individuals reporting an average of three hours per day (Statista, 2022a). The growth and increase in popularity of SM have sparked an interest in research attempting to understand how SMU influences psychological well-being outcomes (Andreassen, 2015; Petropoulos Petalas et al., 2021). Well-being consists of components such as self-esteem, and those who lack well-being in their lives are at greater risk for depression and loneliness (Wood & Joseph, 2010).

Self-esteem is the most common indicator of well-being and is defined as an individual’s subjective value judgement of the self (Rosenberg, 1965a, b). For many, it is a stable trait; however, it may fluctuate for others (Harter & Whitesell, 2003). Low self-esteem is associated with numerous forms of mental distress, including depression (Nguyen et al., 2019) and addiction (Faelens et al., 2021). Self-esteem may be enhanced or deflated based on cognitive processes that can be influenced by SMU (Krause et al., 2019). The Sociometer Theory (Leary, 2005) suggests self-esteem is calibrated to indicators of inclusion or rejection in the social environment. Receiving “likes” and followers may enhance self-esteem through feelings of inclusion and approval; however, these same metrics may reduce self-esteem when lacking (Burrow & Rainone, 2017; Limniou et al., 2022). Additionally, more frequent SMU has been linked to lower self-esteem through social comparisons and ostracization (Bergagna & Tartaglia, 2018; Jan et al., 2017). On SM, social comparisons are ever-present because comparison information (e.g., “likes” and follower counts) is highly noticeable (Jiang & Ngien, 2020). Furthermore, the Need-Threat model (NTM; Williams, 2009) suggests that when users do not receive desired feedback, they may feel ostracised. Social media users may experience cyber-ostracisation in instances where they perceive themselves to not have gained sufficient likes (Dumas et al., 2020; Reich, Schneider & Zwillich, 2023). Thus, this may lead to reduced self-esteem (Lutz & Schneider, 2020) which may be alleviated through deceptive engagement with social media (Dumas et al., 2017).

SMU can also impact social relationships as it provides easy and near-constant access to other people (Subramanian, 2017). Although the heightened interpersonal connectivity provided by SM may be assumed to relate to overall increases in social well-being, loneliness remains a persistent problem in societies where SMU is high (e.g., the UK) (O’Day et al., 2021; Pittman & Reich, 2016). Loneliness is a common human experience of perceived isolation and is made up of social and emotional components (Cacioppo et al., 2014). It is important to understand what variables influence loneliness due to its impact on mental and physical well-being. For example, as suggested by the Social Enhancement hypothesis (Gadekar & Ang, 2020), SM may ameliorate loneliness through the creation of new social connections and the strengthening of existing ones (Ostic et al., 2021). Furthermore, users may also experience feelings of social acceptance and inclusion through receiving “likes” and gaining “followers” (Bradley et al., 2019; Burrow & Rainone, 2017; Moreton & Greenfield, 2022). However, these findings do not explain why reports of loneliness continue to rise. Some research posits that SM creates an isolating experience as others appear happier, healthier, and more successful than themselves (Jiang & Ngien, 2020; O’Day et al., 2021). Online relationships may also enhance loneliness due to the reduced intimacy of online (vs offline) relationships (Matook et al., 2015; Taylor & Lay, 2024). Additionally, spending excessive time on SM may take away from face-to-face interactions which results in feelings of loneliness (see Displacement Theory; Gruner, 2016; Caba Machado et al., 2023). The relationship between SMU and loneliness remains ambiguous, and a better understanding of this relationship may help reduce occurrences of this distressing experience.

Depression is another form of psychological distress experienced abundantly and is ranked as the third leading cause of disability-adjusted life years in Western Europe (World Health Organisation, 2023). Although many factors contribute to depression, there is a growing interest in SMU as an instigating factor. Studies investigating this relationship have yielded mixed results (Adeyanju et al., 2021; Hartanto et al., 2021). Some evidence suggests SMU can help alleviate negative mood through active (vs passive) use (Valkenburg et al., 2022; Verduyn et al., 2017), offers of social support (Neubaum & Kramer, 2015; Meshi & Ellithorpe, 2021), and interruptions to ruminating thought patterns (Wolfers & Schneider, 2021). However, more frequently, SMU is associated with increased depressive symptoms (Cunningham et al., 2021; Lopes et al., 2022). SMU may become depressing when users engage in social comparison as envy is frequently reported as a predictor of depression (Appel et al., 2016; Carraturo et al., 2023). As with loneliness, SMU may relate to depression through the displacement of activities that bring meaning and joy (e.g., exercise and face-to-face interactions; Brailovskaia & Margraf, 2020). Again, the relationship between SMU and depression is ambiguous, and developing a better understanding of this relationship may have positive impacts on the occurrences of depression in society.

It has been further suggested that SM may create a unique social pressure always to be available (Carbonell & Panova, 2016). Indeed, some users have reported experiencing distress and anxiety when they are not able to access SM and even neglecting important needs (e.g., eating, drinking, and physical activity) in favour of spending time online (Valkenburg, 2022). This obsessive engagement has been described in the literature as Problematic Social Media Use (PSMU) which is defined by the following three core factors: (1) an inability to control use, (2) functional impairment, and (3) continued use of SM regardless of negative impact (Chamberlain et al., 2016). Research has linked PSMU to negative mental well-being (Banjanin et al., 2015) possibly through increased social comparison opportunities, and feelings of inferiority if “friends” online appear to have more fulfilling lives than one’s own (Verduyn et al., 2020), and increased loneliness (Marttila et al., 2021). Although SMU may initially gratify the needs of users (e.g., through entertainment) as the Uses and Gratifications Theory (UGT) suggests, this reliance on SM for gratification may eventually become problematic (Falgoust et al., 2022; Ferris, Hollenbaugh & Sommer, 2021).

Reliance on SM is further exacerbated by the emotional value placed on these sites (Lowe-Calverley et al., 2019; Martinez-Pecino & Garcia-Gavilan, 2019). Emotional investment (EI), in this study, describes the importance attributed to SM metrics such as “likes” and “followers”. These metrics can indicate social acceptance, and some users engage deceptively to attain desired approval (Dumas et al., 2017). EI has also been linked to reduced well-being through perceived social isolation and rejection (Chua & Chang, 2016; Stokes & Price, 2017). Examining EI provides an additional layer of detail about users’ experiences, allowing for a deeper understanding of how SMU impacts well-being.

Much of the current literature has focused on (problematic) Facebook use as a predictor of well-being (e.g., Astatke et al., 2022). Few studies have given attention to other platforms, despite evidence that behavioural patterns and well-being outcomes differ between applications (Limniou et al., 2022; Smith & Short, 2022). Hou and Shiau (2019) suggested users were moving away from Facebook to platforms with a high degree of visual content. Instagram and TikTok are both visually orientated platforms (Hellemans et al., 2021; Laor, 2022), and users share similar motives for use across both (e.g., social interaction, self-presentation, escapism) (Lee et al., 2015; Omar & Dequan, 2020).

More specifically, Instagram is a photo-sharing platform allowing users to share their lives through photos and videos (Lowe-Calverley et al., 2019). This platform has over 2 billion monthly active users (Ruby, 2022), and research suggests users have an overwhelming preference for Instagram compared to Facebook (Shane-Simpson et al., 2018). Instagram’s popularity is largely based on users’ immediate and direct access to friends and other followers (Hou & Shiau, 2019). The images on this platform facilitate physical presence and have been suggested to reduce loneliness and contribute to positive mental well-being (Meier et al., 2020; Pittman & Reich, 2016). In line with the Sociometer Theory (Leary, 2005), the posting of curated images on Instagram may enhance well-being if received positively by peers through “likes” and gaining “followers” (Stokes & Prices, 2017). However, in attempting to post content that appeals to others, the risk of problematic engagement and decreased well-being arises (Diefenbach & Anders, 2021; Dumas et al., 2017). Users may engage in exaggerated self-presentation (e.g., through filters and editing) which is associated with lower self-esteem (Ozimek et al., 2023). Additionally, evidence suggests viewing large quantities of “followers” curated photos can lead to negative emotions due to social comparison (Meier et al., 2020).

TikTok, on the other hand, is a video-sharing platform that allows users to create short-form videos that can be shared with anyone through the “For You Page” (FYP) and it was the most downloaded mobile application worldwide in 2022 (Statista, 2022b). The popularity of TikTok is driven by the highly personalised and never-ending content available on the FYP (Zhao & Wagner, 2022). The interface of TikTok has created a unique user experience that drives retention rates much higher than those on any other platform (Zhao & Wagner, 2022). These high retention rates raise concerns as to what this means for user well-being, especially as many TikTok users report losing track of time and spending longer than intended online (Roberts & David, 2023). These experiences are consistent with the concept of “flow” in which users are so engrossed in scrolling that little else seems to matter to them (Brailovskaia et al., 2021). This immersion is related to PSMU (Roberts & David, 2023), and there is concern that SM users who experience flow states more frequently will neglect offline activities upon which well-being depends (Brailovskaia et al., 2021). As TikTok was recently launched, little research exists concerning the influence of TikTok on well-being (Montag et al., 2021). Some evidence implicates TikTok use with negative well-being (Dong & Xie, 2022; Sha & Dong, 2021), although results are conflicting (Masciantonio et al., 2021). As research regarding this platform is limited, further investigation into the relationship between TikTok use and mental well-being is needed.

The current study aims to provide further evidence regarding the relationship between (problematic) Instagram and TikTok use and mental well-being, specifically self-esteem, depression, and loneliness, also considering the role of EI. Based on statistical evidence, TikTok is more popular among young users (18–24-year-olds) compared to Instagram (Jian, 2023). This may have an impact on people’s well-being as TikTok users mainly use this platform for “enjoyment” and “time distortion” purposes (Roberts & David, 2023). The authors are not aware of any other research that examines the impact of TikTok and Instagram on young users’ well-being, and so, this study will investigate the following hypotheses:

  • H1. Greater TikTok Use (time spent per day) will be associated with greater problematic engagement, increased depressive symptoms, increased loneliness, and reduced self-esteem.

  • H2. Greater Instagram Use (time spent per day) will be associated with greater problematic engagement, increased depressive symptoms, increased loneliness, and reduced self-esteem.

  • H3. Greater PSMU scores will be associated with increased depressive symptoms, increased loneliness, and reduced self-esteem.

  • H4. Greater EI (importance of “likes” and importance of “followers”) on TikTok will be associated with greater problematic engagement, increased depressive symptoms, increased loneliness, and reduced self-esteem.

  • H5. Greater EI (importance of likes and importance of followers) on Instagram will be associated with greater problematic engagement, increased depressive symptoms, increased loneliness, and reduced self-esteem.

Method

Participants and Experimental Conditions

The recruitment process, based on opportunity sampling, started after gaining ethical approval from the University’s Ethics Committee. Study advertisements were posted on various social media platforms (i.e., Instagram, Facebook, and Twitter) and on an internal recruitment platform on which first-year students volunteer themselves as participants in undergraduate and postgraduate research. Participants were initially directed to the hosting website Qualtrics (www.qualtrics.com) where participants could find the information sheet and the consent form. The participants were informed of the aim of the study and the data storage process. Participation was anonymous, and participants could withdraw at any time. All participants met the inclusion criteria (i.e., being 18 years of age or above, having no prior diagnoses of depression, and having a social media account on both platforms of Instagram and TikTok). After providing consent, participants could continue to the online survey. At the end of the study, participants were given a debrief sheet that contained contact information for any subsequent questions, and links to mental well-being web pages for further support if needed.

Overall, 252 participants fully completed the study (mean age = 19.93, SD = 4.70). Based on the power calculation, the margin error of the 252 participants who completed the questionnaire was ± 6.14% for a total population of 20,000 social media users with a 95% confidence level. As there was a requirement for participants to have an account on both Instagram and TikTok, at the beginning of the questionnaire, they were asked on which of the two social media sites they spent more than 75% of their time. A total of 80.9% of participants mostly used TikTok rather than Instagram, and most participants were females (86.1%).

Questionnaire

An online questionnaire, including 69 questions and expected to take 20 min to complete, was used for this study. The initial part of the questionnaire included 12 items collecting demographic information (gender and age), social media usage (time spent on each platform, numbers of “likes” and “followers”, and the importance of these). The next part of the questionnaire included validated scales to measure participants’ self-esteem, depression, loneliness, and problematic social media use.

Specifically, the Rosenberg Self-Esteem Scale (Rosenberg, 1965a, b) includes 10 items and is the most widely used measure of self-esteem for adult populations. Items are ranked on a 4-point Likert scale, with values ranging from 1 (strongly agree) to 4 (strongly disagree). Five items contain positively worded questions about the self (e.g., I feel I have a number of good qualities). The remaining five items contain negatively worded questions about the self (e.g., All in all, I am inclined to think I am a failure). Total scores can range between 10 and 40, whereby higher scores are indicative of lower self-esteem. The Cronbach’s alpha of self-esteem for this study was 0.88.

The 20-item University of California, Los Angeles (UCLA) Loneliness Scale was used to measure subjective feelings of loneliness (Russell et al., 1978). Items are ranked on a 4-point Likert scale, with values ranging from 1 (never) to 4 (often). There are positively worded items, reflecting satisfaction with social relationships, (e.g., I feel part of a group of friends) as well as negatively worded items, suggesting dissatisfaction with these relationships (e.g., I feel left out). The scores can range between 20 and 80, with higher scores indicating greater subjective feelings of loneliness. The Cronbach’s alpha of loneliness for this study was 0.89.

The next 20 items on the questionnaire measured depressive symptoms experienced over the last week, and the Centre for Epidemiological Studies Depression Scale (CES-D; Radloff, 1977) was used. Items are ranked on a 4-point Likert scale, with values ranging from 1 (rarely or none of the time (less than once a day)) to 4 (most or all of the time (5–7 days)). This scale contains items related to depression (e.g., restlessness and poor appetite). There are four positively worded items (e.g., I enjoyed life) and 16 negatively worded items (e.g., I had crying spells). The scores can range between 20 and 80, with higher scores being indicative of greater depressive symptoms being experienced over the last week. The Cronbach’s alpha of depression for this study was 0.92.

The final part of the online questionnaire included the Bergen Social Media Addiction Scale (BSMAS; Andreassen et al., 2017) which measured problematic social media use. This scale assessed social media use over the past 12 months. This 6-item questionnaire is anchored in general addiction theory according to six basic symptoms of addiction (salience, conflict, mood modification, withdrawal, tolerance, and relapse) (Griffiths, 2005). All items are rated on a 5-point Likert scale, with values ranging from 1 (very rarely) to 5 (very often). Total scores can range between 6 and 30, with higher scores indicating greater addiction symptoms. The Cronbach’s alpha of problematic use for this study was 0.79.

Results

An overview of the participant’s well-being and PSMU is given in Table 1. Information regarding time spent on each platform is provided in Table 2.

Table 1 Descriptives of participants’ well-being and problematic social media use
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Table 2 Descriptives of participants’ time spent in minutesa on each platform
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Hypothesis 1

To investigate the association between time spent on TikTok, PSMU, and well-being, four simple regression analyses were conducted.

There was a significant association between time spent on TikTok and problematic use (adjusted R2 = 0.08, F(1, 250) = 24.12, β = 0.30, p < 0.001), with those who spent longer on TikTok each day scoring higher on the problematic use scale. There was a significant association between time spent on TikTok and depression (adjusted R2 = 0.04, F(1, 250) = 10.71, β = 0.20, p = 0.001), with longer time spent on TikTok predicting greater scores on the relevant CES-Depression scale. There was also a significant relationship between time spent on TikTok and self-esteem scores (adjusted R2 = 0.02, F(1, 250) = 6.46, β = 0.16, p = 0.012), with greater time spent on TikTok predicting higher self-esteem scores which indicate experiences of lower self-esteem.

There was no significant relationship between time spent on TikTok and loneliness scores (R2 =  − 0.003, F(1, 250) = 0.36, p = 0.551).

Hypothesis 2

This hypothesis investigated the relationship between time spent on Instagram, problematic use (PSMU), and well-being. Four simple regression analyses were conducted.

Time spent on Instagram was significantly associated with PSMU (adjusted R2 = 0.03, F(1, 250) = 7.49, β = 0.17, p = 0.007), with greater time spent on Instagram each day predicting greater scores on the problematic use scale.

There was no significant association between time spent on Instagram and any well-being outcomes, including depression (R2 =  − 0.004, F(1, 250) = 0.10, p = 0.753), loneliness (adjusted R2 =  − 0.003, F(1, 250) = 0.34, p = 0.563), and self-esteem (adjusted R2 = 0.007, F(1,250) = 2.84, p = 0.093).

Hypothesis 3

To investigate the association between problematic use (PSMU) and well-being outcomes, three linear regression analyses were conducted.

PSMU was significantly associated with both depression (adjusted R2 = 0.08, F(1,250) = 22.55, β = 0.29, p < 0.001) and self-esteem (adjusted R2 = 0.06, F(1, 250) = 16.05, β = 0.25, p < 0.001). Greater PSMU related to higher scores on scales measuring depression (i.e., more depressive symptoms experienced) and self-esteem (i.e., lower self-esteem). PSMU had no association with loneliness (R2 =  − 0.001, F(1, 250) = 0.66, p = 0.417).

Hypothesis 4

Emotional investment (EI) and its association with problematic use (PSMU) and well-being were investigated. Table 3 provides an overview of participants’ responses regarding EI across both platforms.

Table 3 Participants’ response per social media platform on questions regarding the importance of “likes” and “followers” (4-point Likert scale; 1, extremely important to 4, not important at all)
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To investigate the association between EI on TikTok and problematic use (PSMU) and well-being, four multiple regressions were conducted.

The regression model regarding PSMU was significant and predicted approximately 5% of the variance; adjusted R2 = 0.05, F(2, 201) = 6.51, p = 0.002. There was a significant negative association between the importance of “likes” and PSMU (β =  − 0.27, p < 0.001), with those regarding likes as more important scoring higher on the problematic use scale. However, no significant association was found between the importance of followers on TikTok and PSMU scores (p = 0.122).

The overall regression model regarding depression was not significant (adjusted R2 = 0.01, F(2, 201) = 2.16, p = 0.118). However, there was a significant negative relationship between the importance of “likes” and depression (β =  − 0.15, p = 0.045), with those regarding “likes” as more important experiencing more depressive symptoms. There was no significant relationship between the importance of “followers” and depression (p = 0.205).

The regression model investigating self-esteem was not significant (adjusted R2 = 0.003, F(2, 201) = 1.26, p = 0.286. Neither the importance of “likes” (p = 0.158) nor the importance of “followers” (p = 0.930) were significant predictors of self-esteem.

Lastly, the regression model investigating loneliness was also non-significant (adjusted R2 = 0.01, F(2, 201) = 2.26, p = 0.106. Neither the importance of likes (p = 0.056) nor the importance of “followers” on TikTok (p = 0.936) were significant predictors of loneliness.

Hypothesis 5

Finally, the association between EI on Instagram and problematic use (PSMU) and well-being was investigated using four multiple regressions.

The regression model regarding PSMU was significant and predicted 8.6% of the variance; adjusted R2 = 0.09, F(2, 45) = 3.21, p = 0.050. There was a significant negative association between the importance of “likes” and PSMU (β =  − 0.40, p = 0.028), with those regarding “likes” as more important scoring higher on the problematic use scale. However, no significant association was found between the importance of “followers” on TikTok and PSMU scores (β = 0.09, p = 0.616).

The regression model investigating depression was not significant (adjusted R2 =  − 0.02, F(2, 45) = 0.62, p = 0.542). Neither the importance of “likes” (p = 0.893) nor the importance of “followers” on Instagram (p = 0.435) predicted depression scores.

The regression model investigating self-esteem was also non-significant (adjusted R2 = 0.03, F(2, 45) = 1.80, p = 0.178). Neither the importance of “likes” (p = 0.367) nor the importance of “followers” on Instagram (p = 0.460) predicted depression scores.

Finally, the regression model investigating loneliness was also non-significant adjusted (R2 =  − 0.03, F(2, 45) = 0.38, p = 0.686). There was no significant association between the importance of “likes” (p = 0.545) nor the importance of “followers” on Instagram (p = 0.391) and loneliness scores.

Discussion

Social Media (SM) platforms are a ubiquitous presence in modern society, with users spending longer online each year (Statista, 2023a). The popularity of these platforms has encouraged research into the association between Social Media Use (SMU) and well-being. Some evidence suggests that SMU encourages well-being through increased social opportunities (Burrow & Rainone, 2017) and the management of negative emotions (Wolfers & Schneider, 2021). However, many studies provide evidence for the negative influence of SMU on well-being (e.g., Jan et al., 2017; Marttila et al., 2021), especially in those who engage problematically (Lopes et al., 2022). The current study set out to investigate the influence of Instagram and TikTok use on well-being, also considering the influence of PSMU and EI. It was expected that a greater understanding of the relationship between SMU and well-being would be established, particularly as few studies have investigated Instagram and TikTok use.

The first hypothesis suggested greater time spent on TikTok per day would predict greater scores of PSMU, depression, loneliness, and self-esteem. This hypothesis was partially supported as time spent on TikTok did positively predict scores of PSMU, depression, and self-esteem, however, did not predict a significant association with loneliness. The significant results are consistent with existing literature suggesting a relationship between SMU and reduced well-being (Cunningham et al., 2021) and further provide support for studies demonstrating TikTok use, specifically, to impact well-being (Sha & Dong, 2021). These findings may be explained through experiences of “time distortion” (losing track of time) and “telepresence” (deep immersion) that previous studies have shown are common in TikTok users (Roberts & David, 2023). Brailovskaia and colleagues (2020) concluded these experiences to be strongly related to PSMU as users find “flow states” rewarding and feel anxious when they are unable to immerse themselves within these platforms. Furthermore, in line with the Displacement Theory (Gruner, 2016), spending longer time on TikTok reduces the time available to complete important tasks (e.g., academic work) (Lau, 2017) which in turn predicts depression (DeRoma et al., 2009) and self-esteem (Arshad et al., 2015). These are important findings concerning the current population of predominately university students.

Unexpectedly, time spent on TikTok was not associated with scores of loneliness. This contrasts existing literature about loneliness as presented in the “Introduction” Section. Additionally, these results are surprising given that UK university students often report experiences of loneliness (Vasileiou et al., 2019), and so, it is unexpected to see no relationship between SMU and loneliness in the current sample. The lack of significance may lie in the unidimensional measure of loneliness used in this study. Past studies that have shown significant associations have examined loneliness in its two components—social and emotional, and shown these to differ in their relationship with SMU (Bonsaksen et al., 2021). Perhaps, it is that social loneliness is reduced through SMU due to access to a huge audience, but emotional loneliness is enhanced through a lack of intimate connection with this audience. As the current study only investigated unidimensional loneliness scores, a more nuanced understanding of the relationship between SMU and loneliness could not be provided.

Secondly, this study investigated the association between time spent on Instagram and levels of PSMU, depression, loneliness, and self-esteem. Time spent on Instagram was only significantly associated with PSMU. Some past research has suggested there to be no relationship between time spent on SM platforms and well-being (Banjanin et al., 2015; Stapleton et al., 2017). These findings, together with the present study, suggest there exist factors in addition to duration that influence the relationship between Instagram use and well-being. For example, passive browsing vs active commenting has been related to reduced well-being (Frison & Eggermont, 2017). As this study did not investigate how people use SMU, this may explain the lack of significant findings here.

Across both platforms, time spent was associated with PSMU. This is in line with previous research (Lopes et al., 2022). Following UGT suggestions (Katz et al., 1973), users learn that SMU can easily and conveniently provide gratification for their needs (e.g., through entertainment), and so, users rely increasingly on SM to satisfy these (Kircaburun et al., 2020). However, SMU may not satisfy needs wholly (e.g., online social interactions are not as fulfilling as those that occur offline), and ungratified needs accumulate over time and drive subsequent SMU ( Hussain & Shabir, 2020).

Additionally, PSMU was significantly associated with depression and self-esteem, but not loneliness. Addictive behaviours may disrupt other activities that can be protective against negative well-being (e.g., exercise and/or sleep) (Brailovskaia & Margraf, 2020). In university students, these healthy behaviours are often already disrupted (e.g., Jiang et al., 2015), and so, PSMU creates an added layer of risk in this population. Negative patterns of use (e.g., obsessive self-comparison) that are linked to reduced self-esteem and increased depressive symptoms are exacerbated in those exhibiting PSMU (Banjanin et al., 2015). These results demonstrate that PSMU is independently associated with depression and self-esteem, regardless of time, suggesting it may be how users are engaging, rather than the duration of engagement, that poses a risk to well-being (Lopes et al., 2022).

Unexpectedly, PSMU was not associated with loneliness. Throughout the literature, there are inconsistencies in findings relating to the relationship between PSMU and loneliness (Limniou et al., 2022; Lutz & Schneider, 2020;). It has been suggested that differences across studies may lie in the way that users are engaging with SM (O’Day & Heimberg, 2021; Verduyn et al., 2017). Active users, engaging in direct messaging, posting, and sharing, often report reduced feelings of loneliness (Verduyn et al., 2017). On the other hand, passive use, monitoring other people’s lives without direct engagement, is associated with increased feelings of loneliness (Moody, 2004; Verduyn et al., 2017). As the current study did not investigate active or passive SMU, this may explain why no association was found.

The final two hypotheses investigated the influence of EI on PSMU and well-being. “likes” and “followers” constitute evidence of social inclusion and acceptance (Burrow & Rainone, 2017; Chua & Chang, 2016), and some users may take extreme measures to ensure positive online feedback (Dumas et al., 2017). Across both platforms, the importance of “likes” significantly predicted PSMU scores, as was expected from Sociometer theory, with individuals’ self-esteem being affected by the sense of social acceptance and the importance placed on their relationships (Zhang et al., 2023). This is in line with past research that demonstrates a relationship between those seeking validation and “addictive” SMU (Limniou et al., 2022). One key factor associated with excessive behaviours is the reward, as previous research has shown the rewarding power of “likes” (Martinez-Pecino & Garcia-Gavilan, 2019), including at a neural level (Sherman et al., 2018). “Likes” can indicate social acceptance and approval and, for those who are more sensitive to measures of these, encourage excessive and problematic engagement as they rely increasingly on SM to meet needs (Kircaburun et al., 2020; Li et al., 2018). Followers on these platforms do not provide rewards in the same manner as “likes”. Indeed, research has shown users engage more in deceptive “like-seeking” (Dumas et al., 2017) as this metric carries more value in online spaces.

The importance of “likes” for those who prefer TikTok was significantly associated with depression. TikTok users often create videos to increase confidence and entertain others (Falgoust et al., 2022). Other studies have shown users feel disheartened when they do not receive the recognition they believe they deserve (Chua & Chang, 2016). In line with the Need-Threat model (NTM), the results of the current study suggest that TikTok users who are more sensitive to peer acceptance/rejection through “likes” may suffer reduced well-being when their need for validation is not met. Insufficient validation may trigger negative thoughts (e.g., viewing oneself as less likeable) which are known risk factors for depression (Ali et al., 2023).

Contrastingly, the importance of “likes” for those who prefer TikTok did not significantly predict self-esteem or loneliness. The importance of TikTok “followers” was also not significantly related to any well-being variables. Although these findings are somewhat contrasting to existing literature regarding other SM platforms (Li et al., 2018), they do align with some preliminary studies on TikTok use. Indeed, some evidence shows that TikTok-use motivations are a significant factor in predicting well-being (Dong & Xie, 2022). As this study did not investigate SMU motives, this may explain the lack of significant findings. Examining the relationship between SMU motives and well-being could provide a deeper understanding of the influence TikTok use has on well-being.

EI on Instagram was not significantly related to any well-being outcomes. Although past studies contrast present findings (Martinez-Pecino & Garcia-Gavilan, 2019), these studies considered additional variables that the current study did not. For example, it has been suggested that the anticipation of posting is more closely related to well-being outcomes compared to the reception of this post (Lowe-Calverley et al., 2019). As the current study only focused on investment in “likes” and “followers”, the failure to examine additional variables may account for the lack of significant findings. An additional point to note here is that Instagram recently introduced a feature in which users can “hide like count” (Instagram, 2021). The reduced visibility of this metric has been shown to obscure significant relationships with well-being (Wallace & Buil, 2021), as in the current study. Thus, as the current study did not control for the visibility of “likes” on Instagram, this may also have impacted the findings.

Whilst the current findings do provide useful evidence of the relationship between Instagram and TikTok use and well-being, there are some limitations. Firstly, this study employed a cross-sectional, correlational design, which hampers the ability to determine directional relationships. Some studies have suggested that those with reduced well-being engage more with SM to cope with these negative feelings (Rasmussen et al., 2020), so, well-being may predict SMU. The directionality proposed in this study requires further research. Future studies would benefit from using longitudinal designs to better understand the direction and stability of these relationships. Secondly, self-report measures may have presented an issue, particularly regarding time spent online each day. Users likely underestimate their time spent online (“time distortion” effects) (Scharkow, 2016). The time frame associated with well-being and PSMU needs to be accurately measured to best advise interventions for those struggling. Future research could use active monitoring methods to track SMUs more accurately. Additionally, users may have felt embarrassed to attribute important values to “likes” and “followers” and so downplayed their true feelings for these questions. This may have influenced the significance of the present results. This study also assumed that the importance of “likes” would be consistent across both platforms; however, future research would benefit from investigating value attributed across the two platforms. Finally, the current sample is largely representative of the majority user base on both platforms (18–29 years old) with more females participating in this survey, while the gender ratio in both platforms is approximately equally split (Statista, 2023b, c). However, these findings should not be overgeneralised to other populations. Across most SM platforms, the minimum age for initial sign-up is 13; however, this study only considered those over 18. Thus, these findings may not be relevant to younger adolescents. Considering the importance of emotional development during adolescence, future research should investigate the age and gender demographic further.

From the current findings, several implications may be considered. Firstly, users should spend less time on social media platforms. This could entail using screen-time management settings or an accountability partner promoting healthier SMUs. The prior may improve self-regulation which helps an individual to practice restraint the next time they use SM. These practices may help mitigate the negative impact of time distortion and telepresence through disruption of flow states, thus reducing the risk of PSMU and reduced well-being. Additionally, the influence of PSMU on well-being is of interest to healthcare professionals. For example, it may be valuable to track SMUs in clinical populations to examine excessive patterns of use which may be contributing to mood dysregulation.

To conclude, this study has discussed how individuals’ well-being may be impacted by PSMU, EI, and time spent on SM each day. Participants engaged with TikTok more and showed a preference for this platform over Instagram. Increased use and EI across both platforms predicted PSMU, which in turn was associated with depression and self-esteem, but not loneliness. The findings of this study reinforce the complexity of the relationship between SMU and well-being and highlight the need for continued investigation in this area, particularly as SMU continues to impact daily life.