Don’t put all social network sites in one basket: Facebook, Instagram, Twitter, TikTok, and their relations with well-being during the COVID-19 pandemic

Alexandra Masciantonio; David Bourguignon; Pierre Bouchat; Manon Balty; Bernard Rimé

doi:10.1371/journal.pone.0248384

. 2021 Mar 11;16(3):e0248384. doi: 10.1371/journal.pone.0248384

Don’t put all social network sites in one basket: Facebook, Instagram, Twitter, TikTok, and their relations with well-being during the COVID-19 pandemic

Alexandra Masciantonio ^1,^*, David Bourguignon ¹, Pierre Bouchat ¹, Manon Balty ¹, Bernard Rimé ²

Editor: Barbara Guidi³

PMCID: PMC7951844 PMID: 33705462

Abstract

Prior studies indicated that actively using social network sites (SNSs) is positively associated with well-being by enhancing social support and feelings of connectedness. Conversely, passively using SNSs is negatively associated with well-being by fostering upward social comparison and envy. However, the majority of these studies has focused on Facebook. The present research examined the relationships between well-being—satisfaction with life, negative affect, positive affect—and using actively or passively various SNSs—Facebook, Instagram, Twitter, TikTok—during the COVID-19 pandemic. In addition, two mediators were tested: social support and upward social comparison. One thousand four persons completed an online survey during the quarantine measures; the analyses employed structural equation modeling. Results showed that passive usage of Facebook is negatively related to well-being through upward social comparison, whereas active usage of Instagram is positively related to satisfaction with life and negative affect through social support. Furthermore, active usage of Twitter was positively related to satisfaction with life through social support; while passive usage was negatively related to upward social comparison, which, in turn, was associated with more negative affect. Finally, TikTok use was not associated with well-being. Results are discussed in line with SNSs’ architectures and users’ motivations. Future research is required to go beyond methodological and statistical limitations and allow generalization. This study concludes that SNSs must be differentiated to truly understand how they shape human interactions.

Introduction

The COVID-19 pandemic that has hit the world since the end of 2019 has led the governments of many countries to impose quarantine measures on their populations. For many people, these confinement measures led to a drastic reduction in interpersonal relations. However, interpersonal relations have powerful beneficial effects on physical and mental health [1,2]. In order to cope with the negative effects of social isolation on well-being, a significant number of recommendations were issued [3,4]. Several of them, derived predominantly from non-scholars, have promoted the use of social network sites (SNSs) to keep contact with family and friends [5,6]. Nevertheless, this assertion involves addressing one complex question: are SNSs really beneficial to well-being? The present study will contribute to this research question, through a short literature review and an empirical study. More research will be required to provide a clear understanding of how SNSs impact well-being.

Definition of key concepts

Before addressing the relationship between SNSs and well-being, both need to be defined. On one side, the familiar definition of Ellison and boyd [7 p157] described SNSs as networked communication platforms in which participants 1) have uniquely identifiable profiles that consist of user-supplied content, content provided by other users, and/or system-level data; 2) can publicly articulate connections that can be viewed and traversed by others; and 3) can consume, produce, and/or interact with streams of user-generated content provided by their connections on the site.

On the other side, the term well-being refers, in this study, to the subjective part of well-being. Instead of relying on physical and material resources, subjective well-being can be understood as “people’s overall evaluations of their lives and their emotional experiences.” [8 p87]. Hence, subjective well-being is a multidimensionality construct, and each component needs to be assessed individually. Typically, subjective well-being comprises at least three components: a sense of satisfaction with life, the presence of positive affect, and the absence of negative affect [8]. The satisfaction with life allows to capture how people evaluate their lives (i.e., cognitive level of subjective well-being). Likewise, positive and negative affect reflect what feelings people experience in their lives (i.e., affective level of subjective well-being).

Literature review

The relation between SNSs and well-being may at first seem inconsistent. Several studies showed that SNSs use is negatively associated with well-being [9,10], while others revealed a positive relationship [11,12]. However, these studies relied on an overall measure of SNSs use, whereas two distinguish usages can be proposed: an active (e.g., interacting directly with others by posting content or commenting others’ content) and a passive one (e.g., reading and consuming others’ content). Gerson, Plagnol and Corr [13] pointed out how these usages match with specific SNS activities, demonstrating they reflect related, but separate constructs. In that respect, results seem more uniform when different modalities of SNSs use have been taken into account: actively using SNSs is positively associated with well-being and in contrast, passively using SNSs is negatively associated with well-being [14–19]. Verduyn, Ybarra, Résibois, Jonides and Kross [20] reviewed the literature and identified the mechanisms underlying these relationships. Their model suggests that actively using SNSs increases subjective well-being by improving social capital and feelings of connectedness. Conversely, passively using SNSs lessens subjective well-being by fostering social comparison and envy. Although this model is a major step to clarify the consequences of SNSs on well-being, most of the studies underpinning these mechanisms have focused on Facebook. Doing so, one can wonder whether other SNSs might have different impacts on well-being.

Few studies have investigated the effects of different SNSs on well-being. Pittman and Reich [21] demonstrated that the use of image-based platforms (e.g., Instagram, Snapchat) was positively associated with well-being and negatively with loneliness, whereas text-based platforms (e.g., Twitter, Yik Yak) were not related to well-being and loneliness. Recently, Chae [22] has also examined the relationships between various platforms and well-being through social comparison. As expected, social comparison was negatively associated with well-being; but while Instagram and LinkedIn enhanced social comparison, Twitter decreased it. Surprisingly, Facebook use was not related to well-being. These two studies have therefore yielded contradictory outcomes, but they employed an overall measure of SNSs use which makes impossible to investigate the distinct effects of passive and active usages.

Overview of the research

To draw conclusions on SNSs and well-being, the literature on passive and active usage need to be integrated with the literature on cross-media studies. To that end, the present research examines the relationships between various SNSs and well-being through two mediators—social support and upward social comparison. Specifically, this study focuses on the active and passive usages of four popular SNSs: Facebook, Instagram, Twitter and TikTok [23]. Although Facebook, Instagram and Twitter are henceforth well studied in the literature, TikTok is a new SNS created in 2016 with a number of users increasing day by day [24]. These SNSs differ from each other by their architectures [25]: Facebook incorporates both image and text, Twitter is text-based, and Instagram as well as TikTok are image-based (the first concerns pictures and the second relies on videos). Instagram, TikTok and Twitter are also unidirectional (i.e., possibility to follow someone’s content without their approval), whereas Facebook is dyadic (i.e., need to be approved by someone to access their content). Moreover, people do not use them for the same reasons: Facebook use is mainly related to social support and self-presentation [26]; Instagram allows users to self-document, self-promote, express one’s creativity and see other’s content [27]; Twitter use is mainly driven by informational needs [28,29]. Finally, only one study examined TikTok use and concluded that the platform was seen as a “recording tool rather than a social media app” [30 p132]. Indeed, self-document was the most important motivation to use TikTok.

The model of Verduyn et al. [20] is mainly based on Facebook use, one would therefore expect to draw the same conclusions as the authors:

Hypothesis 1: Social support mediates the positive association between actively using Facebook and subjective well-being, and upward social comparison mediates the negative association between passively using Facebook and subjective well-being.

Image-based SNSs, such as Instagram and TikTok, have been shown to be related to well-being [21]. Moreover, Instagram users want to keep in touch with their friends, but also to self-promote [27]. Hence, social support and upward social comparison could both play a part in this relation. One would therefore expect the model of Verduyn et al. [20] to be generalized to Instagram:

Hypothesis 2: Social support mediates the positive association between actively using Instagram and subjective well-being, and upward social comparison mediates the negative association between passively using Instagram and subjective well-being.

In contrast, TikTok use was not firstly motivated by social interaction or self-presentation [30]. So, no assumption can be made about the mediating roles of social support and upward social comparison. The only hypothesis which can be proposed is:

Hypothesis 3: Actively using TikTok is positively associated with well-being and passively using TikTok is negatively associated with well-being.

Finally, text-based SNSs do not appear to be related to well-being [21]. The following hypothesis is therefore proposed for Twitter:

Hypothesis 4: Actively and passively using Twitter is not associated with well-being.

Method

Participants and procedure

One thousand four persons agreed to participate in the study. Among them, were excluded those reporting missing data and under the age of 18. The final sample was composed of 793 participants (613 women, 178 men and 2 persons who have a gender identity other than male or female) aged between 18 and 77 years old (M = 33.75, SD = 14.70). All participants were francophone: 463 were French, 264 were Belgian, 20 were Swiss and 46 had another nationality. Regarding the highest degree completed, one person had no primary education, 253 had a high school degree, 285 had a university short cycle degree (two or three years), 207 had a university long cycle degree (four or five years) and 47 had a doctorate. Finally, 89% had a Facebook account (N = 703), 63% had an Instagram account (N = 502), 38% had a Twitter account (N = 300) and 15% had a TikTok account (N = 121). An anonymous online survey was created using the Qualtrics Survey Software. Participants were recruited through academic mailing lists from social science, which explains the large proportion of women and academic people in the sample. Before completing the measures, all participants signed an informed consent form and accepted voluntary to take part in this research. Data collection was carried out from 7th April 2020 to 16th April 2020. Measures reported in the present study are part of a larger questionnaire; all data are available in OSF (Open Science Framework) at: https://osf.io/s5mjx/.

Measures

Overall SNS use: When participants declared to have an account for one of the four SNSs (Facebook, Instagram, Twitter, TikTok), they indicated the frequency they used this SNS before and during the quarantine measures on a 7-point scale (never; between one and three times a year; less than once a month; one to four times a month; one to four times a week; one to three times a day; more than three times a day).

Active and passive usage of SNSs: To be consistent with the literature [13], we chose to measure passive and active usage as separate constructs. This means that users can have both an active and a passive SNS usage; they can spend most of their time scrolling their news feed, but they can also send messages throughout the day. When participants declared to have an account for a SNS (Facebook, Instagram, Twitter, TikTok), they were therefore asked how much they used this SNS actively (1 = not actively at all; 7 = very actively) and passively (1 = not passively at all; 7 = very passively) during the quarantine measures [19]. Active usage was defined as “posting and commenting on [Facebook][Instagram][Twitter][TikTok], for example: post content on your profile, react to posts and comments from other users, etc.”, while passive usage as “browsing [Facebook][Instagram][Twitter][TikTok], for example: scrolling through your news feed, looking at other users’ profiles, etc.”.

Motivations to use SNSs: Three motivations to use SNSs were derived from Cheung, Chiu and Lee [31]: maintaining interpersonal interconnectivity (“To stay in touch”; “To have something to do with others”), purposive value (“To get information”; “To provide others with information”) and entertainment value (“To pass time away when bored”; “To be entertained”). Participants were asked to rate the extent to which these 6 items correspond to their motivations to use SNSs during the quarantine measures on a 7-point Likert scale (1 = strongly disagree; 7 = strongly agree). McDonald’s ω computed a value of.80 for maintaining interpersonal interconnectivity,.57 for purposive value and.79 for entertainment value.

Social support on SNSs: Social support on SNSs was measured using eight items adapted from Nick et al. [32]. Two items were chosen for each subscale (emotional support, informational support, social companionship and instrumental support). Participants indicated their agreement with these items on a 7-point Likert scale. Sample items include “During quarantine measures, people show that they care about me on social network sites.” and “During quarantine measures, people give me useful advice on social network sites.”. Scores for each subscale were averaged such that a higher overall score indicated greater social support on SNSs during the quarantine measures (McDonald’s ω = .83).

Upward social comparison: The upward social comparison was inspired from Brunot and Juhel [33] and consisted in two items: “On social network sites, I sometimes think that my relatives (friends, family and colleagues) are fare better than me during the quarantine measures” and “On social network sites, I sometimes think that my relatives (friends, family and colleagues) are better off than me”. Participants indicated their agreement with these items on a 7-point Likert scale (McDonald’s ω = .84).

Positive affect: Positive affect were assessed by asking participants to rate of much they feel “Optimistic, encouraged, hopeful” and “Proud, trustful, self-confident” on a 7-point Likert scale (McDonald’s ω = .76). The measure was adapted from Fredrickson [34].

Negative affect: Negative affect were assessed by asking participants to rate of much they feel “Sad, depressed, unhappy”, “Angry, furious” and “Anxious, frightened” on a 7-point Likert scale (McDonald’s ω = .75). The measure was adapted from Gaudreau, Sanchez and Blondin [35].

Satisfaction with life: Satisfaction with life was measured using the Satisfaction with Life Scale [36]. An example item is “I am satisfied with my life”. Participants indicated their agreement with the five items on a 7-point Likert scale. Given the good reliability (McDonald’s ω = .89), the five items were aggregated.

Results

Analyses were conducted using the JASP software [37].

Preliminary analyses

An exploratory analysis of the data is available in OSF at: https://osf.io/fe4pn/. Four paired samples T-Tests have been also carried out between the overall SNS use before the quarantine measures and during the quarantine measures. Results showed that the overall use have increased during the quarantine for all SNSs, and in particular for TikTok: Facebook (t₍₇₀₂₎ = 11.84, p < .001, d = .45), Instagram (t₍₅₀₁₎ = 6.33, p < .001, d = .28), Twitter (t₍₂₉₉₎ = 4.02, p < .001, d = .23) and TikTok (t₍₁₂₀₎ = 10.31, p < .001, d = .94). Finally, correlations between overall SNS use during quarantine and motivations to use SNSs are presented in Table 1.

Table 1. Pearson’s correlations between overall SNSs use during the quarantine measures and motivations to use SNSs.

	Maintaining interpersonal interconnectivity	Purposive value	Entertainment value
Overall Facebook use during quarantine	.101^**	.076^*	.135^***
Overall Instagram use during quarantine	.100^*	.062	.376^***
Overall Twitter use during quarantine	.008	.147^*	.307^***
Overall TikTok use during quarantine	.132	.090	.229^*

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Note.

*p < .05;

**p < .01;

***p < .001.

Main analyses

Structural equation modeling with Lavaan [38] was used to examine the relationships between SNSs (Facebook, Instagram, Twitter, TikTok) and well-being (positive affect, negative affect and satisfaction with life) through two mediators, social support and upward social comparison. For each model, dependent variables were controlled for age and gender. A one step approach was employed, that means that the parameters of the measurement model and the structural model were estimated simultaneously. Analyses were carried on with DWLS (diagonally weighted least squares) estimator which is adapted for data violating normality [39]. Five fit indices were chosen: χ² (chi-square), SRMR (Standard Root Mean Square Residuals), RMSEA (Root Mean Square Error of Approximation), CFI (Comparative Fit Index) and TLI (Tucker-Lewis Index) [39]. The first two address the global fit of the model: χ² must be nonsignificant and the value of SRMR must be equal or lower to.08. RMSEA concerns the parsimony of the model and must be lower to.06. Lastly, CFI and TLI are incremental indices and must be superior to.9.

Facebook

All standardized item loadings exceeded.4 and were significant (p < .001). The results also revealed a satisfactory model fit to the data: χ²(227, N = 703) = 723.86, p < .001; SRMR = .06; RMSEA = .056; CFI = .948; TLI = .938. Although the χ² is significant, this statistic is very sensitive to sample size [39].

As shown in Fig 1, direct paths from actively and passively using Facebook to satisfaction with life, positive affect and negative affect were nonsignificant (p >.05), except the path from using actively Facebook to negative affect (β = .17, p < .05). Contrary to hypothesis 1, direct path from actively using Facebook to social support was nonsignificant (p >.05), but direct path from passively using Facebook to upward social comparison was significant (β = .13, p < .05). All estimated paths from social support and upward social comparison to the three constructs of well-being were significant (p < .05). Consistent with hypothesis 1, the indirect effects of passively using Facebook on well-being (satisfaction with life, positive affect and negative affect) through upward social comparison were significant (p < .05).

In other words, results revealed that upward social comparison mediates the negative association between passively using Facebook and subjective well-being. Nonetheless, using actively Facebook was also directly associated with greater negative affect.

Instagram

All standardized items loadings exceeded.4 and were significant (p < .001). The results also revealed a satisfactory model fit to the data: χ²(227, N = 502) = 532.32, p < .001; SRMR = .061; RMSEA = .052; CFI = .955; TLI = .947.

As shown in Fig 2, direct paths from actively and passively using Instagram to satisfaction with life, positive affect and negative affect were nonsignificant (p >.05). Contrary to hypothesis 2, direct path from passively using Instagram to upward social comparison was nonsignificant (p >.05), but direct path from actively using Instagram to social support was significant (β = .21, p < .05). All estimated paths from social support and social comparison to the three constructs of well-being were significant (p < .05), except the path from social support to positive affect which was nonsignificant (p >.05). Partially consistent with hypothesis 2, the indirect effects of actively using Instagram on satisfaction with life and negative affect through social support was significant (p < .05).

In other words, results revealed that social support mediates the positive association between actively using Instagram and satisfaction with life on one hand, and the positive association between actively using Instagram and negative affect on the other.

Twitter

All standardized items loadings exceeded.4 and were significant (p < .001). The results also revealed a satisfactory model fit to the data: χ²(227, N = 300) = 415.61, p < .001; SRMR = .071; RMSEA = .053; CFI = .953; TLI = .944.

As shown in Fig 3, direct paths from actively and passively using Twitter to satisfaction with life, positive affect and negative affect were nonsignificant (p >.05). Contrary to hypothesis 4, direct path from passively using Twitter to upward social comparison was significant (β = -.14, p < .05), and direct path from actively using Twitter to social support was also significant (β = .15, p < .05). All estimated paths from social support and social comparison to the three constructs of well-being were significant (p < .05), except the path from social support to positive affect which was nonsignificant (p >.05). The indirect effect of actively using Twitter on satisfaction with life through social support was significant (p < .05). Likewise, the indirect effects of passively using Twitter on negative affect through upward social comparison was significant (p < .05), and the indirect effects of passively using Twitter on satisfaction with life and positive affect through upward social comparison was significant were near significant (p = .06 for satisfaction with life; p = .056 for positive affect).

In other words, results showed that actively using Twitter was associated with more social support, and that using passively Twitter was associated with less upward social comparison. In addition, social support mediated the relation between using actively Twitter and satisfaction with life, and social comparison mediated the relation between passively using Twitter and negative affect.

TikTok

All standardized items loadings were significant (p < .05) and exceeded.4, except one item of the social support construct (.24). The results also revealed that the model fits the data well: χ²(227, N = 121) = 227.034, p >.05; SRMR = .084; RMSEA = .001; CFI = 1.000; TLI = 1.000.

As shown in Fig 4 and inconsistent with hypothesis 3, direct paths from actively and passively using TikTok to satisfaction with life, positive affect and negative affect were nonsignificant (p >.05). Besides, direct paths from passively and actively using Twitter to upward social comparison and social support respectively, were nonsignificant (p >.05). Lastly, only paths from upward social comparison to positive and negative affect were significant (p < .05).

In other words, results revealed that actively and passively using Tiktok was not associated with well-being, and that social support and upward social comparison did not appear to play a meditational role between TikTok use and well-being.

Discussion

Past researches have shown that actively using SNSs is positively associated with well-being through social support, and that passively using SNSs is negatively associated with well-being through upward social comparison [20]. This study extends the scope of this conclusion by systematically testing the model to various SNSs (Facebook, Instagram, Twitter, TikTok) within a wider context: the COVID-19 pandemic.

First of all, participants’ increase in the use of all SNSs during the quarantine measures strengthens the need to explore the relation between SNSs and well-being. Consistent with Verduyn et al. [20], upward social comparison mediated the negative association between passively using Facebook and well-being. Nevertheless, no relation was found for active Facebook usage and social support (hypothesis 1 partially supported). Instagram showed the opposite relation: social support mediated the positive association between actively using Instagram and well-being (satisfaction with life and negative affect). In contrast to Chae [22], no relation was found for passively using Instagram and upward social comparison (hypothesis 2 partially supported). One surprising outcome is that negative affect were positively related to social support and using actively Facebook. However, in line with Rimé, Bouchat, Paquot and Giglio [40], it is plausible that interacting with others on SNSs elicits emotional reactivation rather than discharge. As a consequence, obtaining social support during the COVID-19 pandemic, a negative and painful event, may increase negative affect. This result is particularly interesting and highlights the role of the socio-emotional context in the relation between SNSs and well-being. As regard to TikTok, no association with well-being, social support or upward social comparison was found (hypothesis 3 not supported). Finally, actively using Twitter was associated with more social support, and passively using Twitter with less upward social comparison. Furthermore, social support mediated the relation between using actively Twitter and satisfaction with life, and upward social comparison mediated the relation between passively using Twitter and negative affect (hypothesis 4 not supported). In other words, our results are fully consistent with those of Chae [22] and demonstrate that, rather than an absence of relation [21], both active and passive usage of Twitter can be positively related to well-being. Which might seem surprising—the negative association between passive usage of Twitter and upward social comparison–may find an explanation in the social context of Twitter. Indeed, previous studies have shown that negative messages are shared faster on Twitter [41] and that popular events on Twitter are associated with negative emotions [42]. Recently, Waterloo, Baumgartner, Peter and Valkenburg [43] showed than negative emotions are perceived as more appropriate on Facebook and Twitter, compared to Instagram. Hence, it is plausible that Twitter’s users scrolling through their Twitter news feed and seeing constant bad news from their followers, are more inclined to compare their situation with what they consider to be worse (i.e. downward social comparison), rather than better off (i.e. upward social comparison). Conversely, Facebook is known to be a place for positive self-presentation and impression management [26], which could explain the positive association with upward social comparison.

In that respect, it seems that the model proposed by Verduyn et al. [20] does not stand for every kind of SNSs. Facebook and Instagram use matched partially to the underlying mechanisms, but TikTok use had almost no relation to well-being, and passive usage of Twitter was negatively associated with upward social comparison. The issue is therefore to understand what characteristics and features of SNSs are accountable for these differences. In contrast with Pittman and Reich [21], the findings did not support the architecture of SNSs. Rather, it seems that users’ motivations are more indicative: Twitter and TikTok use during quarantine were not related with social relationships, contrary to Facebook and Instagram. But while TikTok use was only related to entertainment, Twitter use was also related to some purposive values which are considered as a subtype of social support [i.e., informational support, 32]. This is, by the way, fully in line with the literature on motivations to use Facebook, Instagram, Twitter, and TikTok [26–30]. Future studies should further explore how users’ motivations affect the relation between SNSs and well-being.

Concerning the specific context of the COVID-19 pandemic, in the words of IJzerman et al. [44] “psychological science is not yet a crisis-ready discipline”, and caution should therefore be taken to give recommendations. This single study does not allow to give advice. Maybe conclusions are solely to not systematically promote an overall use of SNSs but rather to distinguish active and passive usages, and to differentiate social network sites due to their specificities.

Finally, the present study is not devoid of limitations. Since participants were recruited via academic mailing lists from social science, the sample is quite biased towards academic people, as well as women (there are a majority of women in social science). This kind of limitation is common in research about social network sites, but we could suspect that this unbalance-sample limits the generalization of the results. Likewise, the small number of participants having a TikTok account, and the fact that all participants were Francophone, highlight the need to replicate the study in other populations. Second, to avoid demotivating respondents, we have limited the questionnaire length. Consequently, passive and active SNS usages have been measured with one item. Although they are considered as separated constructs in the literature [13], we may suspect that the use of single items has increased their association. Future studies should therefore assess specific activities on each SNS. For the same reason, only three kinds of motivation were included. But there is a lot of other reasons to use social network sites, like self-enhancement or self-documentation. Thirdly, as noted by an anonymous reviewer, we think that another good way of assessing our hypotheses would have been to test a model including all social network simultaneously. However, we think that this kind of modeling requires much more participants to draw valid conclusions. Last but not least, this study is cross-sectional, which do not allow to speak in terms of causality or consequences. For example, this study cannot support if people with lower well-being go on SNSs to increase their social support [45]. Future studies should therefore employ longitudinal and experimental designs.

Conclusions

The current research addresses the complex relation between SNSs and well-being. It extends the literature on passive and active usages by opening the reflection on various kinds of SNS. Passive usage of Facebook was related to social comparison, which, in turn, was associated with lower well-being. Besides, active usage of Instagram was related to social support, which, in turn, was associated with greater satisfaction with life but also negative affect. Regarding Twitter, active usage was also related to social support, which, in turn, was associated with greater satisfaction with life; but passive usage was rather negatively associated with upward social comparison, which, in turn, was associated with more negative affect. In contrast, TikTok use was not associated with well-being. Taken together, this study demonstrates that the differences between SNSs must be considered to truly investigate how SNSs shape human interactions—generalization to every kind of SNS should always be undertaken with caution.

Acknowledgments

We thank the participants for having dedicated their precious time to our study, especially in these difficult times.

Data Availability

Data are available in OSF (Open Science Framework) at: https://doi.org/10.17605/OSF.IO/S5MJX.

Funding Statement

The author(s) received no specific funding for this work.

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PLoS One. doi: 10.1371/journal.pone.0248384.r001

Decision Letter 0

Barbara Guidi

20 Nov 2020

PONE-D-20-27590

Don’t put all social network sites in one basket: Facebook, Instagram, Twitter, TikTok and their relations with well-being during the COVID-19 pandemic.

PLOS ONE

Dear Dr. Masciantonio,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Dear authors,

the manuscript requires several improvements, as highlighted in the reviews. Principally, the dataset needs to be improved with more data and it should be analysed more in details.

Please, follow all the requirements suggested by the review before the re-submission of the paper.

Please submit your revised manuscript by Jan 04 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Barbara Guidi

Academic Editor

PLOS ONE

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2. We note that you have stated that you will provide repository information for your data at acceptance. Should your manuscript be accepted for publication, we will hold it until you provide the relevant accession numbers or DOIs necessary to access your data. If you wish to make changes to your Data Availability statement, please describe these changes in your cover letter and we will update your Data Availability statement to reflect the information you provide.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: No

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This article deals with an interesting question: the relationship between social network usage and well-being during the COVID-19 pandemics. The article expands the horizon of the review made by Verduyn et al. (2017), by providing an analysis through multiple platforms, based on structural equation modeling. The research design is similar to the one described in Verduyn et al. (2017), with the same two mediators: social support and upward social comparison.

The procedure is correct, and the contribution of the work comes from the results it presents, which might help understand human interactions through social networks. From the methodological point of view, I think the work is not innovative. However, the procedure is sound and the literature review is also correct.

As I flaw, I found the data quite biased towards the female population (line 139): 77% females vs. 23% males, which the authors recognize as a limitation. I wonder if it is also biased towards certain socio-economic segments and age segments, as the cohort was obtained from an academic mailing list. Though this is common in many research works in this area, I think that this type of limitations should be briefly discussed.

In general lines, and despite its limitations, I think that the work can be considered for publication in PLOS ONE.

Minor details:

- This phrase seems contradictory (line 89) "Recently, Chae (21) showed a negative relationship between the use of Twitter, Instagram, LinkedIn and relative well-being through social comparison. Specifically, Instagram and LinkedIn enhanced social comparison, whereas Twitter decreased social comparison." I guess that the first sentence should be neutral: "Recently, Chae (21) showed a negative relationship between the use of Twitter, Instagram, LinkedIn and relative well-being through social comparison. Specifically, ....". Otherwise, it would be interpreted that there it showed a negative relationship between the use of Twitter and relative well-being through social comparison, and I think it was not the case. Please check.

- Line 213: "khi-deux" should read "chi-square".

Finally, I remark that the data used in this research has not been made available with the submission. I understand that the authors will make it public after acceptance, according to the PLOS ONE policy on Data Availability (http://journals.plos.org/plosone/s/data-availability). Data availability is mandatory, and should be checked before publication.

Reviewer #2: In this paper the authors present a study concerning possible correlations of the active/passive usage of social networks and medias and well-being, positive effects and negative effects. The paper seems overall well written, although some typos were found, but there are a number of very important weaknesses listed below.

1) All the key concepts of this paper are not well defined and are extremely vague. I did not find any definition of "well-being", for instance. Is it related to "being healthy"? In this case probably the model is not complex enough to capture the concept of "well-being" because it lacks other important aspects. The same goes for "Satisfaction with life", Positive affects", and "Negative affects". The clarification of these concepts would make the paper clear/easy to understand and technically sound.

2) In Section 2 there is a discussion concerning the 4 sns used for the analyses. A part of the discussion can be easily summarised considering the fact that Facebook is a Social Network (build a network of known people), while the other three are Social Media (media sharing platforms). I agree that the study should be carried considering all the platforms separately, but I somewhat expect some similarities between social media platforms, especially between Instagram and TikTok.

3) The dataset does not seem to be relevant for the study.

- It is made of only 793 people

- The number of women is much higher. In many datasets it was shown that usually the number of females and males on sns are similar or only slightly unbalanced. But not 75% women and 25% men.

- They are all francophone, thus probably living in a specific geographic region

- Participants were recruited using academic mailing lists, thus they are probably all academic people.

- The number of people using Twitter or TikTok seems very low.

The low number of people and all the other very specific features makes me think that probably there is a very strong bias in the dataset.

4) The authors should spend more effort in motivating the methodology. Here are some questions:

- line 154: Why did you use a 7-point scale?

- lines 159-160: does that mean that a user can be both very active and very passive at the same time? And what does "very actively" means? Once per day is "very active"?

- line 172: you use Likert scale extensively in your paper. A relevant citation would increase the quality of the paper. Additionally, why did you use that scale? are there alternatives? Why is Likert the best choice in this scenario?

5) I would spend more effort also in the reorganisation of the contents in the paper: sometimes results (like the McDonald's omega) is shown in the framework presentation section, and part of the framework (lines 210-218) is presented in the result section. You should separate better the framework from the results to help readability.

typos:

line 35: "Analyzes employed", plural of "analysis" is"analyses"

lines 60-64: probably there is some problems with the indentation of the text here

Reviewer #3: The manuscript in question approaches an important problem of how different types of social networks affect subjective well-being of users.

The authors design a thorough survey that includes assessment of such measures as motivation, social support, upward social comparison etc.

Then they use a well established framework of structural equation modeling (SEM) to evaluate direct and indirect effects of active and passive use of social networks on well-being comprised of satisfaction with life, positive affect and negative affect.

1) The paper would benefit from exploratory data analysis: how do the distribution of measures look, what are correlations between them? It is an important first step that can serve as a sanity check when using SEM.

a) Two genders are well represented in this study. It is clear that gender might be an important factor in determining how well-being is derived from the use of social networks, so it should included during modeling.

At the very least it would be interesting to compare the distributions of measures by gender.

b) The gender ratio is imbalanced. Why is it the case? How does it compare to the gender ratio of the recipients of the survey invitation? Does it create a bias? Do those who are not likely to participate in surveys use social networks in the same way? Well, given the gender imbalance of this survey and if there are significant gender differences in SN use, we can hypothesize that those who haven't participated have the inverse gender ratio and so their average motivation and modus operandi might be very different.

3) It appears that the measures were deduced for users who potentially use a mix of social networks. So if someone uses both Facebook and Instagram how do we estimate the fraction that contributes to his motivation for each network?

It seems like a good model should include the use of all networks simultaneously.

4) It would be nice to see a discussion of the impact of the survey invitation being circulated in academic mailing lists and the bias it potentially introduces.

5) Active and passive SN use are treated independently, if I understand correctly. But this is an assumption, and at the very least it should be discussed. It would be interesting to see if the three groups of those who are using actively, passively and both actively and passively have similar distributions of measures.

I conclude that the manuscript is an analysis of the effect social networks on personal well-being based of a large and feature-rich dataset. However, the dataset analysis is incomplete.

This manuscript requires a major revision. Once all the points raised in this review are addressed it can be published in PLOS.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

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PLoS One. 2021 Mar 11;16(3):e0248384. doi: 10.1371/journal.pone.0248384.r002

Author response to Decision Letter 0

2 Dec 2020

We are re-submitting in PLOS One our paper "Don’t put all social network sites in one basket: Facebook, Instagram, Twitter, TikTok, and their relations with well-being during the COVID-19 pandemic.".

We thank the editor and the reviewers for their time spent carefully reviewing our manuscript, and for their valuable comments. We made sure that each one of the reviewer’s comments has been addressed carefully; we believe that the manuscript has been really improved.

Here are the major revisions made to the manuscript:

• Regarding the unbalance-sample, dependent variables were controlled for gender and age. This change did not affect the models for Facebook, Instagram and TikTok, but led to substantial revisions in the Twitter’s model. We think that this contribution enhances the scope of our discussion.

• The definition of what is meant by a passive and an active usage of social network sites had been more detailed.

• As requested, we made our date available on an online repository; the DOI necessary to access our data is (anonymized link for blind peer review): https://osf.io/s5mjx/?view_only=b852a4a3eb884b8bb11b83256bee0161. We also made sure that the manuscript meets PLOS ONE's style requirements.

Minor revisions, if not explained, are applied to the manuscript, mostly for correcting typing errors, and for minor modification along with revisions explained in this document.

The responses to all the reviewer’s comments are detailed in the "Response to reviewers" file.

Please let us know if you still have any questions or concerns about the manuscript. We will be happy to address them.

Sincerely,

The authors of paper PONE-D-20-27590.

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(49.4KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0248384.r003

Decision Letter 1

Barbara Guidi

21 Dec 2020

PONE-D-20-27590R1

Don’t put all social network sites in one basket: Facebook, Instagram, Twitter, TikTok, and their relations with well-being during the COVID-19 pandemic.

PLOS ONE

Dear Dr. Masciantonio,

Reviewers highlighted that the manuscript needs minor revisions in order to be accepted as a possible publication. Please revise the paper by following the suggestions given by the reviewers.

Please submit your revised manuscript by Feb 04 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Barbara Guidi

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

Reviewer #3: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Partly

Reviewer #3: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Reviewer #1: The authors have addressed all the remarks and the quality of the manuscript has been improved. In particular, they have correctly discussed the limitations, clarified some relevant definitions for their work, and made their dataset available.

I consider that the manuscript can be accepted for publication in PLOS ONE.

Minor note: I observe that the authors have changed the subsection title "Measures" into "Materials" on line 164. I think that "Measures" might be more appropriate and standard in the field.

Reviewer #2: Authors put an extraordinary effort to revise and improve the paper. I have just a few follow-up points:

- line 33: you claim that 1008 people took the test, but on line 146 you claim that 1004 people took the test. Please, put the correct number in the paper.

- lines 170-179: now it's much more clearer what "actively" and "passively means", but I still wonder whether it was a good idea to keep these two factors separated. In this way one can be "non active" and "non passive" at the same time which does not make much sense (or am I still missing something?). Additionally, if I got it right, they are mutually exclusive activities: if I am scrolling through posts, I am not creating posts at the very same time. I may spend equal time in active and passive behaviour, and that's why I think that a single indicator is better here. Can you please motivate in the paper why you needed two different indicators? You also asked the participants their "overall SNS use", but I don't see it used in the paper, why?

- concerning your data, I understand your limitations, and that's fine, but you should state more clearly in the abstract and the introduction sections that this is a preliminary work.

Reviewer #3: I would like to thank the authors for such a quick and thorough revision.

From my point of view all of the suggested modification have been implemented except for item 1).

The descriptive statistics and correlation table following the link

https://osf.io/s5mjx/?view_only=b852a4a3eb884b8bb11b83256bee0161

shed little light.

I would still recommend trying to visualize the exploratory data analysis (EDA) in terms of histograms and KDE plots of PDFs. See, for example, https://seaborn.pydata.org/generated/seaborn.pairplot.html

One could separate the group of actively using social networks per network by its median into two and plot PDFs for positive affect, negative affect etc questions, or directly use x-y scatter plots with with KDE.

I would encourage to perform such EDA for all available variables, including age, gender etc

With EDA plots the reader would be prepared and actually expect the result. The benefits of EDA include dataset consistency check and motivation of the model: is the input dataset biased and what conclusion should we expect from the model?

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

PLoS One. 2021 Mar 11;16(3):e0248384. doi: 10.1371/journal.pone.0248384.r004

Author response to Decision Letter 1

20 Jan 2021

We responded to each point raised by the academic editor and reviewers in the separate file labeled 'Response to Reviewers'.

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(27.6KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0248384.r005

Decision Letter 2

Barbara Guidi

9 Feb 2021

PONE-D-20-27590R2

Don’t put all social network sites in one basket: Facebook, Instagram, Twitter, TikTok, and their relations with well-being during the COVID-19 pandemic.

PLOS ONE

Dear Dr. Masciantonio,

The paper should be revised. Please follow the MINOR SUGGESTIONS given by the reviewers.

Please submit your revised manuscript by Mar 26 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Barbara Guidi

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Partly

Reviewer #3: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Reviewer #1: I appreciate the data exploration analysis that the authors have included in the current revision.

As the authors have satisfactorily addressed the comments, I consider that the manuscript can be accepted for publication in PLOS ONE.

Reviewer #2: (No Response)

Reviewer #3: The exploratory data analysis was nominally performed, however, it is completely detached from the rest the analysis (and not very informative).

Usually it serves the purpose of motivating further analysis using more advanced techniques.

For instance, in lines 266-268,"Age was associated with satisfaction ... with life negative affect ... and positive affect"

The significance and the signs of effects should be manifest in EDA.

I believe any reader would appreciate an announcement of strong correlation in the prelude and a following confirmation by a stronger method.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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PLoS One. 2021 Mar 11;16(3):e0248384. doi: 10.1371/journal.pone.0248384.r006

Author response to Decision Letter 2

15 Feb 2021

We thank again the editor and the reviewers for the time they spent reviewing our manuscript.

The detailed responses to all the reviewer’s comments are available in the "Response to Reviewers" file.

Sincerely,

The authors of paper PONE-D-20-27590.

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PLoS One. doi: 10.1371/journal.pone.0248384.r007

Decision Letter 3

Barbara Guidi

26 Feb 2021

Don’t put all social network sites in one basket: Facebook, Instagram, Twitter, TikTok, and their relations with well-being during the COVID-19 pandemic.

PONE-D-20-27590R3

Dear Dr. Masciantonio,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Partly

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #1: All comments have been addressed and the exploratory analysis has been expanded. I consider that the article can be accepted for publication in PLOS ONE.

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PLoS One. doi: 10.1371/journal.pone.0248384.r008

Acceptance letter

Barbara Guidi

3 Mar 2021

PONE-D-20-27590R3

Don’t put all social network sites in one basket: Facebook, Instagram, Twitter, TikTok, and their relations with well-being during the COVID-19 pandemic.

Dear Dr. Masciantonio:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Barbara Guidi

Academic Editor

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

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Data Availability Statement

Data are available in OSF (Open Science Framework) at: https://doi.org/10.17605/OSF.IO/S5MJX.

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PERMALINK

Don’t put all social network sites in one basket: Facebook, Instagram, Twitter, TikTok, and their relations with well-being during the COVID-19 pandemic

Alexandra Masciantonio

David Bourguignon

Pierre Bouchat

Manon Balty

Bernard Rimé

Roles

Abstract

Introduction

Definition of key concepts

Literature review

Overview of the research

Method

Participants and procedure

Measures

Results

Preliminary analyses

Table 1. Pearson’s correlations between overall SNSs use during the quarantine measures and motivations to use SNSs.

Main analyses

Facebook

Fig 1. The estimated standardized parameters of the Facebook model.

Instagram

Fig 2. The estimated standardized parameters of the Instagram model.

Twitter

Fig 3. The estimated standardized parameters of the Twitter model.

TikTok

Fig 4. The estimated standardized parameters of the TikTok model.

Discussion

Conclusions

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Barbara Guidi

Roles

Author response to Decision Letter 0

Decision Letter 1

Barbara Guidi

Roles

Author response to Decision Letter 1

Decision Letter 2

Barbara Guidi

Roles

Author response to Decision Letter 2

Decision Letter 3

Barbara Guidi

Roles

Acceptance letter

Barbara Guidi

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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