Social Status and Social Media Use Motivations

Examinations of the relationship between perceived social status and motivations for social media use and abstinence

Author

Affiliation

Gregory Hurford

Binghamton University

Abstract

Political polarization has become an increasingly relevant topic in American society, and a primary vessel for political content is social media. This study examines how politically polarizing content on social media engages with users’ zero-sum beliefs to impact their mental health. Many previous studies have developed the idea that high levels of social media use have negative impacts on users’ mental health, specifically through anxiety and mental distress (O’Reilly, 2020). This study utilizes a survey built via Qualtrics to ask participants questions relating to political content, media literacy, zero-sum beliefs, and motivations for social media use. Participants were specifically undergraduate college students above the age of eighteen. A sample of 101 valid responses were collected, and data was screened before analysis took place. The main hypothesis tested here concerns how users are motivated to start and stop using social media based on their perceived social status. Data was visualized and it was found that there is no substantial correlation between a social media user’s perceived social status and what motivations they have to start and stop using social media. Further research can be conducted related to other sociodemographics, or to see what other impact social status may have when related to social media beyond its use. This research can be broadened to the field when dealing with social media related mental distress or addiction, as it shows a construct that would not have much of an effect when considered for use in an intervention.

Keywords

social media, sociodemographics, motivations, addiction, social status

1 Results

1.1 Load

# installation of packages to be used
install.packages("readxl")
install.packages("dplyr")
install.packages("ggplot2")
install.packages("stats")

# adding libraries of the added packages
library(readxl)
library(dplyr)
library(ggplot2)
library(stats)

1.2 Import

# source: https://readxl.tidyverse.org/
# note: import data from excel file .xlsx
alldata <- read_excel("10.20.2025.QualtricsData.Team3.Clean2.xlsx")

1.3 Transform

# source: https://r4ds.hadley.nz/workflow-style.html#sec-pipes
# note: selects variables from the alldata set and creates a dataframe called selectdata
library(dplyr)
selectdata <- alldata %>%
  select(AGE, HEALTHSTATUS, GENDER, STATUS, MOTIVESTART1, MOTIVESTART2, MOTIVESTART3, MOTIVESTART4, MOTIVESTART5, MOTIVESTART6, MOTIVEEND1, MOTIVEEND2, MOTIVEEND3, MOTIVEEND4, MOTIVEEND5, MOTIVEEND6)

1.4 Model

1.4.1 Social Status

# sources: https://r4ds.hadley.nz/data-visualize.html#creating-a-ggplot, https://r4ds.hadley.nz/layers.html#facets, and https://ggplot2.tidyverse.org/reference/scale_continuous.html
# note: creates histograms from variables in selectdata

status_plot <- ggplot(
  data = selectdata,
  mapping = aes(x = STATUS) 
) +
  geom_histogram(binwidth = 1) + scale_x_continuous(limits = c(1, 10))

status_plot + labs (x = "Participants' perceived social status on a scale of 1-10 (ladder method)")

The models below depict the results of plotting respondents’ perceived social status against all surveyed motivations for social media use. Motivations listed for starting a social media session were: Social Connection (Figure 1), News (Figure 2), Fear of Missing Out (FOMO) (Figure 3), Boredom (Figure 4), Sharing Life Events (Figure 5), and Viewing Agreeable Opinionated Content (Figure 6). Motivations listed for ending a social media session were: Current Events (Figure 7), Viewing Disagreeable Opinionated Content (Figure 8), Fear of Addiction (Figure 9), Desire for Socialization (Figure 10), Boredom (Figure 11), and Viewing Disturbing Content (Figure 12).

1.4.2 Motivations to Start Using Social Media

# source: https://shanemccarty.github.io/FRIplaybook/ggplot2.html
# note: creates plots relating perceived social status to various motivations participants cited for starting social media use

plot1 <- ggplot(
  data = selectdata,
  mapping = aes(x = STATUS, y = MOTIVESTART1)
) + geom_point(position = "jitter", color = "red") + geom_smooth(method = "lm", color = "red") + xlim(0, 10) + ylim(0, 5) +
  ggtitle("Perceived Social Status vs. Social Connection related Motivations") +
  xlab("Perceived Social Status (Lowest to Highest)") +
  ylab("Motivation related to Socialization (Least to Most Impact)") + theme_bw()

plot1

Figure 1 - Participants’ perceived social status and their motivations to use social media based on a desire for social connection.

ggsave("plots/plot1_socialconnection.png", 
       plot = plot1,
       width = 10, height = 8, dpi = 300)

# source: https://shanemccarty.github.io/FRIplaybook/ggplot2.html
# note: creates plots relating perceived social status to various motivations participants cited for starting social media use

plot2 <- ggplot(
  data = selectdata,
  mapping = aes(x = STATUS, y = MOTIVESTART2)
) + geom_point(position = "jitter", color = "orange") + geom_smooth(method = "lm", color = "orange") + xlim(0, 10) + ylim(0, 5) +
  ggtitle("Perceived Social Status vs. News related Motivations") +
  xlab("Perceived Social Status (Lowest to Highest)") +
  ylab("Motivation related to News (Least to Most Impact)") + theme_bw()

plot2

Figure 2 - Participants’ perceived social status and their motivations to use social media based on a desire for obtaining news.

ggsave("plots/plot2_news.png", 
       plot = plot2,
       width = 10, height = 8, dpi = 300)

# source: https://shanemccarty.github.io/FRIplaybook/ggplot2.html
# note: creates plots relating perceived social status to various motivations participants cited for starting social media use

plot3 <- ggplot(
  data = selectdata,
  mapping = aes(x = STATUS, y = MOTIVESTART3)
) + geom_point(position = "jitter", color = "yellow") + geom_smooth(method = "lm", color = "yellow") + xlim(0, 10) + ylim(0, 5) +
  ggtitle("Perceived Social Status vs. Fear of Missing Out related Motivations") +
  xlab("Perceived Social Status (Lowest to Highest)") +
  ylab("Motivation related to a FOMO (Least to Most Impact)") + theme_bw()

plot3

Figure 3 - Participants’ perceived social status and their motivations to use social media based on a fear of missing out (FOMO).

ggsave("plots/plot3_fomo.png", 
       plot = plot3,
       width = 10, height = 8, dpi = 300)

# source: https://shanemccarty.github.io/FRIplaybook/ggplot2.html
# note: creates plots relating perceived social status to various motivations participants cited for starting social media use

plot4 <- ggplot(
  data = selectdata,
  mapping = aes(x = STATUS, y = MOTIVESTART4)
) + geom_point(position = "jitter", color = "green") + geom_smooth(method = "lm", color = "green") + xlim(0, 10) + ylim(0, 5) +
  ggtitle("Perceived Social Status vs. Boredom related Motivations") +
  xlab("Perceived Social Status (Lowest to Highest)") +
  ylab("Motivation related to Boredom (Least to Most Impact)") + theme_bw()

plot4

Figure 4 - Participants’ perceived social status and their motivations to use social media based on boredom.

ggsave("plots/plot4_boredom1.png", 
       plot = plot4,
       width = 10, height = 8, dpi = 300)

# source: https://shanemccarty.github.io/FRIplaybook/ggplot2.html
# note: creates plots relating perceived social status to various motivations participants cited for starting social media use

plot5 <- ggplot(
  data = selectdata,
  mapping = aes(x = STATUS, y = MOTIVESTART5)
) + geom_point(position = "jitter", color = "blue") + geom_smooth(method = "lm", color = "blue") + xlim(0, 10) + ylim(0, 5) +
  ggtitle("Perceived Social Status vs. Life Event related Motivations") +
  xlab("Perceived Social Status (Lowest to Highest)") +
  ylab("Motivation related to Life Events (Least to Most Impact)") + theme_bw()

plot5

Figure 5 - Participants’ perceived social status and their motivations to use social media based on a desire for sharing life events.

ggsave("plots/plot5_lifeevents.png", 
       plot = plot5,
       width = 10, height = 8, dpi = 300)

# source: https://shanemccarty.github.io/FRIplaybook/ggplot2.html
# note: creates plots relating perceived social status to various motivations participants cited for starting social media use

plot6 <- ggplot(
  data = selectdata,
  mapping = aes(x = STATUS, y = MOTIVESTART6)
) + geom_point(position = "jitter", color = "purple") + geom_smooth(method = "lm", color = "purple") + xlim(0, 10) + ylim(0, 5) +
  ggtitle("Perceived Social Status vs. Agreeable Opinionated Content related Motivations") +
  xlab("Perceived Social Status (Lowest to Highest)") +
  ylab("Motivation related to (+) Opinions (Least to Most Impact)") + theme_bw()

plot6

Figure 6 - Participants’ perceived social status and their motivations to use social media based on a desire for viewing opinionated content that aligns with their beliefs.

ggsave("plots/plot6_sameopinion.png", 
       plot = plot6,
       width = 10, height = 8, dpi = 300)

1.4.3 Motivations to Stop Using Social Media

# source: https://shanemccarty.github.io/FRIplaybook/ggplot2.html
# note: creates plots relating perceived social status to various motivations participants cited for starting social media use

plot7 <- ggplot(
  data = selectdata,
  mapping = aes(x = STATUS, y = MOTIVEEND1)
) + geom_point(position = "jitter", color = "red") + geom_smooth(method = "lm", color = "red") + xlim(0, 10) + ylim(0, 5) +
  ggtitle("Perceived Social Status vs. Current Events related Motivations") +
  xlab("Perceived Social Status (Lowest to Highest)") +
  ylab("Motivation related to Current Events (Least to Most Impact)") + theme_bw()

plot7

Figure 7 - Participants’ perceived social status and their motivations to stop using social media based due to learning about current events.

ggsave("plots/plot7_currentevents.png", 
       plot = plot7,
       width = 10, height = 8, dpi = 300)

# source: https://shanemccarty.github.io/FRIplaybook/ggplot2.html
# note: creates plots relating perceived social status to various motivations participants cited for starting social media use

plot8 <- ggplot(
  data = selectdata,
  mapping = aes(x = STATUS, y = MOTIVEEND2)
) + geom_point(position = "jitter", color = "orange") + geom_smooth(method = "lm", color = "orange") + xlim(0, 10) + ylim(0, 5) +
  ggtitle("Perceived Social Status vs. Disagreeable Opinionated Content related Motivations") +
  xlab("Perceived Social Status (Lowest to Highest)") +
  ylab("Motivation related to (-) Opinions (Least to Most Impact)") + theme_bw()

plot8

Figure 8 - Participants’ perceived social status and their motivations to stop using social media based due viewing opinionated content that clashes with their beliefs.

ggsave("plots/plot8_differentopinions.png", 
       plot = plot8,
       width = 10, height = 8, dpi = 300)

# source: https://shanemccarty.github.io/FRIplaybook/ggplot2.html
# note: creates plots relating perceived social status to various motivations participants cited for starting social media use

plot9 <- ggplot(
  data = selectdata,
  mapping = aes(x = STATUS, y = MOTIVEEND3)
) + geom_point(position = "jitter", color = "yellow") + geom_smooth(method = "lm", color = "yellow") + xlim(0, 10) + ylim(0, 5) +
  ggtitle("Perceived Social Status vs. Addiction related Motivations") +
  xlab("Perceived Social Status (Lowest to Highest)") +
  ylab("Motivation related to Addiction (Least to Most Impact)") + theme_bw()

plot9

Figure 9 - Participants’ perceived social status and their motivations to stop using social media based due to feeling addicted.

ggsave("plots/plot9_addiction.png", 
       plot = plot9,
       width = 10, height = 8, dpi = 300)

# source: https://shanemccarty.github.io/FRIplaybook/ggplot2.html
# note: creates plots relating perceived social status to various motivations participants cited for starting social media use

plot10 <- ggplot(
  data = selectdata,
  mapping = aes(x = STATUS, y = MOTIVEEND4)
) + geom_point(position = "jitter", color = "green") + geom_smooth(method = "lm", color = "green") + xlim(0, 10) + ylim(0, 5) +
  ggtitle("Perceived Social Status vs. Socialization related Motivations") +
  xlab("Perceived Social Status (Lowest to Highest)") +
  ylab("Motivation related to Social Reasons (Least to Most Impact)") + theme_bw()

plot10

Figure 10 - Participants’ perceived social status and their motivations to stop using social media based due to social reasons.

ggsave("plots/plot10_socialreasons.png", 
       plot = plot10,
       width = 10, height = 8, dpi = 300)

# source: https://shanemccarty.github.io/FRIplaybook/ggplot2.html
# note: creates plots relating perceived social status to various motivations participants cited for starting social media use

plot11 <- ggplot(
  data = selectdata,
  mapping = aes(x = STATUS, y = MOTIVEEND5)
) + geom_point(position = "jitter", color = "blue") + geom_smooth(method = "lm", color = "blue") + xlim(0, 10) + ylim(0, 5) +
  ggtitle("Perceived Social Status vs. Boredom related Motivations") +
  xlab("Perceived Social Status (Lowest to Highest)") +
  ylab("Motivation related to Boredom (Least to Most Impact)") + theme_bw()

plot11

Figure 11 - Participants’ perceived social status and their motivations to stop using social media based due to boredom.

ggsave("plots/plot11_boredom2.png", 
       plot = plot11,
       width = 10, height = 8, dpi = 300)

# source: https://shanemccarty.github.io/FRIplaybook/ggplot2.html
# note: creates plots relating perceived social status to various motivations participants cited for starting social media use

plot12 <- ggplot(
  data = selectdata,
  mapping = aes(x = STATUS, y = MOTIVEEND6)
) + geom_point(position = "jitter", color = "purple") + geom_smooth(method = "lm", color = "purple") + xlim(0, 10) + ylim(0, 5) +
  ggtitle("Perceived Social Status vs. Disturbing Content related Motivation") +
  xlab("Perceived Social Status (Lowest to Highest)") +
  ylab("Motivation related to Disturbing Content") + theme_bw()

plot12

Figure 12 - Participants’ perceived social status and their motivations to stop using social media based due to seeing disturbing content.

ggsave("plots/plot12_disturbingcontent.png", 
       plot = plot12,
       width = 10, height = 8, dpi = 300)

1.4.4 Spearman Rho Correlation

# source: https://rpubs.com/aaronsc32/spearman-rank-correlation
# note: performs spearman rho correlation tests on all combinations of STATUS and motivation variables
x1 = selectdata$MOTIVESTART1
x2 = selectdata$MOTIVESTART2
x3 = selectdata$MOTIVESTART3
x4 = selectdata$MOTIVESTART4
x5 = selectdata$MOTIVESTART5
x6 = selectdata$MOTIVESTART6
x7 = selectdata$MOTIVEEND1
x8 = selectdata$MOTIVEEND2
x9 = selectdata$MOTIVEEND3
x10 = selectdata$MOTIVEEND4
x11 = selectdata$MOTIVEEND5
x12 = selectdata$MOTIVEEND6
y = selectdata$STATUS

test_result1 = cor.test(x1, y, method = "spearman")
print(test_result1)

    Spearman's rank correlation rho

data:  x1 and y
S = 170512, p-value = 0.7206
alternative hypothesis: true rho is not equal to 0
sample estimates:
       rho 
0.03584117 

test_result2 = cor.test(x2, y, method = "spearman")
print(test_result2)

    Spearman's rank correlation rho

data:  x2 and y
S = 178045, p-value = 0.9463
alternative hypothesis: true rho is not equal to 0
sample estimates:
         rho 
-0.006753073 

test_result3 = cor.test(x3, y, method = "spearman")
print(test_result3)

    Spearman's rank correlation rho

data:  x3 and y
S = 178000, p-value = 0.9483
alternative hypothesis: true rho is not equal to 0
sample estimates:
         rho 
-0.006497839 

test_result4 = cor.test(x4, y, method = "spearman")
print(test_result4)

    Spearman's rank correlation rho

data:  x4 and y
S = 191179, p-value = 0.6171
alternative hypothesis: true rho is not equal to 0
sample estimates:
        rho 
-0.04983472 

test_result5 = cor.test(x5, y, method = "spearman")
print(test_result5)

    Spearman's rank correlation rho

data:  x5 and y
S = 163068, p-value = 0.4362
alternative hypothesis: true rho is not equal to 0
sample estimates:
       rho 
0.07793531 

test_result6 = cor.test(x6, y, method = "spearman")
print(test_result6)

    Spearman's rank correlation rho

data:  x6 and y
S = 166200, p-value = 0.3663
alternative hypothesis: true rho is not equal to 0
sample estimates:
        rho 
-0.09273055 

test_result7 = cor.test(x7, y, method = "spearman")
print(test_result7)

    Spearman's rank correlation rho

data:  x7 and y
S = 184546, p-value = 0.6641
alternative hypothesis: true rho is not equal to 0
sample estimates:
        rho 
-0.04351145 

test_result8 = cor.test(x8, y, method = "spearman")
print(test_result8)

    Spearman's rank correlation rho

data:  x8 and y
S = 182160, p-value = 0.5451
alternative hypothesis: true rho is not equal to 0
sample estimates:
        rho 
-0.06091945 

test_result9 = cor.test(x9, y, method = "spearman")
print(test_result9)

    Spearman's rank correlation rho

data:  x9 and y
S = 159597, p-value = 0.4836
alternative hypothesis: true rho is not equal to 0
sample estimates:
       rho 
0.07049067 

test_result10 = cor.test(x10, y, method = "spearman")
print(test_result10)

    Spearman's rank correlation rho

data:  x10 and y
S = 163523, p-value = 0.4645
alternative hypothesis: true rho is not equal to 0
sample estimates:
        rho 
-0.07512986 

test_result11 = cor.test(x11, y, method = "spearman")
print(test_result11)

    Spearman's rank correlation rho

data:  x11 and y
S = 179618, p-value = 0.8911
alternative hypothesis: true rho is not equal to 0
sample estimates:
       rho 
0.01365263 

test_result12 = cor.test(x12, y, method = "spearman")
print(test_result12)

    Spearman's rank correlation rho

data:  x12 and y
S = 186581, p-value = 0.2359
alternative hypothesis: true rho is not equal to 0
sample estimates:
       rho 
-0.1195969 

The demographic collected that is most relevant to this study is the perceived social status of the participants. With a sample of 101 respondents, the average rating they gave themselves (on a scale of 1 to 10, 1 being the lowest social status and 10 being the highest) was 6.02. The standard deviation of the sample was 1.686. 63.3% of the respondents were women (n=64), 29.7% were men (n=30), and 6.9% (n=7) were another gender (non-binary, genderfluid, etc.). It is worthwhile to note that this is not aligned with the average gender demographics of all colleges in the United States (Data USA). A Spearman Rho correlation showed no significant relationship between perceived social status and any of the motivations inquired about in the survey, both relating to starting social media use and stopping social media use. The figures also demonstrate this, with points plotted leading to a line of best fit that is not very representative of the data. These findings fail to reject the null hypothesis, showing that one’s social status does not play a role in how they are motivated to use or abstain from social media.

2 Discussion

The results of this study demonstrate a lack of correlation between a social media user’s belief on where they rank on the “societal ladder” and how they are motivated to start and stop using social media. After comparing the study’s participants’ perceived social status with how much various motivations impact their social media use, the null hypothesis that there is no substantial relationship between the two fails to be rejected. A main limitation of the study was location. While the survey was open to participants from any college within the United States, it was difficult to get very many responses from campuses other than Binghamton. Thus, the data is skewed to be more reflective of Binghamton students rather than undergraduate college students overall. Existing literature has shown relationships between how social media users of differing sociodemographics interact/are impacted by social media in different ways (Tandon et al., 2021). In combination with previous research, the results from this study can be built upon with further focus on sociodemographics in the social media landscape. This includes consideration when building interventions for social media-related mental distress or media addiction. Future researchers may also find benefit in looking into how one may be motivated to use social media based on other demographics, or how perceived social status may be correlated with the impact politically polarizing content has on a user.

3 References

Colleges, universities & professional schools, including junior colleges | Data USA. (n.d.). Retrieved October 30, 2025, from https://datausa.io/profile/naics/colleges-universities-professional-schools-including-junior-colleges

O’Reilly, M. (2020). Social media and adolescent mental health: The good, the bad and the ugly. Journal of Mental Health, 29(2), 200–206. https://doi.org/10.1080/09638237.2020.1714007

Tandon, A., Dhir, A., & Mäntymäki, M. (2021). Jealousy due to social media? A systematic literature review and framework of social media-induced jealousy. Internet Research, 31(5), 1541–1582. https://doi.org/10.1108/INTR-02-2020-0103