Introduction

Social Media (SM) is a group of Internet-based applications that build on the ideological and technological foundations of web 2.0. (i.e., the way in which end-users started to utilize the Internet platform, whereby content and applications are no longer created and published by individuals but instead continuously modified by all users in a participatory and collaborative way) and that allow the creation and exchange of user-generated content [1,2,3]. SM platforms have exponentially grown in their use over the last decade, particularly among young people [4, 5] and the so-called ‘digital natives’ have grown up immersed in the digital technology, with an habitual SM use of about 90% [6, 7].

Several studies outlined the positive role of SM in fostering individual’s wellbeing, by enriching everyday-user experiences, facilitating social cohesion, increasing feelings of connectedness and closeness with friends, as well as enhancing selective self-disclosure and social support [8,9,10,11]. An enhanced friendship quality and perceived increased social support, due to the social networking use, were also identified as protective factors against depressive symptomatology [12, 13]. SM use is becoming increasingly common also in mental health care, by facilitating networking with peers/colleagues/patients, mentorship, education and research, particularly among youths [14]. However, despite these promising beneficial advantages, other studies raised concerns regarding the potential addictive use of social networking platforms, by underlining the negative association between a problematic social media use (PSMU) and psychological consequences [15,16,17,18,19]. PSMU is characterized by an excessive individual’s concern about online social networking activities, being driven by a strong motivation to log on or use SM, and devoting so much time and effort to use these online platforms. This may lead the subject to totally or partially impair other social activities, including the school/university/job, interpersonal relationships and overall wellbeing [15, 16]. The global pooled prevalence of PSMU was estimated to be 17.4% (95%CI 12.4–23.9), considering the increase occurred due to the COVID-19 [20]. PSMU, including the compulsive involvement in online social networking activities, has been also negatively associated with worst individual's physical and mental health, poor wellbeing, relationships, reduced self-esteem levels, poor sleep quality, increased loneliness and depressive levels [17, 21,22,23,24,25,26], particularly among adolescents and young adults [27,28,29]. Systematic reviews reporting a suggestive detrimental effect of wireless devices and SM use on youth’ mental health also recommended caution in interpreting these findings, because of the lack of high-quality longitudinal studies [30,31,32]. Recent literature also reported that the effect on youth mental health mainly depends on the time and type of SM used as well as on individual vulnerability [31,32,33].

The rapid outbreak of the coronavirus pandemic (COVID-19) had a significant impact on people’s everyday life, as restrictive measures were imposed to reduce physical contact to limit the viral transmission [34,35,36,37]. These changes in lifestyle were associated to an increase of the frequency and intensity of individual’s engagement in online activities, including social networking use [20, 38]. Such an increase has been associated to managing the reduced sense of control, to esca** the anxiety and uncertainty feelings and to being constantly informed about updates and news and maintain peer-to-peer social contact [39, 40].

According to our previous results from the COMET study [41, 42], COVID-19-related social isolation predispose to increased problematic internet activities (i.e., videogaming and internet addiction) in the general population, while youngsters reported a higher increase in SM activities compared to adults [43]. The COVID-19-related ‘stay-at-home’ policy may have disrupted certain social needs (i.e., need for relatedness) and incentivized the SM use, to gain social compensation through virtual interactions [44, 45]. Other studies documented that SM use and relatedness need satisfaction improve mental health issues, particularly depressive symptoms and loneliness [46,59] was run to carry out mediation analyses (Model 4) to test whether the direct or indirect effect of BSNAS (as independent variable) on DASS-21 depression subscale (as dependent variable), were mediated by AQ total score and/or subscales and BIS-11 total score and/or subscales. Indicators of indirect effects were tested using a bias-corrected bootstrap** (n = 5000) with 95% CI, by setting a statistical significance when the 95% CI does not contain zero. For all analyses, the level of statistical significance was set at p < 0.05, two-tailed. All analyses were performed using the software Statistical Package for Social Science (SPSS) version 27.0 (IBM SPSS Statistics, Chicago, IL, United States).

Results

Socio-demographic and psychopathological characteristics

The final sample consists of 491 individuals, with a slight prevalence of females (64.4%, N = 316) and single (58%, N = 285). The mean age was 21.8 (SD = 1.7) years, without any statistical significant gender-based differences. The most frequent educational levels were university degree (53%; N = 260) and high school diploma (46%; N = 226). Most respondents reside in southern Italy (53.4%; N = 262), followed by central (25.9%; N = 127) and northern Italy (20.8%; N = 102). The employment profile was mainly constituted by students (45.2%, N = 222) and full-time employed (36.3%, N = 178). 3.7% (N = 18) of respondents declared to have lost their job during the current pandemic. Overall, 50.1% of total respondents declared to be satisfied (26.1%, N = 128) or quite satisfied (24%, N = 118) regarding their own current financial situation. Only 11.4% (N = 56) of the sample declared a pre-existing physical illness before the COVID-19 pandemic, while 5.3% (N = 26) were previously affected by a mental disorder. Regarding the COVID-19-related variables, only 4 participants (0.8%) declared a previous COVID-19 infection, 1.8% (N = 9) declared to have experienced a home-based isolation due to the COVID-19, and only one subject had been hospitalized due to the COVID-19 infection. About 4.9% (N = 24) reported to have been isolated due to the contact with a subject infected by COVID-19 (Table 1).

Table 1 Socio-demographic characteristics of the sample
Full size table

The mean total score of DASS-21 was 60.1 (SD = 12.8), DASS-21 depression subscale was 20.1 (SD = 5.4), DASS-21 anxiety subscale was 23.1 (SD = 4.7), and DASS-21 stress subscale was 16.9 (SD = 5.3) (Table 2). The mean total score of BSNAS was 20.4 (SD = 6.8) with about 75.8% of the sample who were classified as having a PSMU (BSNAS ≥ 16). Interestingly, significantly higher DASS-21 total scores, depression, anxiety and stress levels were found in those participants without clinically significant BSNAS scores (BSNAS− group) compared to BSNAS+ group (all with p < 0.001) (Table 2).

Table 2 Clinical characteristics of the sample
Full size table

Predictors of the problematic social networking use

According to the multivariate regression model, social networking use levels were reduced by the presence of a COVID-19 related hospitalization (Beta coefficient, B = − 12.135; 95% confidence interval, CI = (− 24.038)–(− 0.233)], high levels of AQ total scores [B = − 0.123, 95%CI = (− 0.163)–(− 0.083)] and high levels of general psychiatric symptomatology (as measured by DASS-21 total score) [B = − 0.128, 95%CI = (− 0.175)–(− 0.080)]. These variables statistically significantly predicted social networking use levels (F (3, 487) = 42.338, p < 0.001, R2 = 0.207).

Mediation analyses showed that social networking use (as measured by BSNAS) negatively predicted depressive symptomatology and that their interaction is mediated by AQ total score (ß = − 0.1075, 95%CI [(− 0.1449)–(− 0.0740)]) (Fig. 1A); AQ physical aggression subscale (ß = − 0.207, 95%CI [(− 0.378)–(− 0.0069)]) (Fig. 1B); AQ anger subscale (ß = − 0.0582, 95%CI [(− 0.0863–0.0351]) (Fig. 1C); BIS-11 total score (ß = − 0.0272, 95%CI [(− 0.0482)–(− 0.0100)]) (Fig. 2A), and BIS-11 attentional subscale (ß = − 0.0302, 95%CI [(− 0.0520)–(− 0.0130)]) (Fig. 2B).

Fig. 1
figure 1

Aggressiveness in the mediation analyses between the problematic social networking use and depressive symptomatology

Full size image
Fig. 2
figure 2

Impulsiveness in the mediation analyses between the problematic social networking use and depressive symptomatology

Full size image

Legend Fig. 1 Mediation analyses showed that the problematic social networking use negatively predicted depressive symptomatology and that their interaction was mediated by aggressiveness total score (Fig. 1A), physical aggression score (Fig. 1B) and anger/aggressiveness subscale (Fig. 1C).

Legend Fig. 2 Mediation analysis showed that the problematic social networking use negatively predicted depressive symptomatology and that their interaction was mediated by impulsiveness total score (Fig. 2A), attentional impulsiveness (Fig. 2B).

Predictors of the COVID-19-related psychopathology

According to the multivariate regression model, high COVID-19-related depressive levels were predicted by high levels of AQ total scores (B = 0.218, 95%CI = 0.165–0.272, p < 0.001). On the contrary, depressive levels were reduced by the presence of a mental disorder [B = − 3.692, 95%CI = − 5535)–(− 0.189), p < 0.001], high levels of BSNAS [B = − 0.119, 95%CI = (-0.186)–(− 0.052), p < 0.001], high levels of verbal aggressiveness [B = − 0.338, 95%CI = (− 0.491)–(− 0.185), p < 0.001], high levels of physical aggressiveness [B = − 0.207, 95%CI = (− 0.324)–(− 0.090), p < 0.001] and high levels of attentional impulsiveness [B = − 0.153, 95%CI = (− 0.278)–(− 0.029), p = 0.016], which significantly predicted depressive levels F(6, 484) = 30.322, p < 0.001, R2 = 0.273 (Table 3).

Table 3 Multinomial linear regression model (outcome = depression DASS-21 subscale)
Full size table

High anxiety levels were predicted by high levels of AQ total scores (B = 0.146, 95%CI = 0.097–0.195, p < 0.001). Anxiety levels were reduced by the presence of a mental disorder [B = − 2.153, 95%CI = (− 3.837)–(− 0.470), p = 0.012], high levels of BSNAS [B = − 0.103, 95%CI = (− 0.164)–(− 0.042), p = 0.001], high levels of verbal aggressiveness [B = − 0.213, 95%CI = (− 0.353)–(− 0.074), p = 0.003], high levels of physical aggressiveness [B = − 0.116, 95%CI = (− 0.223)–(− 0.009),− 0.253)–(− 0.025), p = 0.017], which significantly predicted depressive levels, F (6, 484) = 19.944, p < 0.001, R2 = 0.198.

High stress levels were predicted by high levels of AQ total scores (B = 0.192, 95%CI = 0.138–0.246, p < 0.001). Stress levels were reduced by high levels of BSNAS [B = − 0.126, 95% CI = (− 0.194)–(− 0.059), p < 0.001], high levels of verbal aggressiveness [B = − 0.250, 95%CI = (− 0.403)–(− 0.097), p = 0.001], high levels of physical aggressiveness (B = − 0.216, 95%CI = (− 0.333)–(− 0.098), p < 0.001] and high levels of attentional impulsiveness [B = − 0.151, 95%CI = (− 0.276)–(− 0.026), p = 0.018], which significantly predicted depressive levels, F (5, 485) = 28.062, p < 0.001, R2 = 0.224.

Furthermore, general psychopathology was predicted by high levels of AQ total scores (B = 0.556, 95%CI = 0.434–0.678, p < 0.001) and reduced by the presence of a mental disorder [B = − 5.962, 95%CI = (− 10.169)–(− 1.755), p = 0.006], high levels of BSMAS [B = − 0.348, 95%CI = (− 0.501)–(− 0.195), p < 0.001], high levels of verbal aggressiveness [B = − 0.801, 95%CI = (− 1.150)–(− 0.452), p < 0.001], high levels of physical aggressiveness [B = − 0.539, 95%CI = (− 0.806)–(− 0.271), p < 0.001] and high levels of attentional impulsiveness [B = − 0.443, 95%CI = (− 0.727)–(− 0.159), p = 0.002]. These variables statistically significantly predicted depressive levels, F (6, 484) = 39.201, p < 0.001, R2 = 0.327.

Mediation analyses showed that COVID-19 related depressive symptomatology significantly predicted social networking use (as measured by BSNAS) and that their interaction is negatively mediated by AQ total score (ß = − 0.1640, 95%CI [(− 0.2237)–(− 0.1130)]) (Fig. 3A), AQ verbal aggression subscale (ß = 0.0436, 95%CI [(0.0760)–(0.0178)]) (Fig. 3B), AQ anger subscale (ß = -0.0807, 95%CI [(− 0.1237–0.0431]) (Fig. 3C), BIS-11 total score (ß = − 0.0448, 95%CI [(− 0.0771)–(− 0.0193)]) (Fig. 4A), and BIS-11 attentional subscale (ß = − 0.0409, 95%CI [(− 0.0721)–(− 0.0153)]) (Fig. 4B).

Fig. 3
figure 3

Aggressiveness in the mediation analyses between the depressive symptomatology and problematic social networking use

Full size image
Fig. 4
figure 4

Impulsiveness in the mediation analyses between the depressive symptomatology and problematic social networking use

Full size image

Legend Fig. 3 Mediation analyses showed that COVID-19-related depressive symptomatology significantly predicted social networking use and that their interaction was negatively mediated by aggressiveness (Fig. 3A), verbal aggression (Fig. 3B), and anger (Fig. 3C).

Legend Fig. 4 Mediation analyses showed that COVID-19 related depressive symptomatology significantly predicted social networking use and that their interaction was negatively mediated by impulsiveness (Fig. 4A) and attentional impulsiveness (Fig. 4B).

Discussion

Social media use has been supposed to cover a pathoplastic role in the development of psychological impairment and/or psychiatric disorders [30,31,32,33]. However, it is still controversial the impact of SM use on wellbeing, life satisfaction and mental illness. Actually, such a complex interaction might be influenced by both internal and external factors, including social isolation, decreased community engagement, and loneliness [26, 60,61,62,63]. In turn, the impact of SM use on mental wellbeing/illness is influenced by several factors such as use modality (occasional vs. excessive), users’ age and gender, intrinsic and extrinsic motivations in SM use (e.g., to manage impressions, to share emotions, to reduce loneliness feelings, to increase social connectedness) [64, 65].

Our findings show a higher SM use among young people, with 75.8% of them who were classified as having a PSMU (as measured by BSNAS). Interestingly, significantly higher DASS-21 total scores, depression, anxiety and stress levels were found in those participants without clinically significant BSNAS scores; while subjects with PSMU did not manifest a significant psychopathological burden. Our findings point to a ‘protective/resilient’ role of social networking use in mitigating the depressive symptomatology in people aged 18–24, during the early phases of the COVID-19 outbreak. The effect did not change, after controlling for the type of COVID-19 phase (II vs. III), gender, and all COVID-19 disease-related variables. Our findings are in line with previous studies that found a positive effect of SM use in overcoming the negative consequences of COVID-19-related social isolation, by reducing loneliness and increasing social connectedness [66,67,68]. However, other studies reported a detrimental association, by outlining that COVID-19-related restrictive measures and lockdown favored an excessive SM use and increased the risk of develo** a PSMU [39, 69,70,71]. Indeed, a recent metanalysis collecting data coming from 14 cross-sectional studies carried out during the COVID-19 pandemic among young people in different countries, reported that an excessive time spent on social networking platforms was much more likely associated with an increased risk of develo** depressive (OR = 1.43) and anxiety symptoms (OR = 1.55) [72].

Studies carried out before COVID-19 often reported inconclusive and contrasting results about the link between SM use and depression [18, 23, 28, 73,74,75,76,77], probably due the correlational nature of most studies that precludes causal inference. Some authors proposed a bidirectional association, by supporting the hypothesis that PSMU may lead to depression and, similarly, depression may increase a pre-existing PSMU [78]. However, another systematic review found that the time spent by young people on SM was significantly associated with the occurrence of depressive, anxiety and psychological distress symptoms, also in those without a previous mental distress and/or psychiatric disorder [28]. A more recent “dose–response” meta-analysis demonstrated a linear dose–response and gender-specific association between time spent on SM and the risk of depression in young people (OR = 1.6), with a stronger association mainly found in females (OR = 1.7) versus males (OR = 1.20) [81] proposed two main types of co** strategies to manage stressful events: (a) problem-focused co** (i.e., engage in behaviors that could help solve problems); and (b) the emotion-focused co** (i.e., regulate emotional responses to the problem without affecting the actual presence of stress) [82]. Within this theoretical model, the challenges determined by the COVID-19 situation may have forced young people to more likely turn to social networking for both problem-focused co** (e.g., browsing health-related information) and emotion-focused co** (e.g., venting emotions for mood management, joining online communities for social support, meeting online friends and peers) [83]. Accordingly, our findings found that young people experiencing a PSMU significantly described lower aggressiveness (physical and verbal) and anger levels compared to those without a PSMU, who reported significantly higher levels of general psychopathology, anxiety, stress and depression. Indeed, our PSMU sample reported significantly higher impulsiveness levels (particularly, motor and attentional impulsiveness) compared to young people without a PSMU. Our mediation analyses also demonstrated that the association between higher depression levels and lower BSNAS levels was mediated by higher aggressiveness and lower impulsiveness levels. Therefore, the protective effect of social networking use could be beneficial to younger people, at different degrees according to specific personality profiles (i.e., levels of impulsiveness and aggressiveness).

Future studies should confirm these findings and evaluate specifically the role of the impulsiveness and aggressiveness personality traits in mediating the relationship between PSMU and depression, along stressful events. Furthermore, our study contains limitations that should be considered before generalizing our findings. First, the cross-sectional design allows only hypothetical conclusions of causality between the protective role of SM use in the emergence of depressive symptomatology in the general population during COVID-19 outbreak. We could not argue whether the SM use was protective only during the early stages of COVID-19 phases and if such a protective effect will be maintained over time. Therefore, longitudinal studies should collect data, considering the number of occurring COVID-19 lockdowns, the intensity and frequency in social networking use pre-COVID-19, during each COVID-19-related phase and post-COVID-19. Second, the convenience sampling method and online recruitment strategy, while allowed to overcome the recruitment obstacles due to physical distancing measures, could be highly vulnerable to selection bias and may imply a significant sampling problem. Third, our sample included apparently healthy participants (without a previous PSMU) and, to validate the association between SM use and depression, our findings should be replicated in a clinical sample. Fourth, our sample is mainly constituted by young university students, with a possible unbalance with respect to young workers; thus, further studies should recruit by stratifying workers and students, in order to more reliably compare findings coming from both groups and evaluate whether this protective effect is only found in the university students. Finally, we did not collect data on the time spent on social networking platforms nor regarding the type of social network, therefore, further studies should find out the role of time spent on SM and of the type of used/preferred social networking platform.

Overall, our study documented a potential positive role of social networking platforms, at least during the early stages of the COVID-19 pandemic, particularly in overcoming depressive symptomatology and general psychopathological burden due to the COVID-19 situation, in young adults. Furthermore, our findings highlight the need of implementing youth-friendly targeted SM-based interventions, specifically addressed to digital natives to overcome the emergence of potential depressive symptomatology due to stressful events.