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. Author manuscript; available in PMC: 2022 Jun 30.
Published in final edited form as: J Community Psychol. 2020 Nov 10;49(2):432–446. doi: 10.1002/jcop.22469

Association of social participation, perception of neighborhood social cohesion, and social media use with happiness: Evidence of trade-off (JCOP-20-277)

Mesfin A Bekalu 1, Rachel F McCloud 2, Sara Minsky 2, Kasisomayajula Viswanath 3
PMCID: PMC9245361  NIHMSID: NIHMS1641448  PMID: 33169391

Abstract

Social participation and neighborhood social cohesion are positively associated with health and wellbeing. Given that in-person social interactions have generally dwindled over the past several decades at least in Western countries and social media use has become more common, in this study, we examined whether and how social media use interacts with social participation and neighborhood social cohesion in influencing happiness. Data were gathered from a representative sample of adults in Massachusetts, USA. General linear model was used to estimate the main and interaction effects of social participation, perception of neighborhood social cohesion and social media use on happiness, controlling for sociodemographics, marital status, employment, and self-rated health. Results indicated that both social participation and perception of neighborhood social cohesion were positively associated with happiness whereas social media use was not. However, there was a significant interaction effect of social media use and perception of neighborhood social cohesion on happiness. Compared with people with a high perception of neighborhood social cohesion, those with low perception were more likely to be happy as their social media use increased, suggesting that social media use may be helpful to promote happiness among people who perceive their neighborhoods as less supportive, trustworthy, and close-knit.

Keywords: happiness, neighborhood social cohesion, social participation, social media use

1 |. INTRODUCTION

Happiness has increasingly become a focus of public health inquiries beyond its original disciplines in psychology, sociology, and economics. A growing number of studies have explored the link between happiness and health in general and the possibility that impaired happiness may not only be a consequence of poor health but also a potential contributor to disease risk (Steptoe, 2019). About a decade ago, a synthetic analysis of 30 longitudinal studies on happiness and longevity by Veenhoven (2008, p. 449) found that happiness predicts longevity among healthy populations and as such although “happiness does not cure illness, it does protect against becoming ill.” In a similar vein, public health researchers and epidemiologists have argued that mainstream epidemiology that focuses on the study of diseases and risk factors should be supplemented with a “positive epidemiology” that focuses on health assets and a range of outcomes including happiness (Trudel-Fitzgerald et al., 2019; VanderWeele et al., 2020).

As both an outcome of and a factor contributing to good health, happiness has been shown to be associated with several individual and social factors, although associations may vary cross-culturally (Oishi & Gilbert, 2016). Studies show that happiness varies across age (Morgan et al., 2015), gender (Ngamaba, 2017), socioeconomic (Howell & Howell, 2008; Jebb et al., 2018; Oshio & Kobayashi, 2010), religious (Ngamaba & Soni, 2018), and racial/ethnic (Assari, 2019) groups. Research also indicates that happiness is associated with macrosocial and economic factors such as GDP per capita and freedom to make life choices (Ngamaba, 2017).

A large body of social science research has also looked at the multifaceted link between the different forms of social participation and happiness (Bian et al., 2018; Montpetit et al., 2017; Neira et al., 2018; Wang & Wong, 2014). For example, early on, Ishii-Kuntz (1990) found that the quality of social interaction measured by satisfaction with family life and friendship was positively related to the psychological well-being of American adults in all age groups. In the UK, based on rigorous analysis of a large set of predictors from various disciplines, Huxley and Mishra (2013) found that social capital was a robust predictor of happiness in addition to other factors such as income, marital status, religion, and discrimination. Drawing from nationally representative samples from three countries (Australia, Britain, and China), Bian et al. (2018) showed that social networks, particularly informal networks, have importance in increasing people’s subjective well-being in all three countries. In a study examining the link between leisure and happiness across 33 countries, Wang and Wong (2014) found that individuals who felt that they established useful contacts and strengthened relationships with others, among other things, felt happier than others. In all these studies, social participation, ranging from interactions with close companions such as families and friends to neighborhood and community level ties, is arguably pivotal in influencing happiness. Even in the well-documented link between religiosity and happiness, research shows that associations are much stronger for communal religious participation than for spiritual-religious identity or for private practices suggesting the importance of social participation as a mechanism linking religiosity with happiness (VanderWeele, 2017).

Research in public health and social sciences has long documented the pivotal role that social networks and social interactions play in health and wellbeing (Huxhold et al., 2013; Kauppipi et al., 2018; Marquez et al., 2014; Southwell, 2013; Southwell et al., 2010). Social network effects on health and wellbeing may result from the benefits that network members take advantage of within and across their networks and may include useful health-related information, social support, and other resources such as other network members’ skills and knowledge (Southwell, 2013; Viswanath, 2008). However, researchers argue that despite their pivotal role, social networks and the assets that accrue from them have dwindled over the past several decades, particularly in Western countries (Putnam, 2000). In his seminal work Bowling Alone, Putnam argued that over the past decades, Americans have become increasingly disconnected from one another and have withdrawn from public life and civic engagement. Similarly, according to McPherson et al. (2006), the number of confidants that Americans resort to discuss important matters decreased by about onethird between 1985 and 2004 (Arampatzi et al., 2018). In this regard, recent advances in communication technologies may hold promises to provide people with virtual platforms for interaction. Specifically, social media networks may have the potential to complement and, in some cases, substitute the decreasing face-to-face social interactions. Social networks, resulting from deliberate efforts of building community connection infrastructure or arising organically in response to different topics and issues in ways that are not planned (Southwell, 2013), may have the potential to provide people within and across geographies and social groups with platforms to share information and other useful resources.

However, the empirical basis for the promises of social media has been sparse and controversial. On the one hand, research suggests that social media can offer individuals with a platform that overcomes distance and time barriers to connect and reconnect with others and thereby expand and strengthen their offline networks and interactions (Antoci et al., 2015; Bekalu et al., 2019; Hall et al., 2018; Subrahmanyam et al., 2008). For example, Ellison et al. (2007) argue that apart from the two common forms of social capital—bonding and bridging—digital networks provide people with the tools to stay in touch with a social network after physically disconnecting from the network and thereby benefit from a form of social capital named “maintained social capital.”. In a study among university students, the authors found empirical support for this and argued that social media, in their case, Facebook use, could be beneficial for young people who experience low self-esteem and low life satisfaction. Similarly, another study among university students showed that social media use was positively related to individuals’ communication network heterogeneity, which, in turn, was positively associated with social capital and subjective well-being. A study by Valenzuela et al. (2009) also showed that intense social media use, in this case, Facebook use, was positively related to individuals’ life satisfaction, among other outcomes.

Yet a growing number of studies also suggest that social media use could be detrimental for mental health and/or affects happiness negatively. For instance, in a study among young adults, Krosss et al. (2013) examined the long-term effects of Facebook use on subjective well-being and found that Facebook use may undermine rather than enhance wellbeing. Another study that drew data from a sample of adolescents and their parents in the United States, found that social media use was moderately and positively associated with fear of missing out, loneliness, hyperactivity/impulsivity, anxiety, and depression (Barry et al., 2017). Similarly, in a national survey among American young adults, Primack et al. (2017) found that individuals who used 7 to 11 social media platforms had substantially higher odds of having increased levels of depression and anxiety symptoms compared with individuals who used 0–2 platforms. A recent study has also found both negative and positive associations of social media use with health and well-being and has proposed the need for a more sophisticated conceptualization and measurement of the social media use behavior beyond current dose–effect approaches (Bekalu et al., 2019).

To date, much of the research on the link between social media use and health has focused on negative mental health outcomes such as anxiety, fear of missing out, and depression. As such, relatively little is known whether and how social media use is associated with positive mental health outcomes such as happiness. In addition, little is known whether and how social media use interacts with social assets such as social participation and neighborhood social cohesion in predicting positive mental health outcomes such as happiness. Even the few studies that looked at the issue (e.g., Arampatzi et al., 2018) have predominantly focused on adolescent and young adult populations and have rarely involved participants across age groups. Against this background, in this study, first we examined whether and how social participation, perception of neighborhood social cohesion, and social media use were associated with happiness among a probability-based sample of adults from Massachusetts, USA. We then examined if there are interactions of social participation and perception of neighborhood social cohesion with social media use in predicting happiness.

2 |. METHODS

2.1 |. Data

Data for this study were gathered in July 2017 as part of a larger project called the Massachusetts Health Information National Trends Survey (MassHINTS). Partnering with Growth from Knowledge (GfK), a survey research firm that maintains a nationwide online probability-based panel (Knowledge-Panel), we conducted an online survey among a representative sample from across the state. GfK uses a household sampling frame that recruits houses with landline telephones, unlisted telephone numbers, cell phone–only households, and houses either with or without Internet access. GfK recruits panel members through probability-based selection that involves both random-digit dial and address-based sampling methods. Potential panelists are sent a recruitment letter and requested to complete a profile survey before becoming panel members. For households without a computer and/or access to the Internet, GfK provides a web-enabled device (laptop or netbook) along with free monthly Internet access. For this study, we used a sub-sample of the MassHINTS sample (n = 516) for whom we had responses to all our variables of interest.

2.2 |. Measures

2.2.1 |. Dependent variable

Happiness

The Pemberton Happiness Index, a scale widely used to measure wellbeing in the general population (e.g., Hervás & Vázquez, 2013), was used. The measure has 11 items related to remembered well-being, and each item is provided with a 11-point Likert scale. Although initially developed as a multidimensional scale covering the three dimensions of wellbeing (hedonic, eudaimonic, and social), the scale is considered unidimensional (Paiva et al., 2016). The Happiness score was calculated by averaging the 11 items (range 0–10), M = 7.3513, SD = 1.65, Cronbach’s alpha = 0.93.

2.2.2 |. Independent variables

The independent variables include social participation, perceived neighborhood social cohesion, and social media use.

Social participation

Using a measure adapted from Poulsen et al. (2011), respondents were asked to indicate how many times in a typical week they get together with friends or relatives, such as going out together or visiting in each other’s homes. The responses ranged from 0 to 35. Because responses were skewed and for reasons of gaining capability for examining moderation effects, we dichotomized the responses based on the median, which was 1, and created a categorical variable as low versus high participation.

Perceived neighborhood social cohesion

On a scale anchored at 1 = definitely disagree to 5 = definitely agree, respondents were asked whether they agreed or disagreed with the following four statements: (1) “People in this neighborhood help each other out”; (2) “There are people I can count on in this neighborhood”; (3) “People in this neighborhood can be trusted”; and (4) “This is a close-knit neighborhood.” Using the sum score, approximate tertiles of neighborhood social cohesion were created as low, medium, and high neighborhood social cohesion categories. These four items have been used by prior research such as Murillo et al. (2016) to assess neighborhood social cohesion. The internal consistency of the four items was high (Cronbach’s alpha = 0.92).

Social media use

Respondents were provided with a list of nine widely used digital social media platforms and were asked whether or not they used the Internet or a mobile app to use each platform in the last week. The list of social media platforms was adapted from previous research (Greenwood et al., 2016) and included: Twitter, Instagram, Pinterest, Facebook, LinkedIn, messaging apps like WhatsApp or Kik, an app that automatically deletes the messages you send like Snapchat or Wickr, anonymous social media apps like YikYak, Whisper, After School, or Rumr, a video-sharing site such as YouTube or Vimeo. A cumulative social media use index was created by summing responses indicating the use of the nine social media platforms (Ra et al., 2018). Actual scores ranged between 0 and 6, with 80% of the respondents reporting using at least one of the nine social media platforms. While we used a continuous media use variable in our analysis, we performed sensitivity analysis treating this variable as categorical to make sure that results were similar.

Sociodemographic variables

The sociodemographic variables assessed included age (measured as both continuous and categorical: 25–34, 35–44, 45–54, 55–64, and 65–74), gender (Male vs. Female), education (high school or less, some college, and bachelor’s or higher), household income (<$24,999; $25,000–$34,999; $35,000–$49,999; $50,000–$74,999; $75,000–$99,999; $100,000–$149,999; $150,000 and more), race/ethnicity (White, Non-Hispanic; Black, Non-Hispanic; Hispanic; and Other, Non-Hispanic), marital status (Never Married, Divorced/Separated, Widowed, and Living with Partner, Married), and employment status (Employed, Unemployed, and Retired). Self-Rated Health (individuals’ rating of their own health as excellent, very good, good, fair, or poor) was also assessed.

2.3 |. Analysis

First, we examined levels of happiness by socioeconomic and demographic factors—gender, age, education, income, and race/ethnicity—using univariate analysis of variance (ANOVA). In this analysis, all variables, including age, were treated as categorical. Then, using the general linear model (GLM), we examined the association of the three independent variables—social participation, perception of neighborhood social cohesion, and social media use—with happiness, controlling for the five sociodemographic variables (age as a continuous variable, gender, education, income, and race/ethnicity) as well as marital status, employment, and self-rated health. To test if there are interaction effects of social media use with social participation and perception of neighborhood social cohesion in influencing happiness, we included two two-way interaction terms—Social media Use by Social Participation, and Social Media Use by Perception of Neighborhood Social Cohesion into the GLM. All analyses were conducted using IBM SPSS version 20.

3 |. RESULTS

3.1 |. Descriptive statistics

Data on sociodemographic and control variables were analyzed using descriptive statistics (see Table 1).

TABLE 1.

Sociodemographic characteristics (n = 516)

Sociodemographic characteristics N (%)
Age, M (SD) 48.72 (13.33)
Gender
  Male 235 (45.6)
  Female 281 (54.4)
Education
  High school or less 141 (27.3)
  Some college 125 (24.1)
  Bachelor’s degree or higher 250 (48.5)
Household Income
  Less than $24,999 57 (11.0)
  $25,000 to $34,999 35 (6.7)
  $35,000 to $49,999 26 (5.0)
  $50,000 to $74,999 77(15.0)
  $75,000 to $99,999 76 (14.7)
  $100,000 to $149,999 120 (23.3)
  $150,000 and more 124 (24.1)
Race/Ethnicity
  White, Non-Hispanic 393 (76.1)
  Black, Non-Hispanic 34 (6.5)
  Hispanic 38 (7.3)
  Other, Non-Hispanic 52 (10.1)
Marital Status
  Never Married 83 (16.0)
  Divorced/Separated 59 (11.4)
  Widowed 21 (4.0)
  Living with Partner 48 (9.3)
  Married 305 (59.2)
Employment Status
  Employed 379 (73.4)
  Unemployed 70 (13.5)
  Retired 68 (13.2)
Self-Rated Health
  Excellent 73 (14.1)
  Very Good 215 (41.7)
  Good 180 (34.9)
  Fair 42 (8.2)
  Poor 6 (1.1)
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3.2 |. Sociodemographic factors and happiness

3.2.1 |. Demographic factors

Univariate ANOVA showed that happiness varied by age, F(4, 478) = 4.90, p < .001, and income, F(6, 478) = 3.74, p < .001. Pairwise comparisons indicated that happiness was significantly lower among the 35–44 age group compared with older age groups (45–54, 55–64, and 65–74). However, happiness did not show any significant variations across gender and racial/ethnic groups (see Table 2).

TABLE 2.

Global effects of sociodemographic variables on happiness.

Variables Happiness
Estimated Marginal Means Scale range: totally disagree = 0, totally agree = 10
M SE F df p
Gender
  Male 7.17 0.15
  Female 7.25 0.15 0.30 (1,478) 0.58
Age
  25–34 7.02 0.18
  35–44 6.61 0.17
  45–54 7.32 0.19
  55–64 7.36 0.20
  65–74 7.73 0.25 4.90 (4,478) 0.00
Education
  High school or less 7.24 0.18
  Some college 7.11 0.18
  Bachelor’s degree or higher 7.27 0.17 0.35 (2,478) 0.71
Household Income
  Less than $24,999 6.86 0.22
  $25,000 to $34,999 6.75 0.30
  $35,000 to $49,999 6.41 0.36
  $50,000 to $74,999 7.31 0.21
  $75,000 to $99,999 7.64 0.23
  $100,000 to $149,999 7.69 0.20
  $150,000 and more 7.80 0.22 3.74 (6,478) 0.00
Race/Ethnicity
  White, Non-Hispanic 7.12 0.10
  Black, Non-Hispanic 7.78 0.29
  Hispanic 7.01 0.29
  Other, Non-Hispanic 6.92 0.24 2.03 (3,478) 0.12
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Note: Adjustment for multiple comparisons: Sidak.

3.2.2 |. Socioeconomic factors

Happiness was significantly lower among the $35,000–$49,999 income group compared with the top three highest income groups ($75,000 to < $99,999; $100,000 to < $149,999 and > $150,000). However, happiness did not show any significant variations across education groups (see Table 2).

The univariate general linear model indicated that apart from the sociodemographic variables described above, marital status, F(4, 459) = 3.20, p < .05, and self-rated health, F(4, 459) = 31.31, p < .001, were positively associated with happiness. Compared with married, widowed individuals were less likely to be happy. With regard to health status, individuals who rated their health as “good,” “very good,” and “excellent” were more likely to be happy compared with those who rated their health as “poor.”

3.3 |. Social participation, neighborhood social cohesion, social media use, and happiness

Controlling for the five sociodemographic variables, marital status, employment status, and self-rated health, social participation, F(1, 459) = 5.12, p < .05, and neighborhood social cohesion, F(2, 459) = 9.18, p < .001, were positively associated with happiness, whereas social media use was not, F(1, 459) = 0.63, p = .43. However, there was a significant interaction effect of social media use and neighborhood social cohesion on happiness, F(2, 459) = 4.44, p < .01.

Individuals who reported low levels of social participation were less likely to be happy compared with those who reported high levels of social participation. Similarly, people who reported low and medium levels of perceived neighborhood social cohesion were less likely to be happy compared with those who reported high levels of perceived neighborhood social cohesion (see Table 3). However, the association between perceived neighborhood social cohesion and happiness differed based on social media use. Interaction analysis between perceived neighborhood social cohesion and social media use indicated that compared with people with high perceived neighborhood social cohesion, those with low perceived neighborhood social cohesion were more likely to be happy as their use of social media increased (see Figure 1).

TABLE 3.

General linear model parameter estimates for the effects of predictors on happiness

Parameter Coef. SE t-stat
Age 0.02* 0.01 2.31
Gender:
  Male 0.15 0.13 1.13
  Female (ref.)
Education:
  High school or less 0.36 0.19 1.92
  Some college 0.04 0.18 0.20
  Bachelor’s degree or higher (ref.)
Household Income:
  Less than $24,999 −0.31 0.30 −1.00
  $25,000 to $34,999 −0.48 0.33 −1.45
  $35,000 to $49,999 −1.08** 0.34 −3.18
  $50,000 to $74,999 −0.35 0.23 −1.51
  $75,000 to $99,999 −0.25 0.22 −1.13
  $100,000 to $149,999 0.02 0.18 0.08
  $150,000 and more (ref.)
Race/Ethnicity:
  White, Non-Hispanic (ref.)
  Black, Non-Hispanic 0.30 0.28 1.07
  Hispanic −0.40 0.27 −1.45
  Other, Non-Hispanic 0.09 0.22 0.42
Marital Status:
  Never Married −0.33 0.20 −1.62
  Divorced/Separated 0.25 0.21 1.19
  Widowed −0.94* 0.36 −2.60
  Living with Partner 0.14 0.25 0.54
  Married (ref.)
Employment:
  Unemployed 0.18 0.21 0.86
  Retired 0.43 0.24 1.83
  Employed (ref.)
Self-Rated Health:
  Excellent 2.61** 0.61 4.31
  Very good 2.11** 0.58 3.66
  Good 1.25* 0.58 2.14
  Fair −0.13 0.61 −0.21
  Poor (ref.)
Get-together:
  Low −0.62* 0.27 −2.26
  High (ref.)
Neighborhood Social Cohesion:
  Low −0.98** 0.24 −4.19
  Medium −0.79* 0.29 −2.70
  High (ref.)
Social media Use: −0.21* 0.08 −2.61
  Low Social Participation X Social Media Use 0.09 0.12 0.73
  High Social Participation X Social Media Use (ref.)
  Low Neighborhood Social Cohesion X Social Media 0.28* 0.10 2.82
  Medium Neighborhood Social Cohesion X Social Media 0.06 0.12 0.52
  High Neighborhood Social Cohesion X Social Media (ref.)
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*

p < .05

**

p < .01

FIGURE 1.

FIGURE 1

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Plot for the interaction between perceived neighborhood social cohesion and social media use in predicting happiness

4 |. DISCUSSION

This study assessed the association of social participation, perception of neighborhood social cohesion and social media use with happiness, and whether and how social media use interacts with social participation and perception of neighborhood social cohesion in influencing happiness. The assessment began by looking at how happiness varied by sociodemographic and other theoretically relevant factors such as health status and employment.

The findings indicated that happiness varied across age groups. Happiness was found to be low among the 35–44 age group compared with older age groups. Research on happiness–age relationship has supported two rival hypotheses (Morgan et al., 2015). The first, the positivity effect hypothesis, posits that as people become older, they tend to prefer more positive information and positive memories, and as a result, emotional wellbeing remains stable or increases across the life span (Carstensenensen et al., 2011; Morgan et al., 2015). The second hypothesis, the U-shape hypothesis, proposes a curvilinear relationship between happiness and age in which happiness dips at midlife and then rises again (Cheng et al., 2017; Morgan et al., 2015). While the age ranges that mark midlife could be slightly different from one study to another, our findings seem to support the U-shape hypothesis.

The study also showed that happiness was low among the $35,000–$49,999 income group compared with the top three income groups. This finding is generally consistent with literature showing that happiness is positively related to income up to a certain point (Howell & Howell, 2008; Jebb et al., 2018), but the observation that happiness was low among the $35,000–$49,999 income group also highlights the complex and puzzling nature of the income–happiness relationship found in past studies (Easterlin, 2001, 2003). Nevertheless, we would also like to note that the income variable in our study refers to the respondent’s annual household income and was not adjusted for household size and other factors.

It was noted that widowed people were less likely to be happy compared with married people. While this appears to be intuitive, the finding contradicts the claims of adaptation or setpoint theory proponents that life circumstances such as widowhood have virtually no lasting effect on happiness (Lucas et al., 2003). Instead, the finding seems to be supporting Easterlin’s (2003) claims that life events such as marriage and widowhood can have a lasting effect on happiness, and do not just deflect the average person temporarily below or above a setpoint determined by genetics and personality as the setpoint theorists argue. However, it should also be noted that our data did not show a significant difference in happiness between married and divorced or separated individuals and as such may not fully support the importance of life events in happiness.

Health status (self-rated health) was also positively associated with happiness. Compared with people who rated their health as “poor,” those who rated their health as “good,”“very good,” and “excellent” were more likely to be happy. This finding is consistent with previous research showing that happiness and self-rated health are not only highly correlated (Subramanian et al., 2005), but also have a bidirectional relationship (Siahpush et al., 2008) and imply that happiness should not be on the sidelines of policies and interventions that aim to promote public health.

Our data showed that social participation and perception of neighborhood social cohesion were positively associated with happiness. Individuals who reported that they frequently get-together with friends or relatives, such as going out together or visiting in each other’s homes, were more likely to be happy compared with those who reported infrequent get-together. Similarly, people with a high perception of neighborhood social cohesion were more likely to be happy compared with those with low and medium levels of perceived neighborhood social cohesion. These findings are generally consistent with past literature that has documented the link between social participation and neighborhood social cohesion with happiness. Past research has shown that neighborhood social cohesion or community and social ties more generally were positively associated with a variety of health and wellbeing outcomes such as better physical and mental health (Echeverría et al., 2008; Rios et al., 2012; Yu et al., 2019), higher preventive healthcare use (Kim & Kawachi, 2017), increased physical activity (Quinn et al., 2019), and the diffusion of available healthcare services such as mammograms (Southwell et al., 2010). The more socially cohesive neighborhood people perceive, the more they feel they have available help when needed, count on, and trust their neighbors, and are happy. In addition, neighborhood social cohesion could promote a sense of belonging which in turn is a prerequisite for happiness (Baldwin & Keefer, 2019). The sense of belonging that neighborhood social cohesion engenders could have relational and space dimensions. People may feel a sense of belonging provided by meaningful relationships with their neighbors or by the experience of rootedness associated with being in a certain place—their neighborhood (Baldwin & Keefer, 2019).

The study showed that although social media use did not have a significant main effect on happiness, it had a significant interaction effect with neighborhood social cohesion to influence happiness. Interestingly, compared with people with high perception of neighborhood social cohesion, those with low perception of neighborhood social cohesion were more likely to be happy as their social media use increased. This suggests that social media use may serve as a substitute for neighborhood social cohesion by promoting feelings of trust in and belonging to a virtual community among individuals with low perception of neighborhood social cohesion. It stands to reason that people who feel that they are not living in supportive, trustworthy, and close-knit neighborhoods are more likely to use social media platforms to reach out for friends and relatives elsewhere to make up for the lack of connection to their neighborhood. And to the extent they do so mindfully (Apaolaza et al., 2019), they are likely to gratify their needs for attachment and belonging which in turn may lead to happiness. On the contrary, people who feel that they are living in more supportive, trustworthy, and close-knit neighborhoods, are more likely to enjoy a higher sense of belonging provided by meaningful relationships with their neighbors or by the experience of rootedness in their neighborhood, which in turn may result in using social media platforms for other purposes than seeking attachment and support from friends and relatives elsewhere. It may be argued that for people whose needs for social support and sense of belonging to a community have been provided by their neighborhoods (or at least perceived as provided), social media may have little or no contribution to their happiness at best or may even be counter-productive at worst.

This study may inform the current understanding of the role of social media use in health and wellbeing. With more than three billion users worldwide in 2020 (Statista, 2018), social media use has not just become ubiquitous but also intricately embedded in our daily lives. This warrants the need for research that investigates not only the link between social media use and negative health outcomes but also the conditions and ways in which these ubiquitous platforms can lead to positive health outcomes and/or improve population health and wellbeing (Pagoto et al., 2019). Particularly in times of public health crisis such as the COVID-19 where people have to reduce inperson contacts, whether and how social media could be utilized to enhance social interactions and minimize popular concerns related to doom-scrolling and echo-chamber effects need to be investigated. In view of this, our study has brought preliminary evidence that suggests the use of these platforms may be helpful to promote wellbeing among people who perceive their neighborhoods as less supportive, trustworthy, and close-knit.

While the findings are important, their interpretation should take into account the limitations of the study. The first limitation is related to our social media use measure, which largely focused on the frequency of use. Researchers now argue that part of the reason why we find mixed, and often contradictory, findings on the link between social media use and health could be related to the way we conceptualize and measure social media use behavior (Bekalu et al., 2019; Jenkins-Guarnieri et al., 2012). In most studies, social media use is measured in terms of self-reported frequency of use or number of social media accounts or platforms that individuals opened and/or used which is referred to as “dose-effect” approach. Although such dose–effect approaches that rely on frequency and duration of media use measurement and associated effects have been widely used in conventional media effects studies, the unique features of social media, such as interactivity, “always-on,” and networked-ness, may require measurement approaches that go beyond frequency and dose (Bekalu et al., 2019). Also, consistent with previous research, our measure was created by summarizing the use of different social media platforms. However, while most platforms have common features, the specific content and activity that users engage in may differ from one platform to another. Indeed, even on the same platform, different users may have different experiences depending on the specific activities they perform and the specific content they consume. For example, experiences and health-related outcomes could vary depending on whether a person engages in active use (such as posting and commenting) or passive use (such as scrolling through profiles and posts; Frison & Eggermont, 2015). The other limitation is that although our data have been collected from a relatively large probability-based sample, the study is cross-sectional. Because all variables—independent, moderator, and outcome—have been assessed simultaneously, there is little or no evidence of temporal and causal relationships. As such, while the findings have the potential to offer preliminary evidence of associations, it is important to acknowledge the inherent predictive limitations of cross-sectional studies. Further research that takes these limitations into account is required to establish the associations observed in this study.

Acknowledgments

Funding information

National Cancer Institute, Grant/Award Number: P30 CA006516-51S6

Footnotes

PEER REVIEW

The peer review history for this article is available at https://publons.com/publon/10.1002/jcop.22469

CONFLICT OF INTERESTS

All the authors declare that there are no conflict of interests.

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