The Dual Nature of Social Media: Examining the Direction of Causal Flow Between Fear of Missing Out and Social Media Use

Abstract

The dual (bidirectional) nature of social media suggests that fear-of-missing-out (FOMO) leads to greater social media use (SMU). In turn, higher levels of SMU lead to heightened FOMO. Ironically, individuals use social media to assuage their FOMO but end up with higher levels of FOMO after being exposed to a wide variety of social opportunities, where they may not have been included. The present research examines the hypothesized bidirectional causal flow between FOMO and SMU. Extant research involving FOMO has been largely correlational. In Study 1, FOMO was manipulated and found to increase reported levels of SMU. Study 2 manipulated SMU, which led to higher levels of FOMO. It appears that, regarding FOMO, social media does exhibit a dual (bidirectional) nature.

Introduction

As a popular social media app, Snapchat's Location Sharing Snap Map is an excellent example of the bidirectional nature of social media. Snap Map allows its 300 million global users to track the physical location of others in real time. Many teens feel they would be social outcasts if they did not use Snap Map.¹ An individual's fear-of-missing-out (FOMO) finds a convenient outlet on Snap Map. Users can check to see where their friends are and often what they are doing. If a group of friends is attending a party or concert without the user, however, feelings of FOMO may be heightened.

FOMO is defined as “a pervasive apprehension that others might be having rewarding experiences from which one is absent…” (p.1841).² A 10-item self-report measure of FOMO developed by Przybylski et al. has been used by most of the research involving the FOMO construct.³ A more recent study created, using the original 10 items of the FOMO scale and six items intended to measure state-FOMO in the online context, a second measure of FOMO.⁴ This was an important addition to the FOMO literature. Wegmann et al.'s 12-item scale⁴ revealed that Przybylski's scale² measures FOMO as a trait. A state measure is more suitable for the present study's experimental design.

To date, research involving FOMO has been almost exclusively correlational in nature.³ Extant research has found that FOMO and social media use (SMU) are correlated^3,5–7 In a comprehensive review of the literature regarding the relationship between FOMO and SMU, Tandon et al. conclude FOMO is an “emergent aspect of the dark side of social media” (p. 783). FOMO, across many studies was found to be associated with problematic SMU.^7–10 However, no experimental research has been conducted to establish the direction of causal flow between FOMO and SMU, despite numerous calls for such research.^3,9,11 The present research seeks to address this gap in the literature by providing an experimental examination of the direction and causality between FOMO and SMU. We predict that FOMO increases SMU but, FOMO in turn, is also impacted by SMU. In essence, we expect a continuous feedback loop exists between FOMO and SMU.

The impact of FOMO on SMU

In a systematic review of the FOMO and SMU literature, Tandon et al. found that only three percent of studies involving FOMO were experimental by design, and none of these studies manipulated FOMO.³ Thus, our first study (presented below) was designed to systematically investigate the causal effect of FOMO on SMU using an experimental induction of FOMO. In doing so, our research fills an important gap in the literature surrounding FOMO.

Humans have an innate drive to be in relationship with others.¹² Several theories exist that might explain why FOMO leads to greater SMU. Roberts and David⁷ used the Belonging Hypothesis¹² and Information Foraging Theory¹³ to explain how FOMO increases SMU. The Belonging Hypothesis argues that “…human beings have a pervasive drive to form and maintain at least a minimum quantity of lasting, positive, and significant interpersonal relationships”¹² (p. 497). Relatedly, Information Foraging Theory explains that, like animals foraging for food, “…humans are constantly seeking information - particularly regarding their relationships with others”⁷ (p. 387). Social media serves as a convenient resource for information on the activities and personal information of others. Thus, it can be argued that FOMO drives SMU to gauge social acceptance.

Sociometer Theory argues that self-esteem is “Part of a psychological system (the sociometer) that monitors the social environment for cues indicating low or declining relational evaluation (e.g., lack of interest, disapproval, rejection) and warns the individual when such cues are detected”¹⁴ (p. 75). Self-esteem is viewed as a function of social feedback.¹⁵ Leary argues that the sociometer developed in humans to monitor and respond to environmental cues that might suggest social exclusion and/or a lowering of relational value.⁵ Such social acceptance is as vital today as it was hundreds, if not thousands of years ago. Relational success is critical to human survival. Social media allows for surveillance of such social cues 24 hours a day, across a broad spectrum of one's relationships.

The impact of SMU on FOMO

Probably the best argument for a causal flow from SMU to FOMO was offered by Przybylski et al.² These authors argued that social media provides a wide spectrum of social opportunities—too many for any one individual to pursue. Not being able to participate in all the activities displayed on social media will likely heighten a social media user's FOMO.

It is hypothesized FOMO leads to SMU, but SMU also triggers FOMO. This assumption is consistent with Roberts and David⁷ and Buglass et al.¹¹ who argued that a bidirectional causal flow likely exists between FOMO and SMU and merits further research scrutiny. In a large survey of UK Facebook users, Buglass et al. found that individuals who reported higher levels of SMU also exhibited higher levels of FOMO. The authors posit that this may be because the use of social media “promotes social surveillance”¹¹ (p. 254). A survey of 207 college students found SMU increased reported levels of FOMO.⁵ Hunt et al. found that restricting the use of Facebook, Instagram, and Snapchat among undergraduate college students decreased reported levels of FOMO.¹⁶

Given the proposed dual nature of social media² and the lack of experimental studies in this area, two experiments were conducted to assess the causal flow between FOMO and SMU. Study 1 manipulates FOMO, while study 2 manipulates SMU.

Study 1

Participants and procedure

Two hundred and fifty U.S. adults were recruited through TurkPrime to participate in the study. See Table 1 for sample characteristics. A 2 × 1 design was used whereby FOMO was manipulated through a scenario adapted from Hayran et al.¹⁷ and used by Bui et al.¹⁸ and David and Roberts¹⁹ in which participants were asked to consider that they are spending a Friday evening at home watching a movie. Sample size was determined based on similar studies in the existing literature, which have used the same FOMO manipulation as employed herein (cf. Bui et al., study 1; Dhand and Khatkar).^18,20

Table 1.

Demographic Profile for the Sample in Study 1

Demographic characteristic	Study 1
Demographic characteristic	n	%
Gender
Female	106	43
Male	143	57
Income
<$20,000	17	7
$20,000–$39,999	33	17
$40,000–$49,999	40	16
$50,000–$74,999	75	30
$75,000–$99,999	52	21
$100,000–$149,999	16	6
$150,000 or more	6	2
Ethnicity
White/Caucasian	184	73
Asian/Pacific Islander	11	4
Hispanic	10	4
African American	37	15
Native American	6	2
Education
High school/GED	24	10
Some college	52	21
College degree	99	39
Masters/doctoral degree	74	30
Age
Mean (range)	37	(22–76)

GED, general educational development.

Participants were randomly assigned to either a treatment or control scenario (see Appendix A1 for the complete scenarios) and were then given a description of a situation and were asked to imagine themselves in the situation described. Participants in both conditions were asked to consider that they are spending a Friday evening at home watching a movie.^19,20 Participants in the treatment group were asked to consider the alternative activities and experiences that could be going on this evening, and to list and describe several things that they could be missing out on as they spend the evening at home. These participants were then asked to imagine that, “as you're spending the Friday evening at home, you strongly feel that you're missing out on other activities and experiences going on this evening. Specifically, you're fearful that you're missing out on other fun and rewarding experiences and activities.” Participants in the control group were asked to consider the different types of movies and snacks that they could have for this evening.

These participants were then asked to imagine that, “as you are spending Friday evening at home watching movies you decide it was a good decision to stay home. Specifically, you're not fearful that you're missing out on other fun and rewarding experiences and activities, and instead you're content staying at home watching a movie.”

SMU was assessed by asking participants to indicate how much time they would spend on social media during the evening described. Specifically, participants moved a slider bar to estimate how many minutes they would spend browsing or engaging on social media (including time on Facebook, Twitter, Snapchat, YouTube, Pinterest, LinkedIn, Instagram, TikTok, Tumblr, Vine, Reddit, or other social media platforms) (mean [M] = 66.04, standard deviation [SD] = 44.52). Participants were also asked to indicate how many hours they would likely spend browsing or engaging on social media²¹ during the respective weekend described (M = 4.64, SD = 3.93).

As a manipulation check, participants were asked, “As you were imagining yourself in the situation presented, to what extent did you feel like you were missing out on other alternative activities and experiences that were taking place elsewhere.”¹⁹ A 5-point scale was used (1 = Not at all, 5 = Very much). Additionally, participants' current mood was measured using two 5-point bipolar items (i.e., negative–positive, bad–good, r = 0.78, p < 0.001) (M = 3.81, SD = 0.98). Table 1 shows the demographic profile of the sample.

Results

The manipulation worked as expected; participants in the FOMO (vs. control) condition reported experiencing higher levels of FOMO (M_control = 2.04, M_FOMO = 3.37, t₍₂₄₈₎ = −8.91, p < 0.05). Additionally, the manipulation did not impact mood (M_control = 3.98, M_FOMO = 3.64, t₍₂₄₈₎ = 1.33, p > 0.10).

Next, a t-test was conducted to test the prediction that FOMO increases SMU. Results reveal that participants in the FOMO (vs. control) condition reported that they would spend more time on social media during the evening described (M_control = 56.50 minutes, M_FOMO = 75.14 minutes, t₍₂₄₈₎ = −3.38, p < 0.05) and during that respective weekend (M_control = 3.91 hours, M_FOMO = 5.40 hours, t₍₂₄₈₎ = −2.56, p < 0.05). Thus, findings from this study suggest that FOMO increases SMU.

Study 2

Participants and procedure

Study 2 (n = 108) was designed to assess whether SMU leads to FOMO. Participants were undergraduates at the authors' institution who were recruited through a marketing research subject pool and given partial course credit for their participation. See Table 2 for sample characteristics. A 2 × 1 design was used whereby SMU was manipulated through a scenario used by Alfasi in which participants were randomly assigned to either a control condition or a condition in which they completed a 15-minute task involving social media.²²

Table 2.

Demographic Profile for the Sample in Study 2

Demographic characteristic	Study 2
Demographic characteristic	n	%
Gender
Female	57	53
Male	51	47
Ethnicity
White/Caucasian	87	80
Asian/Pacific Islander	6	6
Hispanic	11	10
African American	4	4
Age
Mean (range)	21	(18–23)

The sample size was decided upon based on similar studies in the existing literature, which have used the same SMU manipulation as employed herein.^20,22 Similar to the procedure used by Alfasi, participants in the treatment condition were asked to spend the next 15 minutes surfing their social media, whereas, participants in the control group were asked to spend the next 15 minutes surfing their university's website. This SMU manipulation was presented at the beginning of the study (see Appendix A2 for the full scenarios).

FOMO was assessed (α = 0.81, M = 2.32, SD = 0.78) using an established 10-item FOMO scale, which has also been used and validated by others.^2,4,21 Example items include “I fear my friends have more rewarding experiences than me,” and “I get anxious when I don't know what my friends are up to.” A 5-point scale was used (1 = Not at all true of me to 5 = Extremely true of me).

As a manipulation check, participants reported the number of minutes spent on social media during the task at the beginning of the study (M = 6.44, SD = 7.38). Mood was measured using the same two bipolar items as in study 1 (i.e., negative–positive, bad–good, r = 0.81, p < 0.001) (M = 3.82, SD = 0.91). Table 2 shows the demographic profile of the sample.

Results

The manipulation worked as expected. Participants in the SMU (vs. control) condition reported spending more minutes on social media during the given task (M_control = 0.12, M_SMU = 14.46, t₍₁₀₆₎ = −55.86, p < 0.001). Additionally, the manipulation did not impact mood (M_control = 3.95, M_SMU = 3.80, t₍₁₀₆₎ = 0.82, p = 0.41).

Next, a t-test was conducted to test the prediction that SMU increases FOMO. Results reveal that participants in the SMU (vs. control) condition reported experiencing greater FOMO (M_control = 2.15, M_SMU = 2.46, t₍₁₀₆₎ = −2.05, p < 0.05). Thus, findings from this study suggest that SMU leads to FOMO.

Discussion

The primary objective of the present research was to assess the direction of causality between FOMO and SMU. Although a large body of research exists concerning FOMO, it is almost entirely correlational.³ In Study 1, FOMO was manipulated to determine whether it increases SMU. Those in the treatment (vs. control) group spent on average 19 minutes longer on social media, thus showing that FOMO leads to more time on social media. Study 2 experimentally induced SMU and found that it increased FOMO compared with the control condition. It appears that, when exposed to a nearly endless array of social opportunities that no individual could take advantage of, a sense of missing out is heightened.

Overall, and consistent with Przybylski et al.'s² thinking on the dual nature of social media, our findings suggest that FOMO increases SMU but, in turn, is also impacted by SMU. Thus, it appears that a continuous feedback loop exists between FOMO and SMU.

Although the present research is the first to investigate the direction of causal flow between FOMO and SMU, its results must be tempered by certain limitations. To begin, the samples used may limit the generalizability of the findings; Study 1 used an adult sample whereas Study 2 used a student sample. A second possible limitation/opportunity for future research stems from how FOMO and SMU were manipulated; rather than manipulating high/low levels, a simple treatment versus control condition was used in both studies. More causal and longitudinal research is needed to address this and other related issues. Another potential limitation relates to the measures used. Future studies could incorporate different types of FOMO as research has shown that their effects can differ depending on temporal nature.¹⁷ Relatedly, future research should consider alternative measures of SMU such as active versus passive use, since this may impact the outcomes elicited by SMU.⁷

Footnotes

Author Disclosure Statement

No competing financial interests exist.

Funding Information

No funding was received for this article.

Appendix

References

Jargon

Why teens say location sharing is the greatest—And the worst. Wall Street J 2023. Available from: https://www.wsj.com/articles/location-sharing-the-agony-and-ecstasy-of-knowing-where-all-your-friends-are-cf3e583c [Last accessed: May 16, 2023].

Przybylski

, Murayama

, DeHaan

, et al. Motivational, emotional, and behavioral correlates of fear of missing out. Comput Human Behav, 2013; 29:1841–1848.

Tandon

, Dhir

, Almugren

, et al. Fear of missing out (FOMO) among social media users: A systematic literature review, synthesis and framework for future research. Intern Res, 2021; 31(3):782–821.

Wegmann

, Oberst

, Stodt

, et al. Online-specific fear of missing out and Internet-use expectancies contribute to symptoms of Internet-communication disorder. Addict Behav Rep, 2017; 5:33–42.

Blackwell

, Leaman

, Tramposch

, et al. Extraversion, neuroticism, attachment style and fear of missing out as predictors of social media use and addiction. Pers Individ Diff, 2017; 116:69–72.

Oberst

, Wegmann

, Stodt

, et al. Negative consequences from heavy social networking in adolescents: The mediating role of fear of missing out. J Adolesc, 2017; 55:51–60.

Roberts

, David

. The social media party: Fear of missing out, social media intensity, connection, and well-being. Int J Human Comput Interact, 2020; 36(4):386–392; doi: 10.1080/10447318.2019.1646517

Alt

Students' social media engagement and fear of missing out (FoMO) in a diverse classroom. J Comput Higher Educ, 2017; 29:388–410.

Roberts

, Petnji YaYa

, Manolis

The invisible addiction: Cell-phone activities and addiction among male and female college students. J Behav Addict, 2014; 3(4), 254–265.

10.

Wolniewicz

, Tiamiyu

, Weeks

, et al. Problematic smartphone use and relations with negative affect, fear of missing out, and fear of negative and positive evaluation. Psychiatry Res, 2018; 262:618–623.

11.

Buglass

, Binder

, Bette

, et al. Motivators of online vulnerability: The impact of social network site use and FoMO. Comput Human Behav, 2017; 66:248–255.

12.

Baumeister

, Leary

. The need to belong: Desire for interpersonal attachments as a fundamental drive. Psychol Bull, 1995; 117(3):497–529.

13.

Muntinga

, Taylor

. Information seeking strategies in medicine queries: A clinical eye-tracking study with gaze-cued retrospective think-aloud protocol. Int J Human Comput Interact, 2018; 34(48); doi: 10.1080/10447318.2017.1368949

14.

Leary

MR.

Sociometer theory and the pursuit of relational value: Getting to the root of self-esteem. Eur Rev Soc Psychol, 2005; 16:75–111; doi: 10.1080/10463280540000007

15.

Back

, Krause

, Hirschmuller

, et al. Unraveling the three faces of self-esteem: A new information processing sociometer perspective. J Res Pers, 2009; 43:933–937; doi: 10.1016/j.jrp.2009.002

16.

Hunt

, Lipson

, Young

. No more FOMO: Limiting social media decreases loneliness and depression. J Soc Clin Psychol, 2018; 37(10):751–768; doi: 10.1521/jscp.2018.37.10.751

17.

Hayran

, Gürhan-Canli

. Understanding the feeling of missing out: A temporal perspective. Pers Individ Differ, 2022; 185:111307.

18.

Bui

, Krishen

, Anlamlier

, et al. Fear of missing out in the digital age: The role of social media satisfaction and advertising engagement. Psychol Market, 2022; 39(4):683–693.

19.

David

, Roberts

. Me, myself, and I: Self-centeredness, FOMO, and social media use. Can J Behav Sci, 2023 [Advance online publication]; doi: 10.1037/cbs0000382

20.

Dhand

, Khatkar

. Statulator: An online statistical calculator. Sample Size Calculator for Comparing Two Independent Means. 2014. Available from: http://statulator.com/SampleSize/ss2M.html [Last accessed: October 15, 2023].

21.

, Griffiths

, Niu

, et al. The trait-state fear of missing out scale: Validity, reliability, and measurement invariance in a Chinese sample of university students. J Affect Disord, 2020; 274:711–718.

22.

Alfasi

The grass is always greener on my Friends' profiles: The effect of Facebook social comparison on state self-esteem and depression. Pers Individ Diff, 2019; 147:111–117.