Lee Farquhar
Butler University
Theresa Davidson
Samford University
Though research has demonstrated links between Facebook use, lower self-esteem, and loneliness, less is known regarding social comparisons on Facebook. Additionally, the effects of these comparisons on general well-being outcomes need further examination. Thus, the present study examined Facebook Intensity, general Social Comparisons, and Facebook-specific comparisons as predictors of Life Satisfaction and Happiness. Using survey data from a sample of college students and Amazon’s Mechanical Turk workers, findings indicate the salience of general social comparisons, and, Facebook-specific comparisons to impact Life Satisfaction and Happiness. Facebook Intensity was not a predictor in either of our models; however, time spent on the site predicted Life Satisfaction. More broadly, social media matters in terms of one’s happiness and life satisfaction.
Facebook claimed 1.86 billion active monthly users as of the fourth quarter of 2016 (Facebook Newsroom), making it by far the most popular social network worldwide (Statista, 2015). It is clear that an enormous portion of the first-world population is on the site. Further, the saturation of Facebook into college life is evidenced by the fact that 94 percent of college students are on Facebook (Ellison, Steinfield, Lampe, 2007). The average Facebook user has 350 friends, and the average 18-24-year-old has about 650 friends (Statista, 2015). Simply put, it is very likely that a sizeable portion of an individual’s social network (family, friends, coworkers, and so on) are represented online.
However, research also shows that most people are passively watching more often than they post (Hampton, et al. 2012). This interesting phenomenon has important implications in terms of social comparison theory, which suggests that individuals are continually watching those around them in an effort to compare self to others on any number of dimensions. Facebook, it would seem, is rife with possibilities for such social comparison. The newsfeed function on Facebook, for example, can be seen as a continual stream of comparison possibilities. The function is often filled with photos and updates about active social lives, vacations, new homes, new babies, engagements, marriages, and so on. Further, these presentations are nearly always accompanied by praise from others in the “comments” section (Farquhar, 2013).
Research has regularly pointed to the benefits of Facebook use to one’s social capital (Binder et al, 2009; Burke, Kraut, and Marlow, 2011; Ellison, Steinfield, and Lampe, 2007; Steinfield, Ellison, Lampe, 2008; Valenzuela, Park, and Kee, 2009). Interestingly, though, a relatively new line of research has begun to look at negative social psychological impacts of heavy Facebook use. A number of studies have examined the potential for Facebook use to skew one’s own self image (LaRose, et al., 2011; Schwartz, 2010), narcissistic tendencies (Ryan and Xenos, 2011), loneliness (Lou, et al., 2012), and life satisfaction (Manago, Taylor, and Greenfield, 2012). It is this line of research that leads us to the present study. The purpose of the current study is to compare college students and Amazon’s MTurk Workers as the initial phase of a multi-phase project on relationships between Facebook Intensity, participants’ engagement in social comparison, and participants’ happiness, life satisfaction, and satisfaction with romantic relationships.
The tendency for individuals to engage in social comparisons, or, comparisons of oneself to the opinion, characteristics, and abilities of others, appears to be a ubiquitous phenomenon. The concept of social comparison is attributed to Festinger’s (1954) theory articulating the need for human’s to self-evaluate. However, lacking objective criteria with which to evaluate oneself, individuals must compare themselves to others. In sum, people socially compare in order to gain more information about the self.
Social comparison can have important impacts on psychological functioning in our daily lives. Thus, considerable research has investigated the effects of engaging in social comparisons, with findings that suggest complexity. Regarding psychological well-being, social comparison has been shown to be positively correlated with self-esteem and negatively correlated with depression among a group of adults with moderate intellectual disability (Dagnan and Sandhu, 1999). However, certain social comparisons, such as those involving media imagery, seem to have negative impacts on self-esteem (Morrison et al, 2004). A study by White et al. (2006) adds further complexity to the findings on social comparisons. Their research showed correlations between frequent social comparisons and a host of negative outcomes including guilt, envy, lying, and blaming others. The second part of this same study showed that among police officers, social comparisons led to more in-group bias and dissatisfaction with their jobs. There were no relationships demonstrated between social comparison and self-esteem, however.
Some studies have considered the role of social comparison for its impact on physical health outcomes, showing varied outcomes. For example, VanDerZee et al. (1998) found that cancer patients who engaged in upward comparisons with other cancer patients doing better than themselves felt better after those comparisons. However, cancer patients who scored high on measures of Neuroticism and engaged in social comparisons interpreted those comparisons more negatively. Similarly, Gilbert and Meyer’s (2003) study of young women found that social comparisons were significantly correlated with bulimic behaviors. The role of social comparison in the workplace has also been considered. Buunk et al.’s (2005) study of Spanish physicians showed that when the atmosphere at work is perceived as cooperative, physicians interpreted social comparisons as positive. However, when the workplace was not seen as cooperative, those who scored high on social comparison orientation interpreted comparisons negatively. Interestingly, Thau et al. (2007) found that among those who were high in social comparison orientation, there was a strong negative relationship between perceptions of fairness in the workplace and antisocial behaviors at work.
Though social comparison theory appears to be quite applicable to online social networks, there are surprisingly few studies that focus on social comparison in online social networks, particularly regarding Facebook. Chou and Edge (2012) showed that Facebook users who had used the site for longer (years as Facebook user) believed that others were happier and that life was less fair than those who had been on the site for fewer years. Further, Chou and Edge’s work showed that spending more time viewing others’ materials led to a belief that others were happier and had better lives.
Krasnova, et al. (2013) found that lurking exacerbated feelings of envy and that envy, in turn, decreased life satisfaction. A little over one third of their sample indicated negative emotions (boredom, frustration, anger) with their most recent Facebook session, and envy was one of the primary reasons for these negative emotions. Further, Krasnova et al. found that seeing others posts about travel, love/relationships, and professional success were envy-inducing activities on Facebook. Lastly, Krasnova et al.’s participants showed three potential actions to minimize their negative emotions: 1) avoidance, 2) unfriending, 3) engagement in even further self-promotion and impression management. Given that avoidance and unfriending go against the social norms of Facebook users, Krasnova et al. suggested that the third option - over-promotion of the self - would become the most viable option for many of their participants. However, they also warned that such enhanced self-promotion might lead to a promotion-envy spiral, which may actually worsen the negative emotions from Facebook use.
Interestingly, though comparing oneself to others at a high rate on Facebook might lead to negative effects, such activity is also tied to greater confidence in making assumptions about others, often with very little information about the other person (Andon, 2006; Farquhar, 2009). Those who frequently lurk on Facebook are even confident in predicting how a stranger will think, react, and feel about certain topics, simply from viewing the stranger’s profile page (Andon; Farquhar).
Several components come into play when trying to examine what might lead to negative social psychological effects of Facebook use, particularly negative impacts that stems from interaction with one’s own social network. A handful of studies have applied the concept called Facebook Intensity (Davidson and Farquhar, 2014; Ellison, Steinfield, Lampe, 2007; Steinfield, Ellison, Lampe, 2008; Valenzuela, Park, and Kee, 2009), which is essentially a series of items that analyze how much time is spent on Facebook, the number of Facebook Friends one has, and how important being a part of the site is to the individual. Basically, larger networks and deeper involvement equate to higher Facebook Intensity scores. Therefore, the measurement of Facebook Intensity appears to be quite applicable to the present study.
Facebook Intensity has recently been associated with negative social consequences, such as anxiety (Davidson and Farquhar, 2014; Fernandez, Levinson, and Rodebaugh, 2012). Further, those who collect more friends on Facebook show higher signs of narcissism and less conscientious than nonusers (Ryan and Xenos, 2011; Shwartz, 2010). Highly active Facebook users are also more likely to have negative opinions of academics and their social circles (Koles and Nagy, 2012). Additionally, transitioning from high school to college has been shown to be more difficult when the incoming college student has high Facebook Intensity (Kalpidou, Costin, and Morris, 2011; LaRose, et al., 2011).
Some research, however, has indicated positive social impacts of greater Facebook Intensity. Manago, Taylor, and Greenfield (2012), for example, showed that having larger networks and a larger estimated audience for any given Facebook post results in high levels of life satisfaction and perceived social support on Facebook. Further, Kalidou, Costin, and Morris (2011) indicate that while Facebook Intensity may harm adjustment to college, it becomes an effective communication tool in the later years of the college experience. The potentially mixed scholarship highlights the importance of including Facebook Intensity on the present study.
Happiness has been defined as a subjective state in which people make evaluations of their lives that are both affective and cognitive (Diener, 2000). Often referred to as “subjective well-being” (SWB) (Diener et al., 1985; Diener, 2000; Kashdan, 2004), happiness is experienced when people feel pleasant emotions, encounter few pains, and are satisfied with their lives (Diener, 2000). A clearer understanding of the determinants of happiness and well-being appears crucial as happiness impacts how individuals respond to life events (Lyobumirsky and Tucker, 1998), shapes the health of individuals and communities (Subramanian, Kim and Kawachi, 2005), and has recently become a goal of public policy (Easterlin, 2013). Following is a brief overview of the determinants of happiness in the empirical literature.
Physical health appears to be a notable predictor of happiness. Angner et al.’s (2009) study of older adults found that subjective assessments of health, along with conditions that affected daily functioning were predictive of happiness. Similarly Subramanian and colleagues (2005) demonstrated that self-rated poor health was related to lowered happiness, yet this effect was stronger at the community level than the individual level. However, a study by Abdel-Khalek (2006) found that mental health, not physical health, predicted happiness among Kuwaiti college students.
A considerable amount of literature has shown that social relationships are fundamental for happiness. In a Japanese sample, Otake and colleagues (2006) found that among happy people, the most frequent happy experiences involved close relations with family, friends, and romantic relationships. Subjective family closeness, support from family and friends, and frequency of contact with neighbors all positively predicted happiness in an African-American sample (Taylor et al., 2001). Lyubomirsky et al. (2006) found that lack of loneliness and satisfaction with friendships were related to increased happiness. Interestingly, in Chyi and Mao’s (2012) study of elderly Chinese, living with an adult child reduced happiness, but the added presence of a grandchild increased happiness. It is clear that social networks and relationships are important influences on subjective well-being.
Other factors also seem to play a role in the happiness of individuals. Mood and certain personality traits such as extraversion and introversion are related to happiness (Lyubomirsky et al., 2006). Demographic factors such as income (Angner, 2009; Taylor, et al., 2001; Subramanian, 2005), education, race, and gender (Subramanian, 2005), and marital status (Helliwell and Putnam, 2004; Taylor, et al., 2001) are all related to happiness. In general, higher levels of income and education predict higher levels of happiness. Subramanian (2005) found that Whites were happier than African-Americans. Angner (2009) found that younger individuals were less happy, but Subramanian (2005) found no age effect. Being married, however, seems to uniformly predict happiness.
The research on the determinants of happiness points to common findings in terms of health, social relationships, and other important factors. Notably, the role of social media use on happiness has yet to be fully investigated. Thus far, the evidence on use of social media shows a range of outcomes. Much of this literature indicates the importance of social capital on well-being outcomes. Social capital, or norms of reciprocity and trust among members of one’s social network, is a salient determinant of well-being and life satisfaction in the off-line world (Helliwell and Putnam, 2004). The online world, however, holds similar potential for building social capital.
Indeed, Best’s (2014) review of the literature on adolescent use of social media shows a clear trend toward enhanced well-being via the creation of social capital. Nonetheless, there is nuance among findings such as this. Ahn (2013) found that while social media use can facilitate connectedness seeking, it cannot substitute the social function of face-to-face communication for enhanced well-being. Burke et al. (2010) showed that social network users who consume greater levels of content reported reduced bridging and bonding social capital and increased loneliness. On the other hand, Valenzuela and colleagues (2009) found a positive relationship between life satisfaction and Facebook use. Interestingly, among Twitter users, happy users seek out other happy users, and likewise, unhappy users connect with other unhappy users (Bollen, 2011). Amidst this complexity, this project hopes to contribute to the question of the effect of Facebook use on users’ happiness.
Also subsumed in the category of subjective well-being is the concept of “life satisfaction”. While happiness is seen to be a subjective and positive state of emotional well-being, life satisfaction is viewed as a cognitive judgmental process that involves an evaluation of one’s general life quality (Bardo, 2010; Diener, 1985). Indeed, happiness and life satisfaction are separate and distinct construct measures. Pavot and Diener (1993) showed that the Satisfaction With Life Scale (Diener et al., 1985) is a useful psychological construct with temporal stability, use-value for clinical applications, and translates well cross-culturally. In overall evaluations of subjective well-being, it seems pertinent that researchers consider its multiple dimensions. Thus, we also consider the effect of Facebook use on life satisfaction. Following is a review of the key determinants of life satisfaction.
Analogous to happiness, social relationships and supportive networks increase life satisfaction (for a review, see Myers and Diener, 1997). Comparing very happy people with those who were of average happiness or unhappy, Diener (2002) showed that social relationships combined with little time spent alone predicted greater life satisfaction. Among those who attend religious services regularly, Lim and Putnam (2010) demonstrated that those who have close friendships with other attendees experienced higher levels of life satisfaction. Notably, church attendance alone did not predict life satisfaction. In a survey of college students (Ellison, 2007) found that less intense Facebook users who reported low satisfaction with life at their university also reported lower bridging social capital, suggesting a relationship between connections established on social media and well-being.
Cross-cultural studies of life satisfaction reveal that the determinants vary depending upon the level of economic development and cultural characteristics of a country. Using a single measure of overall life satisfaction, Diener et al. (1998) found that emotional experiences better predicted satisfaction in individualist, compared to collectivist, cultures. Delhey (2010) showed that in richer countries life satisfaction was predicted by “post-materialist” concerns such as autonomy and job creativity, rather than materialist concerns such as income. Likewise, satisfaction with safety predicted life satisfaction in poorer countries while love and esteem needs increased satisfaction in richer nations (Oishi et al., 1999).
The dearth of research on social media use and life satisfaction is clear. Social relationships clearly are key to determining both happiness and life satisfaction. In that light, we expect venues such as Facebook to hold immense potential for creating and sustaining relationships. However, while there is a growing literature examining the use of social media for its effect on happiness, there is very little on social media and life satisfaction. Given the subtle but important distinction between these concepts, we find this notable. Thus, we hope to address that gap with this study.
H1: Facebook Intensity predicts Lower Happiness
H2: Facebook Activity predicts Lower Happiness
H3: Facebook Time Spent predicts Lower Happiness
H4: Facebook Friends predicts Increased Happiness
H5: Facebook Comparisons predicts Increased Happiness
H6: Social Comparisons predicts Lower Happiness
H7: Facebook Intensity predicts Lower Life Satisfaction
H8: Facebook Activity predicts Lower Life Satisfaction
H9: Facebook Time Spent predicts Lower Life Satisfaction
H10: Facebook Friends predicts Increased Life Satisfaction
H11: Facebook Comparisons predicts Increased Life Satisfaction
H12: Social Comparisons predicts Lower Life Satisfaction
Amazon Mechanical Turk Workers. The majority of our respondents were workers on Amazon’s Mechanical Turk. Amazon’s Mechanical Turk is an online labor market in which employers post computer-based tasks and hire workers to complete these tasks. The workers, or “MTurkers” as they are generally called, can select among a variety of tasks and receive payment for completion of their work. When MTurkers log on to Amazon’s Mechanical Turk website, they are able to choose from an extensive variety of HITs. HITs contain a brief description of the work (survey, in this instance) and list the amount they will be paid for the HIT. Those who chose to continue with the survey were informed about potential risks to their anonymity and this statement included a link to Amazon’s Mechanical Turk privacy statement for workers. They were then given the option to discontinue the survey. Those who opted to complete the survey were given a URL address that linked them to Qualtrics. The pay rate for a completed survey was $.25. A total of 314 MTurkers are included in our analyses. Fifty-eight percent of MTurkers in our sample are from the U.S. and 42 percent are from outside of the U.S., mostly India.
College students. The college student portion of the sample was solicited via emails, containing a survey-linked URL address, from instructors teaching sociology and communication courses in two institutions. These included two universities in the Southeastern United States (one small, private college and one large, public college). A total of 92 college students are included in the following analyses.
There were two stages to the analyses of data. First, we describe the sample in terms of means or proportions of all key variables. Next, we conduct linear regression analyses on the two dependent variables: happiness and life satisfaction.
Table 1: Descriptive Statistics
Variable | Mean/Proportion | Minimum | Maximum |
---|---|---|---|
Happiness | 4.95 | 1.00 | 7.00 |
Life Satisfaction | 4.65 | 1.00 | 7.00 |
Age | 32.28 | 18.00 | 74.00 |
Male | .46 | ||
Social Class | 2.95 | 1.00 | 5.00 |
M-Turker | .77 | ||
FB Intensity | 2.75 | 1.00 | 5.00 |
FB Friends | 5.86 | 1.00 | 9.00 |
FB Time | 2.87 | 1.00 | 6.00 |
Total FB Activities | 3.00 | 1.00 | 5.00 |
FB Comparison | 6.29 | 1.09 | 10.00 |
Social Comparison | 3.29 | 1.40 | 5.00 |
Regarding our dependent variables (see Table 1), the average score in the Happiness measure was 4.95, on a scale ranging from 1.0 - 7.0. The average score on the Life Satisfaction measure was 4.65, also on a scale ranging from 1.0 - 7.0. This indicates that respondents report moderate to high levels of happiness and life satisfaction.
In terms of demographics, the average age of respondents is about 32 and 46 percent of respondents are male. On our social class scale (range is from 1.0 - 5.0) the average score is 2.95 which indicates that the average respondent is between upper-middle and lower-middle class by their identification. Finally, 77 percent of the sample are MTurkers with the remaining being college students.
When it comes to Facebook use and engagement, average Facebook intensity score is 2.57, indicating a low to moderate level of investment in Facebook. The category average for number of Facebook friends is 5.86 which shows that the average Facebooker has between 200 - 250 friends. Average time spent on Facebook daily shows between 30 - 60 minutes (2.87). Average total Facebook activities is 3.00 suggesting a moderate level of activity on Facebook.
Finally, in terms of social comparisons, the average Facebook comparison score of 6.29 indicates that respondents generally rate themselves fairly positively compared to others on Facebook. The average Social Comparison score of 3.29 suggests a moderate to slightly high degree of social comparisons among the average respondent.
Table 2: Linear Regression: Happiness
Variable | Beta (S.E.) |
|||
---|---|---|---|---|
Model 1: Controls | Model 2: FB Activity | Model 3: Social Comparisons | ||
Age | -.037 (.005) | -.044 (.006) | -.051 (.006) | |
Male | -.055 (.118) | -.076 (.123) | -.097 (.125)* | |
Social Class | .303 (.061)*** | .273 (.065)*** | .240 (.067)*** | |
M-Turker | -.027 (.159) | -.018 (.172) | -.032 (.174) | |
FB Intensity | -.077 (.090) | -.029 (.098) | ||
FB Friends | -.005 (.026) | .001 (.026) | ||
FB Time | -.103 (.050) | -.116 (.051)* | ||
Total FB Activities | .088 (.099) | -.040 (.106) | ||
FB Comparison | .398 (.043)*** | |||
Social Comparison | -.023 (.113) | |||
r2 | .101 | .102 | .229 |
Table 2 presents the results for the linear regression predicting Happiness. As stated previously, we conducted the regression in three stages, beginning with the demographic and control variables. In Model 1, only social class is significant such that the the higher one’s social class, the happier they are (.227***). The variance explained in this model is less than 5 percent.
Model 2 adds the Facebook engagement measures. In this model, social class retains its significance such that the higher one’s social class, the happier one is (.178***). Notably, only one of the Facebook engagement measures is predictive: total Facebook activities. Thus, the more frequently one engages in a variety of Facebook activities, the happier one is (.263***). The variance explained increases to 10 percent in this model.
Model 3 includes the social comparison measures. Both measures significantly predict happiness. Those who compare themselves favorably to others on Facebook (.496***) have higher levels of happiness. Those who engage in higher levels of Social Comparisons have lower levels of happiness (-.223***). Notably, the variance explained increases threefold to over 30 percent with the inclusion of social comparison measures. It is also notable that when comparing the standardized beta coefficients, Facebook comparisons hold the strongest predictive power in the final model.
Table 3: Linear Regression: Life Satisfaction
Table 3 presents the linear regression outcomes predicting Life Satisfaction. Model 1 shows that social class is the only significant predictor. The higher one’s social class, the higher their reported life satisfaction (.303***). The variance explained in this model is just 10 percent. Model 2 incorporates the Facebook engagement measures. Social class retains its significance and, again, the higher one’s social class, the higher the life satisfaction (.273***). No other measures are significant. In addition, the variance explained does not notably improve at just above 10 percent.
Model 3 includes the social comparison measures. In this model, gender becomes a significant predictor. Compared to females, males report lower levels of life satisfaction (-.097*). Social class retains its significance, although its predictive power is lowered slightly compared to the previous models (.240***). In this model time spent on Facebook becomes predictive such that the more time one spends on Facebook the lower the life satisfaction (-.116*). Regarding the social comparison measures, only Facebook comparisons is significant. The more positively one evaluates oneself compared to their Facebook peers, the higher the life satisfaction (.398***). This measure has the strongest predictive power in the model. In addition, the variance explained improves to nearly 30 percent in this final model.
In the full model, the biggest predictor of Happiness was Facebook-specific Comparisons. That is, when individuals positively compare themselves to other Facebook users, they have higher levels of (reported) happiness. This might be explained by downward social comparison, wherein the Facebook user would seek out others who have lesser qualities in the points of comparison that matter to the user. For instance, if the user wanted to feel better about her career, she might compare with individuals who are unemployed, at lower-status jobs, or in school. The same type of comparison could be done for virtually any other aspect of one’s life (intelligence, family life, physical attractiveness, etc).
The second biggest predictor of Happiness was general social comparison. That is, those who regularly engage in social comparisons are less happy. This complicates the previous paragraph by suggesting that it isn’t simply the amount of comparisons that generate happiness, but, rather, the specific type of comparisons (Facebook vs. offline) that can lead to happiness.
In general, our findings nuance previous scholarship that largely indicated that heavy Facebook use has a detrimental effect on one’s psychological well-being. In sum, it is not the amount of Facebook use that matters, per se, but, rather, how one feels they measure up in comparison with those around them. Further, it is also not simply the amount of social comparing one does, but, rather, the type of comparison that matters. It is also possible that Facebook’s status updates are prone to include others’ complaints about life, therefore giving increased opportunity for downward comparisons.
In all models, social class was an important predictor of Happiness. Specifically, the MTurkers in this study, who were of a lower class than the college students, had, in general, less Happiness than the students. This finding supports past research.
Again, the strongest predictor of Life Satisfaction was Facebook Comparisons. Interestingly, though, general social comparison was not a predictor for Life Satisfaction. In essence, this may mean that Facebook allows for a targeted comparison whereas general social comparison (which would include all contexts) does not allow for such nuances in making comparisons with others. Facebook, for example, would allow a user to select particular elements to use for comparison while blocking out those elements (or people) that are unwanted.
Time Spent on Facebook also predicted lower Life Satisfaction. Here, it could be argued that the user is bound to run into unfavorable social comparisons if too much time is spent perusing the social network. It might be, of course, that those with lower Life Satisfaction are prone to spend more time on Facebook. Once again, social class was found to predict Life Satisfaction. This, as with social class’ prediction of Happiness, supports past research.
Interestingly, males were generally less satisfied with life than females in our study. Other research has indicated this as well. Gender differences have frequently been found regarding social media use (Barker, 2009; Correa, Hinsley, and De Zuniga, 2010; Gross, 2004; Lenhart et al, 2010; Magnuson and Dundes, 2008; Weiser, 2000), so perhaps this finding should be expected. Typically, females have reported greater overall use of Facebook, and they also tend to use social media for communicating with peers more so than their male counterparts (Weiser). Females also report more collective self-esteem building and social identity gratifications (Barker; Magnuson and Dundes). Rather than social interaction, males tend to use social media for leisure and entertainment (Weiser).
In early analyses, the results for active use (posting status updates, photos, videos) versus passive use (lurking, checking up on friend) did not show a significant difference in this study. This finding is contrary to previous research on activity/interaction type, which regularly has shown that active participation increases self esteem and life satisfaction while lurking tends to lower self esteem and life satisfaction (Kalpidou, Costin, and Morris, 2011; Krasnova et al., 2013).
Based on our findings, there are a handful of areas that warrant further research. First, Facebook allows users to unfriend or unfollow unwanted messages. This results in selective exposure that would likely increase the user’s ability to partake in targeted social comparison. Thus, future research might account for unfriending and unfollowing activities with regard to Happiness, Life Satisfaction, and Social Comparison.
Second, more work is warranted regarding self esteem prior to Facebook sessions. Future research might look at such measures in a pretest-posttest design to better understand the role self esteem has on one’s Facebook experiences.
Third, future research should also account for experience and expertise with the technologies surrounding these comparisons. It might be that individuals with superior photography or photo editing skills are able to present better-received images on Facebook. Those lacking such skills are therefore at a disadvantage in terms of social comparison (at least regarding photos).
Lastly, future research should examine the role that the audience plays in one’s Facebook activity. It might be that some users have an active, supportive network of friends while others have less active or less supportive friends.
Abdel-Khalek AM (2006) Happiness, health, and religiosity: Significant relations. Mental Health, Religion and Culture, 9(1): 85-97
Ahn D and Shin D (2013) Is the Social Use of Media for Seeking Connectedness or for Avoiding Social Isolation? Mechanisms Underlying Media Use and Subjective WEll-Being. Computers in Human Behavior, 29, 2453-2462.
Andon SP (2006) Evaluating computer-mediated communication on the university campus: The impact of Facebook.com on the development of romantic relationships. Available at diginole.lib.fsu.edu (Accessed March 17, 2015)
Angner E, Ray MN, Saag KG and Allison, JJ (2009) Health and Happiness among OlderAdults A Community-based Study. Journal of Health Psychology, 14(4), 503-512.
Bardo AR (2010) The Comparability of Happiness and Life Satisfaction: A Life Course Approach (Doctoral dissertation, Miami University).
Barker V (2009). Older adolescents' motivations for social network site use: The influence of gender, group identity, and collective self-esteem. CyberPsychology and Behavior, 12(2), 209-213.
Bates JA and Lanza BA (2013) Conducting Psychology Student Research via the Mechanical Turk Crowdsourcing Service. North American Journal of Psychology, 15(2), 385-394.
Best P, Manktelow R and Taylor B (2014) Online communication, social media and adolescent wellbeing: a systematic narrative review. Children and Youth Services Review, 41, 27-36.
Berinsky A, Huber G and Lenz G (2012) Evaluating Online Labor Markets for Experimental Research: Amazon.com’s Mechanical Turk. Political Analysis, 20, 351-368.
Binder J, Howes A and Sutcliffe A (2009, April) The problem of conflicting social spheres: effects of network structure on experienced tension in social network sites. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 965-974). ACM.
Bollen J, Goncalves B, Ruan G and Mao H (2011) Happiness is Assortative in Online Social Networks. Artificial Life, 17, 237-251.
Buhrmester M, Kwang T and Gosling S (2011) Amazon’s Mechanical Turk: A New Source of Inexpensive, Yet High-Quality Data? Perspectives on Psychological Science, 6(1), 3-5.
Burke M, Marlow C and Lento T (2010, April) Social network activity and social well-being. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 1909-1912). ACM.
Burn SM and Ward AZ (2005) Men's Conformity to Traditional Masculinity and Relationship Satisfaction. Psychology of Men and Masculinity 6(4): 254.
Chou HTG and Edge N (2012) They are happier and having better lives than I am”: the impact of using Facebook on perceptions of others' lives. Cyberpsychology, Behavior, and Social Networking, 15(2), 117-121.
Chyi H and Mao S (2012) The determinants of happiness of China’s elderly population. Journal of Happiness Studies, 13(1), 167-185.
Davidson T and Farquhar LK (2014) Correlates of Social Anxiety, Religion, and Facebook. Journal of Media and Religion, 13(4), 208-225.
Dagnan D and Sandhu S (1999) Social Comparison, Self-Esteem, and Depression in People with Intellectual Disability. Journal of Intellectual Disability Research, 43(5), 372-9.
Delhey J (2010) From materialist to post-materialist happiness? National affluence and determinants of life satisfaction in cross-national perspective. Social Indicators Research, 97(1), 65-84.
Diener E (2000) Subjective well-being: The science of happiness and a proposal for a national index. American psychologist, 55(1), 34.
Diener ED, Emmons RA, Larsen RJ and Griffin S (1985) The satisfaction with life scale. Journal of personality assessment, 49(1), 71-75.
Easterlin RA (2013) Happiness, Growth, and Public Policy. Economic Inquiry, 51(1), 1-15.
Ellison NB (2007) Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13(1), 210-230.
Ellison NB, Steinfield C and Lampe C (2007) The benefits of Facebook “friends:” Social capital and college students’ use of online social network sites. Journal of Computer-Mediated Communication, 12(4), 1143-1168.
Elphinston RA and Noller P (2011) Time to face it! Facebook intrusion and the implications for romantic jealousy and relationship satisfaction. Cyberpsychology, Behavior, and Social Networking, 14(11), 631-635.
Facebook Newsroom (2016) Number of Active Monthly Users. Available at http://newsroom.fb.com/company-info/ (Accessed on March 31, 2016).
Farquhar L (2013) Performing and interpreting identity through Facebook imagery. Convergence: The International Journal of Research into New Media Technologies, 19(4), 446-471.
Farquhar LK (2009) Identity negotiation on Facebook. com. Theses and Dissertations, 289. Festinger L (1954) A Theory of Social Comparison Processes. Human Relations, 117-140.
Gonzales AL and Hancock JT (2011) Mirror, mirror on my Facebook wall: Effects of exposure to Facebook on self-esteem. Cyberpsychology, Behavior, and Social Networking, 14(1-2), 79-83.
Goodman JK, Cryder CE and Cheema A (2012) Data Collection in a Flat World: The Strengths and Weaknesses of Mechanical Turk Samples. Journal of Behavioral Decision Making.
Gove WR, Hughes M and Style CB (1983) "Does marriage have positive effects on the psychological well-being of the individual?." Journal of health and social behavior, 122-131.
Gross EF (2004) Adolescent Internet use: What we expect, what teens report. Journal of Applied Developmental Psychology, 25(6), 633-649.
Hampton KN, Goulet LS, Marlow C and Rainie L (2012) Why most Facebook users get more than they give. Pew Internet and American Life Project, 3.
Helliwell JF and Putnam RD (2004) The social context of well-being. Philosophical transactions-royal society of London series B biological sciences, 1435-1446.
Ipeirotis P (2010) Demographics of mechanical turk. CeDERWorking Papers, http://hdl.handle.net/2451/29585, March. CeDER-10-01.
Kalpidou M, Costin D and Morris J (2011) The relationship between Facebook and the well-being of undergraduate college students. Cyberpsychology, Behavior, and Social Networking, 14(4), 183-189.
Kapidzic S (2013) Narcissism as a predictor of motivations behind Facebook profile picture selection. Cyberpsychology, Behavior, and Social Networking, 16(1), 14-19.
Kashdan TB (2004) The assessment of subjective well-being (issues raised by the Oxford Happiness Questionnaire). Personality and individual differences, 36(5), 1225-1232.
Kim J and Lee JER (2011) The Facebook paths to happiness: Effects of the number of Facebook friends and self-presentation on subjective well-being. Cyberpsychology, Behavior, and Social Networking, 14(6), 359-364.
Koles B and Nagy P (2012) Facebook usage patterns and school attitudes.Multicultural Education and Technology Journal, 6(1), 4-17.
Krasnova H, Wenninger H, Widjaja T and Buxmann P (2013) Envy on Facebook: A Hidden Threat to Users' Life Satisfaction?. Wirtschaftsinformatik, 92.
LaRose R, Wohn DY, Ellison N and Steinfield C (2011) Facebook fiends: Compulsive social networking and adjustment to college. Proceedings of the international association for the development of the information society.
Lenhart A, Purcell K, Smith A and Zickuhr K (2010) Social media and young adults. Pew Internet and American Life Project, 3.
Lim C and Putnam RD (2010) Religion, social networks, and life satisfaction. American Sociological Review, 75(6), 914-933.
Lou LL, Yan Z, Nickerson A and McMorris R (2012) An examination of the reciprocal relationship of loneliness and Facebook use among first-year college students. Journal of Educational Computing Research, 46(1), 105-117.
Lyubomirsky S and Tucker KL (1998) Implications of individual differences in subjective happiness for perceiving, interpreting, and thinking about life events. Motivation and emotion, 22(2), 155-186.
Magnuson MJ and Dundes L (2008) Gender differences in “social portraits” reflected in MySpace profiles. CyberPsychology and Behavior, 11(2), 239-241.
Mason W and Suri S (2012) Conducting Behavioral Research on Amazon’s Mechanical Turk. Behavioral Research, 44, 1-23.
Mod GBB (2010) Reading romance: The impact Facebook rituals can have on a romantic relationship. Journal of Comparative Research in Anthropology and Sociology, (2), 61-77.
Morrison T, Kalin R and Morrison M (2004) Body-Image Evaluation and Body-Image Investment Among Adolescents: A Test of Sociocultural and Social Comparison Theories. Adolescence, 39(155), 571-572.
MTurk.com (2015) Available at https://www.mturk.com/mturk/help?helpPage=worker (Accessed on March 27, 2015)
Myers DG and Diener E (1997) The pursuit of happiness. Scientific American, 7, 40-43.
Oishi S, Diener EF, Lucas RE and Suh EM (1999) Cross-cultural variations in predictors of life satisfaction: Perspectives from needs and values. Personality and social psychology bulletin, 25(8), 980-990.
Otake K, Shimai S, Tanaka-Matsumi J, Otsui K and Fredrickson BL (2006) Happy people become happier through kindness: A counting kindnesses intervention. Journal of Happiness Studies, 7(3), 361-375.
Paolacci G and Chandler J (2014) Inside the Turk: Understanding Mechanical Turk as a Participant Pool. Current Directions in Psychological Science, 23(3), 184-188.
Papp L M, Danielewicz J and Cayemberg C (2012) Are we Facebook official? Implications of dating partners' Facebook use and profiles for intimate relationship satisfaction. Cyberpsychology, Behavior, and Social Networking, 15(2), 85-90.
Pavot W and Diener E (1993) Review of the satisfaction with life scale. Psychological assessment, 5(2), 164.
Panek ET, Nardis Y and Konrath S (2013) Mirror or Megaphone?: How relationships between narcissism and social networking site use differ on Facebook and Twitter. Computers in Human Behavior, 29(5), 2004-2012.
Preece J, Nonnecke B and Andrews D (2004) The top five reasons for lurking: improving community experiences for everyone. Computers in human behavior, 20(2), 201-223.
Rand DG (2011) The Promise of Mechanical Turk: How Online Labor Markets can Help Theorists Run Behavioral Experiments. Journal of Theoretical Biology, 299, 172-179.
Ryan T and Xenos S (2011) Who uses Facebook? An investigation into the relationship between the Big Five, shyness, narcissism, loneliness, and Facebook usage. Computers in Human Behavior, 27(5), 1658-1664.
Schwartz M (2010) The usage of Facebook as it relates to narcissism, self-esteem and loneliness. Retrieved March 17, 2015 from http://digitalcommons.pace.edu/dissertations/AAI3415681/
Statista (2015) Average number of Facebook friends of U.S. users in 2014, by age group. Available at http://www.statista.com/statistics/232499/americans-who-use-social-networking-sites-several-times-per-day/ (Accessed on March 17, 2015).
Steinfield C, Ellison NB and Lampe C (2008) Social capital, self-esteem, and use of online social network sites: A longitudinal analysis. Journal of Applied Developmental Psychology, 29(6), 434-445.
Subramanian SV, Kim D and Kawachi I (2005) Covariation in the socioeconomic determinants of self rated health and happiness: a multivariate multilevel analysis of individuals and communities in the USA. Journal of Epidemiology and Community Health, 59(8), 664-669.
Taylor RJ, Chatters LM, Hardison CB and Riley A (2001) Informal social support networks and subjective well-being among African Americans. Journal of Black Psychology, 27(4), 439-463.
Tazghini S and Siedlecki KL (2013) A mixed method approach to examining Facebook use and its relationship to self-esteem. Computers in Human Behavior, 29(3), 827-832.
Valenzuela S, Park N and Kee KF (2009) Is there social capital in a social network site?: Facebook use and college students' life satisfaction, trust, and participation. Journal of Computer-Mediated Communication, 14(4), 875-901.
Vitak J (2012) The impact of context collapse and privacy on social network site disclosures. Journal of Broadcasting and Electronic Media, 56(4), 451-470.
Weiser EB (2000) Gender differences in Internet use patterns and Internet application preferences: A two-sample comparison. CyberPsychology and Behavior, 3(2), 167-178.
Lee Farquhar is a professor at Butler University. Theresa Davidson is a professor at Samford University.