Social Media Addiction in Young Adults from the Greater Edmonton Area (Full Article/Chapter 1)

Abstract

The purpose of this study is to explore the relationship between social media usage, social media addiction (SMA), and the effect SMA has on young adults in Edmonton and the surrounding areas. The hypothesis of this study is as follows: Over half of young adults in the Edmonton area are addicted to social media. Six peer-reviewed research studies have been collected that discuss topics such as social media addiction, social media usage, negative effects of social media on young adults, and traits common with high social media usage. The current research will add to the academic conversation surrounding this complex topic. Three phases of data collection were used to collect and analyze the quantitative and qualitative data needed to conclude the research: (1) an online survey on the general conceptions around social media usage, (2) a screen time log that tracked participants’ social media usage hours for one week, and (3) a follow-up interview that determined participants’ perception of social media and addiction. The survey yielded 66 responses, the social media screen-time screenshots yielded 10 participants, and the interviews yielded two participants The main conclusions of this study are as follows: this sample spends more time on social media than the average global population; there is a negative perception and hesitancy surrounding the term addiction; social media has become integral to the lives of most people and is difficult to avoid; and frequent social media use is not always attributed to addiction.

Keywords: social media addiction, young adult, social media, addiction

Introduction

Statement of the Purpose

The hypothesis of this study is as follows: Over half of young adults in the Edmonton area are addicted to social media. This research seeks to discover how many young adults in the Edmonton area struggle with social media addiction. Social media has become an integral part of many lives in the 21st century; it can be consumed in moderate amounts, but young adults may succumb to its addictive design. This research uses surveys, social media screen-time logs, and interviews to evaluate how much time young adults spend on social media. For the purpose of this research, young adults are considered to be people between the ages of 18 and 24 years old. University students are not the only young adults who participated; the participants range from students to full-time workers, people on maternity leave, and parents. Furthermore, for this research, the Edmonton area is considered to be anywhere within city limits or within 20 kilometres of Edmonton—the specific neighbourhoods and cities included are outlined in the survey. Lastly, social media is considered to be one of the following platforms: Facebook, Instagram, Twitter, Snapchat, TikTok, YouTube, and Pinterest.

Research Problem

Most young adults in this generation have grown up with smartphones and social media at their disposal 24/7; therefore, it has become a part of most of their daily lives. This poses the question: Where is the line drawn between frequent use and addiction? The recent addition of the “screen time” feature on most mobile devices has made people aware of how much time they spend on their smartphones daily. This feature illustrates how much time is spent on each app, which is beneficial in determining whether most of their time is spent on social media or elsewhere. With everyone at home for the year 2020, due to the Covid-19 pandemic, screen time reports increased. In 2020, novelist Phil Stamper wrote “when I got that [screen time] notification, I found myself flinching at the results . . .” (as cited in Andrews, 2020, para. 9).

One benefit of studying social media use and addiction is that most of the source material is from within the past decade, due to the recent emergence and popularity of these platforms. Recent research studies are on topics such as the following:

●       How social media affects behaviour (Larson, 2021)

●       Variables that predict social media addiction (Ciplak, 2020)

●       The impact of social media addiction on the well-being of students (Zhao, 2021)

●       Social media addiction and the internet (Nakaya, 2015)

The source material on this subject shows that there are harmful effects of high social media use and that it can negatively affect students. Since that has been established, it is first important to determine whether or not young adults in the Edmonton area are addicted to social media before examining its negative effects. Most of these studies also touch on how much time their participants spend on social media; however, there have been no peer-reviewed studies that specifically focus on Canadian young adults, never mind those in the Edmonton region.

Research Question

This study’s research question is: Are the majority of young adults in the Edmonton area addicted to social media? The sub-question is: How does social media addiction impact the lives of young adults in the Edmonton area?

Significance of the Study

Social media addiction is an increasing issue as technology evolves. Technology has become an integral part of people’s lives, whether it be through school, work, or personal use. This research study assesses how much time the average young adult, which includes people between the ages of 18 and 24 years old, spends on social media each day. Most of the research done on this topic has been developed before the Covid-19 pandemic, meaning this current research gives insight as to how increased time at home has affected everyone’s social media usage. Many workplaces and schools have switched to an online format for some of their delivery, increasing each individual’s time spent online. Learning whether or not young adults have increased their social media usage is beneficial for each individual; social scientists can also examine the effects that social media has on the human brain, daily functions, perceptions, and neurological understanding.

Limitations

There are four key limitations within this research study. A substantial limitation is the lack of an agreed upon formal definition of social media addiction. There has been an increased interest in studying the effects of excessive social media usage, but a definite scale of SMA has yet to be established. Currently, social media addiction is not included in the DSM-5, limiting this study to available terminology (American Psychiatric Association, n.d.).

Participation bias that may stem from a negative perception of addiction may also limit the results. The negative perception may be internalized by the participants and addiction denial may skew data. The questioning framework and terminology were staged to reduce data collection and analysis errors.

A limitation within this study is the differentiation between social media use for work, school, and personal reasons. Some of the participants are required to engage with social media more than average, which was reflected in their surveys. Survey question 15 was used to help understand which participants used social media for school, work, or both.

Another area of limitation came from only reviewing the social media usage from a user’s smartphone. The evolution of technology has provided a variety of devices on which to access social media. Having these multiple points of accessibility to social media platforms makes it difficult to track usage across multiple devices. For this reason, the research sample is confined to only cell phone users who can access built-in usage statistics for consistent data collection. The researchers took these limitations into account while conducting the research by adjusting and staging questions and limiting data to specific devices.

Definitions of Terms

Addicted

Exhibiting a compulsive, chronic, physiological or psychological need for a habit-forming substance, behavior, or activity (Merriam-Webster, n.d.)

Dependent Variable

“What happens as a result of the independent variable” (National Library of Medicine, n.d.). For example, in this current study, the presence or lack of addiction is the dependent variable.

DSM-5

Diagnostic and Statistical Manual of Mental Disorders: authoritative volume that defines and classifies mental disorders in order to improve diagnoses, treatment, and research by hundreds of international experts in all aspects of mental health (American Psychiatric Association, 2021).

Independent Variable

“What we expect will influence dependent variables (National Library of Medicine, n.d.). For example, in this current study, social media is the independent variable.

Social Media Addiction

Social media addiction is a behavioral addiction that is defined by being overly concerned about social media, driven by an uncontrollable urge to log on to or use social media, and devoting so much time and effort to social media that it impairs other important life areas. (Addiction Center, n.d.). Although social media addiction is not yet a classified disorder in the DSM-5, “a key aspect of social media addiction is that individuals do not only use social media more frequently, but may experience distress without it” (Lin et al., 2017, as cited in Larson, 2021, p. 233)

Social Media

Forms of electronic communication. . . through which users create online communities to share information, ideas, personal messages, and other content (such as videos) (Merriam-Webster, n.d.)

Young adult

A person who is in his or her late teenage years or early twenties (Cambridge Dictionary, 2022). For the purpose of this research study the age range has been determined as 18 to 24 years old.

Review of the Literature

Introduction

For the purpose of this current study, six peer-reviewed sources were used to guide the research by generating credible sources of information relating to the topic and acknowledging missing factors. The past research studies are used to reference this study’s conclusions with other researchers’ findings and collected data. A change in statistics from past research was presumed due to the Covid-19 global pandemic being identified as one of the causes for an online activity surge, increasing the need and desire to access social media. An abundance of sources exist on the topic of social media usage and addiction, which indicates a growing desire to know more about the effects of increased social media usage on individuals.

Literature Review

Social Media Addiction and Emotional Behaviour

Ciplak (2020) focuses on “[examining] the variables that predict social media addiction” (p. 48); he has two hypotheses related to this:

·         H1: “The level of social media addiction related to the weekly time spent on social media” (p. 48).

·         H2: “Narcissism, happiness and weekly time spent on social media predict social media addiction at statistically significant levels” (p. 48).

The idea behind this research is that certain traits, notably narcissism and happiness, accompanied by extensive time spent on social media weekly, are good predictors of social media addiction. The official definition of narcissism is considered to be “the self-admiration of the individual” (Freud, 2010, as cited in Ciplak, 2020, p. 49). This research highlights that social media addiction has not been added as an official psychological disorder to the Diagnostic and Statistical Manual of Mental Disorders (DSM-5); however, in recent years, they have included computer gaming as an addiction. Therefore, it seems as though social media addiction is on its way to becoming a classified mental illness (Ciplak, 2020).

The convenient sampling method was used to gather participants for Ciplak’s (2020) study. According to Galloway (2005), “convenience sampling involves using respondents who are “convenient” to the researcher. There is no pattern whatsoever in acquiring these respondents” (p. 860). The study group was comprised of 239 adults (140 women and 99 men) in different regions of Turkey (Ciplak, 2020). The measurement tools included the Social Media Addiction Scale, the Happiness Scale – Short Form, the Narcissistic Admiration and Rivalry Scale, and a personal information form. The three scales all “consist of [varied] items with five-point Likert-type response options” (p. 51). The Social Media Addiction Scale has twenty items, the Happiness Scale has six items, and the Narcissistic Admiration and Rivalry Scale has eighteen items. The methodology in this study is quantitative due to the numerical nature of the scales and the close-ended questions in the personal information form. At the end of the study, both hypotheses were supported by the research. Firstly, those who reported spending more time on social media weekly scored higher on the Social Media Addiction scale. Those who reported spending less time on social media weekly scored lower on the Social Media Addiction scale. Secondly, there was a “positive relationship” between high social media usage/addiction and narcissism (Ciplak, 2020, p. 53). There was a “negative relationship” between high social media usage and happiness (p. 53). This means that the more time participants spent on social media and the higher they scored on the addiction scale, the higher they scored on the narcissism scale. It also concluded that the more time they spent on social media, the lower they scored on the happiness scale.

Ciplak’s (2020) research is significant to the present study for various reasons. Firstly, it highlights that social media addiction is not yet a classified psychological disorder in the DSM-5. Secondly, it highlights the relationship between social media addiction and lower rates of happiness. Mental health is something that many young people struggle with, therefore, if proven that the majority of young adults in the Edmonton area are addicted to social media, Ciplak’s (2020) research shows that this could be one reason for lower rates of happiness among this population.

Theories and Models in Social Media Addiction

Sun and Zhang (2021) explore the models and theories that researchers have attributed to social media addiction over time (p. 1). This review questions what theories and models have been used in existing studies to explain the development of SMA, and what purpose they each serve.

Sun and Zhang (2021) use 55 empirical studies on social media addiction and analyze 25 common theories and models found throughout. The review aids future research by clearly defining the concepts existing studies have used concerning SMA. The review provides a better understanding of SMA, but there is more information to be collected on how these models and theories explain the SMA phenomena (Sun & Zhang, 2021).

Table 1, found in the research article, categorizes the summarized models and theories with their corresponding variables, studies, and sources (Sun & Zhang, 2021). The data from the survey, interviews, and screen time logs of this study were compared to the examples in Sun and Zhang’s review to collect common themes and patterns in the results section.

Though the review itself does not conduct primary research, it examines previous empirical studies and observations. The review categorizes the models and theories based on their relation to the dependent variables found. It is important to mention that some theories and models mentioned are relevant to a variety of perspectives and categorizing them under single structures may be a limitation on the focus construct (Sun & Zhang, 2021). The categories included in the review are: Dispositional difference perspective, Motivational Perspective, Neurobiological Perspective, Decision-Making Perspective, Learning Perspective, Technology Use Focused, Social Network Focused, and Internet Specific Models.

The final sample of the review consists of 55 articles which were screened to include studies that have been peer-reviewed and written in English, focus on the development of SMA and related factors, and apply existing theories and models that enhance future research design. Exclusion from the review’s data search happened when, 1) the study adopted a theoretical framework only to measure SMA, 2) the study did not include any key concepts/constructs from the theories/models they draw upon, and 3) addictive social media use was examined as an independent variable only to predict other concepts (Sun & Zhang, 2021, p. 2). The samples found were used to collect and categorize data, Sun and Zhang (2021) used this collection of research to build the review and table for future researchers to use as a reference guide of common models and theories along with the corresponding variables and studies.

The result of this review is a reference guide for future researchers, including this research team, to use in studies on SMA. Its extensive examination of the theories and models of SMA will aid future research in conceptualizing the phenomenon and its effects on those dealing with these behavioural addiction symptoms. The diversity of perspectives mentioned in the review only begins to explain the complexity of SMA, but a better theoretical understanding of this problematic use is required before intervention (Sun & Zhang, 2021). This current study uses the models and theories provided to support the evidence collected in the surveys, screen time logs, and interviews to conclude interpretive and causal claims of social media addiction and its variables in the psychological condition.

The Social Media Addiction Scale

Tutgun-Unal and Deniz’s (2015) article directly correlates with this current research topic because they developed a scale to measure the addictive traits of social media among university students.

Researchers wanted to determine to what extent social media usage is unhealthy, and where the average university student falls on that scale. The research method used is qualitative as it seeks to understand the reasoning behind human social media obsession. Tutgun-Unal and Deniz (2015) are looking for an answer as to why social media is so intriguing and why it holds the attention of so many users for such a long period.

The Social Media Addiction Scale (SMAS) was developed into a five-point Likert scale. An increase in points from the Likert scale equalled an increase in perceived social media addiction for each individual. Individuals who scored between a certain range of points would be classified anywhere from “No addiction” to “Very High Addicted” (Tutgun-Unal & Deniz, 2015).

In this study, 775 students, holding at least one active social media account, were evaluated according to a five-point Likert scale. The media applications included in the participation standards were Facebook, Instagram, and Twitter so the results were consistent. The ages of university students ranged from 18–45 years old across three universities in Istanbul. Over half of the participants were female, with all participants being distributed across various education departments.

After all initial studies were completed, a framework of 41 items and four factors was developed. Out of all participants, the highest score was 205 and the lowest was 41. It was concluded that 59% of the total variance was viewed as being quite high in social sciences. Researchers expect a social media increase as technology grows and becomes more used in everyday life (Tutgun-Unal & Deniz, 2015).

Tutgun and Deniz’s (2015) research applies to this current study because it compares another way of measuring social media addiction. The Likert scale developed is a reference point for the researchers of this study as data is collected and allows for the comparison of different execution styles.

Smartphone Addiction, Stress, Academic Performance, and Life Satisfaction

Samaha and Hawi (2016) explore the idea that smartphone addiction has negative effects on mental health and overall well-being. One goal of the study was to investigate the link between smartphone addiction risk and life satisfaction, as mediated by stress and academic achievement. The other goal was to assess whether stress and academic performance impact life satisfaction and facilitate smartphone addiction.

The stratified random sample method was used in this transverse study (Samaha & Hawi, 2016). The university’s email system was used to reach out to all students regarding the study and ensure the confidentiality of volunteers. This online survey was completed by 293 students, ranging from 18 to 25 years old, using the university’s student portal. The sample size was reduced to 249 after cases with invalid responses were eliminated from the dataset.

In terms of data collection methods, there were four components to the survey, including one for demographic data and three independent research instruments. Gender, age, education level, and academic major were all listed in the demographic information section. The Smartphone Addiction Scale–Short Version (SAS-SV), the Perceived Stress Scale (PSS), and the Satisfaction with Life Scale were the remaining components (SwLS) (Samaha & Hawi, 2016).

Of 249 students surveyed, 44.6% were at risk of smartphone addiction and 53.4% reported high levels of perceived stress compared to that of students at low risk (49.1%). The average respondent was 21 years old. The results of this study reveal a strong correlation between smartphone addiction and stress, a negative link between smartphone addiction and academic performance, and a mediated negative link between smartphone addiction and life satisfaction (Samaha & Hawi, 2016). This study relates to the current research topic by highlighting the negative effects of excessive use of social media on overall health and well-being, especially concerning youth and young adults.

Impact of Social Media Types and Addiction

Zhao (2021) compares the subjective well-being of social media addicted and non-addicted students through social media use types. Zhao focuses on the influence of different social media use types, and the relationship between social media addiction and subjective well-being. The research answers the question: Does the way a student uses social media affect their subjective well-being?

The study consists of qualitative and quantitative data collection across four categories: social use, entertainment use, social media addiction and subjective well-being.

Figure 1

Social Media Addiction and Subjective Well-Being Research Model

Zhao’s (2021) research model above charts the effects of social use and entertainment use on social media addiction and subjective well-being.

The four measures used in existing sources, regarding similar topics, utilized Likert scale questions. By using Likert scales, researchers were able to assign a numerical value to emotions and behaviours and range them from least to most. The numerical value and range provide the ability to measure a unidimensional variable (Merrigan et al., 2012).  The questionnaire Zhao (2021) created was distributed through random sampling at a comprehensive university in Anhui Province, China, with nearly 30,000 students. Four hundred questionnaires were distributed, with 370 valid questionnaires obtained. The total sample included 162 boys (43.9%) and 208 girls (56.1%). The sample was then divided into 224 (60.5%) addicted and 146 (39.5%) non-addicted students. Addicted students were categorized by scoring three or more on at least six responses on the social media addiction scale (Zhao, 2021, p. 3).

The research done by Zhao’s (2021) study supports the interpretative claims that the type of social media use does affect students’ subjective well-being in different ways. The results of the total addicted and non-addicted students show that social use positively predicts the subjective well-being of college students; that is, social use can help them improve subjective well-being (Pang, 2018b). There are various reasons as to why and how social use improves subjective well-being, the reasons seem to focus on the positive enhancements of socialization in real-world behaviours and interactions. Zhao states that this study is “consistent with previous research (Błachnio et al., 2016; Ponnusamy et al., 2020), social media addiction harms college students’ subjective well-being, which is supported in the validation of all three groups” (Zhao, 2021, p. 6). The addictive tendencies caused by the overuse of social media are the reasoning to a decreased overall subjective well-being, producing “symptoms (i.e., salience, tolerance, mood mediation, relapse, withdrawal, and conflict) (Andreassen et al., 2012) which will have adverse effects on their mental health, learning and life, and then reduce their subjective well-being” (Zhao, 2021, p. 5).

Zhao’s (2021) comparative analysis and sources were used in the creation of the survey, interview questioning, and framework. Similar Likert scales were used as the four variable measurements mentioned by Zhao: social use, entertainment use, social media addiction and subjective well-being. These measurements provided participants with a variety of response options that were still controlled for consistent data collection and analysis. The data collected from this research study was compared to Zhao’s data and hypotheses conclusions. Numerical data were analyzed according to the sample Zhao provided to verify the validity of the current hypothesis on the prevalence social media addiction and the impact SMA has on the lives of young adults.

Social Media Usage and Fear of Missing Out

Gosain and Yadav (2020) investigate the impact of social media usage (SMU) and the fear of missing out (FOMO) among youth. To achieve this, Gosain and Yadav assessed the difference between adolescents and young adults in relation to SMU and FOMO, the effects of social media usage among adolescents, the effects of social media usage among young adults, the effect of FOMO among adolescents, as well as the effect of FOMO among young adults. There are two hypotheses for their study:

·         H1: There will be a significant level of difference between youngsters on FOMO (p. 78).

·         H2: There will be a significant level of difference between youngsters on usage of social media (p. 78).

In some research experiments, there is a dependent variable, which gets tested and measured in an empirical study or scientific experiment; there is also an independent variable, which is controlled in a scientific experiment to test the effects on the dependent variable (Gosain & Yadav, 2020). In this particular study, three variables were being measured, two of them being dependent: FOMO and social media usage. The independent variable is the age of adolescents (13–17 years) and young adults (18–25 years). To study independent variables such as age group, social media usage, and fear of missing out, a total sample of 100 students from different age groups was taken, resulting in 50 school-aged students (13 to 17 years old) and 50 college-aged students (18 to 25 years old). All the participants were selected using the “FOMO tool” (Przybylski et.al., 2013). Both male and female participants were selected through a purposive sampling technique, and they were asked to identify their social media usage using two open-ended questions: i) Everyday time spent on social media networking websites (in hours) and ii) Day by day recurrence of visiting internet-based profiles.

Gosain and Yadav’s (2020) study utilizes quantitative research methods and was conducted at both high school school and university level. Other questions asked of the participants include: “are you on more than two social networking sites?” which they confirmed; “Usually you use social media for professional, educational or business purposes?” which more than 90% of the participants denied; and  “which one is more satisfying either playing games online like PubG or preferring to play outdoor games?” more than 85% of the participants from both age groups answered that they spent hours playing games online, to which they prefer over any other outdoor game (p. 80). To organize and interpret the data obtained, statistical techniques such as mean, SDs, and t-tests were utilized. A survey was also carried out to find out the degree of FOMO and social media addiction faced by youth, using a questionnaire and interview plan to collect data for the study. It contained 10 close-ended questions related to FOMO and two questions related to social media usage, a few general questions related to both the variables were also asked for the interview schedule.

In concluding the study, it was found that everyday time spent on social media networking websites was substantially different for adolescents from that of young adults (Gosain & Yadav, 2020, p. 82). Thirty-two percent of adolescents spent 1–3 hours on social media daily, 34% of them spent 4–6 hours, and 34% of them spent 7 hours and more. Thirty-two percent of young adult participants also spent 1–3 hours on social media daily, 60% spent 4–6 hours, and only 8% of them responded that they spent more than seven hours. In response to the question of “day-by-day recurrence of visiting internet based profiles”, adolescents responded that 26% of them visit social media profiles 3–5 times daily, 16% visited them 6–10 times and 58% of them visit social media profiles 11–20 times daily. Young adults’ percentages differed from that of adolescents in terms of frequency of visiting social media platforms, as 36% of them responded that they visit their social media sites 3–5 times daily, 36% about 6–10 times a day, and only 28% responded that they visit social media profiles 11–20 times daily (Gosain & Yadav, 2020, p. 83). This indicated that adolescents are more dependent or addicted to social media usage.

Gosain and Yadav’s research is relevant to the current research study, as it seeks to understand the rise of youth’s engagement in social media, using a concept called fear of missing out (FOMO). FOMO is an example of a social psychological process which has been associated with excessive usage of social media platforms. The current study uses a comparative analysis to highlight the understanding of social media usage and FOMO among adolescents and young adults, and thus proves that there is a significant difference between youth social media usage and FOMO.

Contribution This Study Will Make to the Literature

There is a significant amount of literature surrounding the negative effects of high social media use and social media addiction. Many of these studies also address social media addiction and highlight that “a key aspect of social media addiction is that individuals do not only use social media more frequently, but may experience distress without it” (Lin et al., 2017, as cited in Larson, 2021, p. 233). Although the studies above prove a relationship between high social media usage and social media addiction, high usage alone is not enough to constitute addiction. It is important to perform continual research regarding this topic because technology and social media platforms are constantly evolving. For example, TikTok did not exist until 2016 but is now one of the most popular social media platforms amongst young people (D’Souza, 2021). Furthermore, no specific research has been done on young Canadian adults and social media use and addiction, especially anything specific to Alberta or the Edmonton Area.

This is where the current research study adds to the discussion. It is also important to acknowledge that the Covid-19 pandemic may play a significant role in increased social media use among young adults. Social media addiction statistics may have changed substantially in the past couple of years. This specific sample and period in time are what makes this study different from others that are similar. This research will also represent Alberta on a global scale when it comes to research surrounding social media addiction in young adults.

Review of the Problem

This current study is focused on investigating whether or not the majority of young adults, ranging from ages 18 to 24, in Edmonton are addicted to social media. The impact social media has on the lives of young adults was closely examined by exploring how social media affects behaviour (Larson, 2021), variables that predict social media addiction (Ciplak, 2020), as well as the impact of social media addiction on the health and well-being of students (Zhao, 2020). Insight can be gained from the sources included in the literature review, as they provided data that support the study’s hypothesis and research questions.

The research team learned that social media addiction is not yet a classified psychological disorder in the DSM-5 and that there is a close relationship between social media use and addiction and social behaviours like social skill deficits. There is also a strong correlation between smartphone addiction and stress, a negative link between smartphone addiction and academic performance, and a mediated negative link between smartphone addiction and life satisfaction (Samaha & Hawi, 2016). Additionally, social media addiction can be a cause of lower rates of happiness in youths, FOMO (fear of missing out) being a significant example of a social psychological process which has been associated with excessive usage of social media platforms (Gosain & Yadav, 2020). These insights complement the current study’s findings and contribute to a further understanding of social media’s impact on the lives of young adults, particularly in the Edmonton region. These topics will be expanded upon in the conclusions.

Methodology

The research team used a mixed-methods research approach to test their hypothesis, which is as follows: Over half of young adults in the Edmonton area are addicted to social media. The researchers started by collecting and analyzing quantitative data, then collected and analyzed qualitative data in a second phase as a follow-up to the quantitative results. This design connected the three phases by using the quantitative results to shape the qualitative research questions, sampling, and data collection. Quantitative methods aided in quantifying patterns in attitudes, behaviours, and other defined variables, to contextualize the results from the study sample in a wider population of the same age group. Qualitative methods were used to analyze non-numerical data and understand concepts related to the research topic. The research team utilized three phases to collect research data. Phase one involved a survey distributed online, phase two involved a collection of screen time logs, and phase three involved participant interviews. These were used to evaluate the addictive tendencies to social media held by each participant.

Phase I: Survey

The online anonymous survey, conducted on Google Forms, contained closed-ended questions to collect data from a predefined group of participants and used random selection as a data collection method. Simple random sampling ensured inclusion in the survey, as each participant in the target demographic had an equal chance of being selected. The age range of the participants was limited to young adults between the ages of 18 and 24 years old, specifically residing within Edmonton city limits and 20km outside limits. All participants were informed fully and truthfully and guaranteed confidentiality before consenting to participation. The researchers also refrained from using the word ‘addiction’ until the interview phase, to avoid influencing or skewing participants’ responses. Instead, the term was rephrased and substituted with words such as ‘significant/substantial use, habits, dependence’ to ensure accuracy while avoiding any negative connotation associated with the word ‘addiction’.  The survey was shared on each of the researchers’ personal social media accounts (Instagram, Facebook, and Twitter) as well as in several private social media groups. Personal platforms used and their approximate reach:

In addition to these personal platforms, the survey link was also posted onto three private pages on Facebook and one private messaging channel via Slack:

●       MacEwan University Student Experience (7.6k members)

●       2020/2021 MacEwan University First-Year Students (2.4k members)

●       MacEwan Sh*tposting (2k members)

●       MacEwan Public Relations 20202/21 Slack Channel (48 members)

The survey consisted of 15 questions with one additional question inviting participants to participate in further research. The questions covered demographics, social media usage and habits, and social media attitudes. The survey was distributed on October 26, 2021, and stayed open for 10 days until November 5, 2021, at midnight; it collected 66 valid responses—some were deleted due to age requirements.

Phase II: Screen Time Log

Participants who indicated interest and consent to continued study involvement were then contacted by the researchers. At this point, the participants’ survey number was used as a reference to maintain anonymity. The screen time logs are a collection of behavioural observation data, tracking how much time each participant spends on social media daily.

The screen time log was tracked across a one-week duration in the beginning of November 2021. Some participants had sent days that were not described by the example for the search but were still included in the data collection, recorded under the days they had provided. The screen time data was used to confirm social media screen time usage over the specified period of time. Social media usage data is collected and provided by most cellular phones (e.g., Apple and Android); this feature (if available) was used to date the back data to the week prior. By tracking the data before the meeting, possible limitations that may be caused due to participant bias were reduced. Farnsworth (2019) explains that “participants will sometimes second-guess what the researcher is after, or change their answers or behaviors in different ways, depending on the experiment or environment. This is called participant bias, or response bias, and it can have a huge impact on research findings” (para. 2).

The sample for the screen time log includes the young adults, aged 18–24, that had previously completed the initial survey and consented to disclose their social media usage data. This sample is not exclusive to university students, but through the availability of the personal outreach of the research team, it was assumed many of the participants would be students. The sample was localized to the Edmonton region. The sample participants were required to have the screen time tracking feature downloaded to their cellular phones, computer and/or other devices they use daily to qualify for continued participation.

Participants were required to have mobile devices with screen time tracking to represent the sample of social media users. Through “always on” technologies, the availability of communication is now the norm for many people for most of the day (Ling, 2012). Abstaining from media use and communication access is an action that now requires intentions, planning, and specific arrangements (Klimmt et al., 2018). The current climate of society calls for the use of mobile devices and social media in young adults’ daily routines, therefore providing the opportunity to track how much of the modern daily device usage is spent on social media.

Data collected was used to generate the average social media usage hours across the sample, as well as noting the maximum and minimal times. The data was then compared to the peer-reviewed research on addictions and social media usage to see how the data correlates to previous research and definitions. The data collected was measured at a ratio level, providing the ability to record precise hours of usage across all participants. The precise hours were then analyzed by external factors such as the participants’ general schedule, daily social availability, and the significance of the weekday to usage. Qualitative data measured these external factors  that control daily time and accessibility participants have to their devices for social media usage. The amount of time each participant has in their schedule available to use social media impacts the results. For example, if a participant is working or in classes that day, they may not have time to be on social media, compared to a day off where they have more time to spend online than usual. It is important to note that participants may have to use social media for their jobs. This factor may cause limitations and skew data; this was reduced by asking participants about the correlation between social media and work during this time.

In summary, the screen time log consists of data collection already provided by the participant’s cellular phone devices. This methodology requires education on how to access the desired data, and confirmation of the collected data’s validity. An example of a screen time data log was created to show what information was key in gaining a general sense of social media timing through quantitative data. The screen time log results were analyzed with the variables previously mentioned to generate an accurate representation of causal claims.

Phase III: Interviews

The third data collection approach used, the interview, was composed to answer any open-ended questions generating qualitative data that might have been lost in translation through a survey response. The survey and interview questions were staged to not allude to any preconceived bias towards addictions, possibly skewing the data collection. Two interviews were scheduled, each with two research team members. The interviews were conducted and recorded via a secure online room, and the recordings were then uploaded to a secure Google Drive in their respective folders. Transcriptions of the interviews were written and filed accordingly. The sample population for the interviews were those who expressed interest and consented to be contacted in the initial survey and met all other participant criteria.

The two interviews conducted were approximately 15 minutes in length. Data was collected by the secondary interviewer in a blank questionnaire. The answers were then analyzed by the research team for themes: beliefs, values, and practices in the behavioural observations marked by the survey and screen time log. The responses provide the qualitative data required on participants’ perception of social media and addiction.

The interviews provided further information towards explanatory claims through self-reporting of the participants. Self-reporting is important in this phase of the research, as it provides an insight into how the participants view their own prior, current, and future beliefs on their behaviours and characteristics surrounding social media addiction. Systematic sources of error are possible, as biases may become prevalent throughout this method. Farnsworth (2019) explains how participation bias can be reduced “by ensuring the participants that their data is truly confidential,” and by creating a judgment-free zone when conducting the research (para. 11). Staging the questioning framework may reduce confirmation and normalcy biases caused by possible shame and/or denial found through the verbal interview following the participants’ disclosure of their behavioural habits.

Results

Introduction

A mixed-methods approach (three phases) was used for this study: surveys, social media screen-time screenshots, and interviews. The survey yielded 66 responses, the social media screen-time screenshots yielded 10 participants, and the interviews yielded two participants. Below, there is a section for each data collection method. Some survey questions have an associated graph or table (see Figures 2–5 and Tables 2–3) and the social media screen-time data is laid out in tables (see Tables 4–5).

Survey Results

Question One: What is your Age Range? (N=72)

Question number one determined the age ranges of the survey participants. Six participants over the age of 25 did not meet the requirements for the target demographic (18–24 years) and their survey results were removed from the data collection. The participants who were removed indicated their ages as the following: 25, 25, 25, 28, 30, and 27. The ages of eligible participants varied slightly; the most frequent age bracket was 26 participants (39.4%) aged 23–24, 23 participants aged 21–22 (34.8%), and the least frequent age bracket was 17 participants aged 18–20 (25.8%). The results of this question were crucial in determining the age demographics laid out in the research question; “young adults” was previously defined as those 18–24 years of age. Question one also shows what audiences by age the survey reached and what age groups showed the most interest in participating in this research topic. The response with the most participants was the 23­–24 age group with 26 participants (39.4%) showing the most interest in participating in the research topic.

Question Two: What are your Preferred Pronouns? (N=66)

The results of the survey’s second question were not necessary for the research study and data analysis. These results were used to appropriately address participants that consented to participate in phase two of the research study. Out of 66 survey respondents, 55 answered question two. Forty-five of those responses identified their preferred pronouns as “She/Her”, and 20 of the responses identified with “He/Him.” As such, there was a higher representation of female-identifying participants (69.2%) than male-identifying participants (30.8%). Two alternate options were available for participants to choose from: “They/Them” and “Prefer not to answer”. No participants selected these responses (0%). The one participant that did not answer question two may not have felt comfortable choosing either option available.

Question three of the survey was used to show where the participants reside in Edmonton. The geographic results of this question determined the eligibility of respondents according to the target demographic defined by the research question: “Edmonton area.” The results show that out of 66 participants, the majority of 16 participants reside in the South-West district of Edmonton (24.2%) and the second-largest representation closely followed with 12 participants from the Downtown district (18.2%). Four of the districts average closely in the middle: eight from the West (12.1%), seven from the North-East (10.6%), six from the North-West (9.1%), and six from the South-East (9.1%). Three districts with equal-lower representation: three from Sherwood Park (4.5%), two from Saint Albert (3%), as well as two from Spruce Grove (3%). Four districts had only one respondent: Beaumont (1.5%), Stony Plain (1.5%), Fort Saskatchewan (1.5%), and Gibbons (1.5%).

Figure 2

Question Four: Please Select All That Apply Regarding Your Employment Status. (N=66)

For question four, there were five initial response options, as well as the opportunity to add an option that the participants deemed more applicable. This was used by some participants to group their responses instead of selecting all that applied. In the following analysis those responses were added to the corresponding answers. Thirty-nine out of 66 participants responded with the employment status “Student” (59.1%); This includes 37 participants who selected “Student”, as well as one student who wrote “Student and part time” and one who wrote “Full time student and part time work”. The number of participants that responded with an employed status reported that 26 overall work part-time (39.4%)—again, this includes the two participants who wrote “Student and part time” and “Full time student and part time work”—and 24 work full-time (36.4%). Three participants responded that they were not employed and looking for work (4.5%), and four reported that they were also not employed but were not looking for work (6.1%). One participant responded with their status as “On Maternity Leave” (1.5%). The responses to this question give the background on the participants’ average daily schedules and their availability to access social media.

Question Five: Do You Have a Social Media Account(s)? (N=66)

All 66 participants indicated that they have one or more social media accounts.

Question Six: On Average, How Often Do You Visit Social Media? (N=66)

Of the 66 responses, 40.9% (27) of the participants use social media every hour, 33.3% (22) use it every 30 minutes or more, and 25.8% (17) use it every couple of hours. None of the respondents indicated that they use social media “once a day” or “once a week or less” as the options presented. This confirms that the majority of young adults in the Edmonton region are active on social media platforms daily, which contributes to some of the excessive habitual behaviours seen throughout the study.