On March 31, 2014, a news story appeared in the
Adobo Chronicles website that the American Psychiatric Association (APA) had classed “selfitis” as a new mental disorder (Vincent
2014). The article claimed that selfitis was “the obsessive compulsive desire to take photos of one’s self and post them on social media as a way to make up for the lack of self-esteem and to fill a gap in intimacy” (p.1). The same article also claimed there are three levels of the disorder—borderline (“taking photos of one’s self at least three times a day but not posting them on social media”), acute (“taking photos of one’s self at least three times a day and posting each of the photos on social media”), and chronic (“uncontrollable urge to take photos of one’s self round the clock and posting the photos on social media more than six times a day”). The story was republished on numerous news sites around the world but it soon became clear the story was a hoax. However, just as empirical research into “internet addiction” started following the publication of a hoax criteria for “internet addiction disorder’ by Ivan Goldberg in 1995 (Widyanto and Griffiths
2010), it appears that the same is arguably true for “selfitis.”
Ever since Griffiths (
1995) published the first paper on “technological addictions,” there has been a marked increase in research into internet addiction, online videogame addiction, mobile phone addiction, social media addiction, etc. There have also been other new technologically related mental health disorders such as “nomophobia” (no mobile phone phobia; King et al.
2010), “technoference” (constant intrusions of technology into everyday life; McDaniel
2015) and “cyberchondria” (feeling ill after searching online for the symptoms of illnesses; Lewis
2006). “Selfitis” appears to be another candidate to add to this growing list although there has been little research on its phenomenology or its sub-components. The present study empirically explored the concept and collected data on the existence of selfitis with respect to the three alleged levels (borderline, acute, and chronic) and developed a new psychometric scale to assess sub-components of selfitis.
A Brief Overview of Selfie Behavior
According to the Oxford Dictionary, a “selfie” refers to “a self-portrait photography of oneself (or oneself with other people), taken with a camera or a camera phone held at arm’s length or pointed at a mirror, which is usually shared through social media” (Sorokowski et al.
2015). Ma et al. (
2017) describe the taking of selfies in terms of self-presentation theory, which is applied to impress others. The taking of selfies is arguably not a stand-alone action because it takes on other dimensions when it is shared via social media. Such actions enable selfie-takers to present themselves in a controlled way. In recent years, selfie-taking has become an incredibly popular activity often going viral online when sharing selfies via social media domains (Frosh
2015; Rettberg
2014; Hess
2015; Roberts and Koliska
2017; Moon et al.
2016).
Research examining selfie behavior has encompassed many different areas. Researchers have investigated selfies in the context of gender and race (Albury
2015), use of selfies in a political context (Baishya
2015; Deller and Tilton
2015), military selfies (Dishy
2017), luxury selfies (Marwick
2015), and the association of selfies with personal traits (Choi et al.
2017; Qiu et al.
2015). Hess (
2015) noted that selfies are used in both private and public settings, where users tend to engage in both environments. Additionally, selfie-taking is more than just the taking of a photograph and can include the editing of the color and contrast, changing backgrounds, and adding other effects, before uploading the picture onto a social media platform. These added options and the use of integrative editing has further popularized selfie-taking behavior (Fox and Rooney
2015), where users can observe their selfie creations as beautiful mirrored selves (Liubinienė and Keturakis
2014). The buying of merchandise associated with the taking of selfies (such as selfie-sticks to improve the picture range) has also grown markedly in recent years (Flaherty and Choi
2016). Selfie-sticks help photographs to appear more like regular ones taken by somebody else (Dinhopl and Gretzel
2016). Despite increasing research into selfie behavior, much of the research has been from a qualitative perspective.
The taking of selfies is a self-oriented action which allows users to establish their individuality (Ehlin
2014) and self-importance (Murray
2015). According to some studies, selfie behavior is also associated with traits such as narcissism (Buffardi and Campbell
2008). Bevan (
2017) investigated the role of narcissism, considerateness, and social attraction towards selfie behavior in terms of using selfie-sticks and found that selfie-stick users were perceived as less socially attractive, moderately narcissistic, and moderately inconsiderate. Halpern et al. (
2016) argued that taking selfies and narcissism are reflective actions. Although there is a strong argument that narcissism has a positive effect towards taking selfies (McCain et al.
2016), other researchers have found no relationship between selfie-taking and narcissism (Re et al.
2016). McCain et al. (
2016) reported that social attractiveness was the primary motivation for posting selfies. Selfie-takers try to provide a greater appeal to others in their social media space (Re et al.
2016). Charoensukmongkol (
2016) reported that attention-seeking, loneliness, and self-centered behavior had a significant relationship with selfie-liking. Although initial media reports thought that selfie-taking would be a fad, it appears that the behavior has become more endemic and is a very popular activity among adolescents and emerging adults (Albury
2015).
Anecdotally, there is evidence of excessive selfie-taking which no doubt prompted the hoax story and criteria published in the
Adobo Chronicles (Vincent
2014). One of the reasons that so many news outlets republished the story was that the criteria used to delineate the three levels of selfitis (i.e., borderline, acute, and chronic) had good face validity. Consequently, the present paper examines these three levels empirically. More specifically, the study attempts to answer two key questions: (i) what are the sub-dimensions that aid the development of selfitis? and (ii) do the identified sub-dimensions differ across the three different levels of selfitis? It is hoped that the answer to such questions will increase understanding towards selfitis functions and the determinants of such action. The target population were Indian students because India is the country that has the most users on
Facebook (Simon
2017). It is also worth noting that deaths sometimes occur as a result of trying to take selfies in dangerous contexts and that India accounts for more selfie deaths in the world compared to any other country with 76 deaths reported from a total of 127 worldwide (Lamba et al.
2016).
Method
The present study used an exploratory design to investigate the proposed research questions. The findings were then used to develop a scale to assess the sub-dimensions of selfitis. The study began by using focus groups to gather an initial set of items that underlie selfitis. These initial set of items were then statistically analyzed using component analysis and a rigorous validation procedure. Although the analysis relied on non-clinical convenience samples, they were likely to represent any of the three selfitis categories (i.e., borderline, acute, and chronic).
Focus Group Interviews
Development of the Selfitis Behavior Scale
Ethics
The study was granted approval by the first author’s university research ethics committee. All participants gave their informed consent to take part in the study.
Results
Exploratory Factor Analysis Results
The results of a KMO test and Barlett’s test of sphericity (Table
3) confirmed that the data were adequate for carrying the principal component analysis (Schumacker and Lomax
1998). Two statements with a communality value above 0.50 were removed from the analysis. The PCA method produced six factors with an Eigenvalues ranging from 1.05 to 6.25 (i.e., environmental enhancement, social competition, attention seeking, mood modification, self-confidence, and social conformity). The six factors explained 70.9% of the total variance. The detailed values and notes are shown in Table
3. At least three items converged in each factor, and this explained the sufficient homogeneity in the measurement (Byrne
2001). All six factors identified had a Cronbach’s alpha score of more than 0.7. The overall reliability of the scale was 0.876 and individual reliability scores of each of the six subscales are reported in Table
4.
Table 3
Results of Exploratory Factor Analysis on the Selfitis Behavior Scale (n = 400)
1.1. Item 1 |
.720
| .210 | .173 | .178 | .139 | .123 |
1.2. Item 7 |
.755
| .100 | .167 | .133 | .181 | .094 |
1.3. Item 13 |
.728
| .251 | .093 | .242 | .039 | .113 |
1.4. Item 19 |
.797
| .116 | .147 | .097 | .133 | .254 |
2.1. Item 2 | .181 |
.714
| .290 | .058 | −.003 | .230 |
2.2. Item 8 | .134 |
.780
| .156 | .095 | .057 | .182 |
2.3. Item 14 | .203 |
.748
| .252 | .089 | −.060 | .226 |
2.4. Item 20 | .120 |
.771
| .018 | −.117 | .070 | −.004 |
3.1. Item 3 | .167 | .194 |
.814
| .118 | .004 | .156 |
3.2. Item 9 | .147 | .163 |
.820
| .025 | .045 | .167 |
3.3. Item 15 | .179 | .196 |
.800
| .129 | .088 | .165 |
4.1. Item 4 | .114 | .008 | .086 |
.857
| .163 | .033 |
4.2. Item 10 | .233 | .028 | .075 |
.755
| .180 | −.082 |
4.3. Item 16 | .163 | .025 | .080 |
.835
| .089 | .047 |
5.1. Item 5 | .097 | .001 | .047 | .189 |
.830
| .132 |
5.2. Item 11 | .165 | .076 | .063 | .065 |
.782
| .008 |
5.3. Item 17 | .105 | −.008 | .003 | .157 |
.839
| −.006 |
6.1. Item 6 | .180 | .057 | .188 | −.053 | .057 |
.781
|
6.2. Item 12 | .142 | .173 | .110 | .014 | .094 |
.776
|
6.3. Item 18 | .126 | .258 | .165 | .045 | −.023 |
.744
|
Variance (%) | 31.223 | 13.663 | 7.633 | 6.686 | 6.239 | 5.250 |
Cumulative variance (%) | 31.223 | 44.886 | 52.219 | 59.205 | 65.444 | 70.693 |
Eigenvalues | 6.245 | 2.733 | 1.527 | 1.337 | 1.248 | 1.050 |
Table 4
Subscales of the Selfitis Behavior Scale and their Cronbach’s alpha scores
Factor 1: Environmental enhancement 1.1 Taking selfies gives me a good feeling to better enjoy my environment 1.2 I am able to express myself more in my environment through selfies 1.3 Taking selfies provides better memories about the occasion and the experience 1.4 I take selfies as trophies for future memories | 0.838 |
Factor 2: Social competition 2.1 Sharing my selfies creates healthy competition with my friends and colleagues 2.2 Taking different selfie poses helps increase my social status 2.3 I post frequent selfies to get more ‘likes’ and comments on social media 2.4 I use photo editing tools to enhance my selfie to look better than others | 0.826 |
Factor 3: Attention seeking 3.1 I gain enormous attention by sharing my selfies on social media 3.2 I feel more popular when I post my selfies on social media 3.3 By posting selfies, I expect my friends to appraise me | 0.812 |
Factor 4: Mood modification 4.1 I am able to reduce my stress level by taking selfies 4.2 Taking more selfies improves my mood and makes me feel happy 4.3 Taking selfies instantly modifies my mood | 0.821 |
Factor 5: Self-confidence 5.1 I feel confident when I take a selfie 5.2 I become more positive about myself when I take selfies 5.3 I take more selfies and look at them privately to increase my confidence | 0.793 |
Factor 6: Subjective conformity 6.1 I gain more acceptance among my peer group when I take selfie and share it on social media 6.2 I become a strong member of my peer group through posting selfies 6.3 When I don’t take selfies, I feel detached from my peer group. | 0.752 |
Scale Validity
The confirmatory factor analysis showed an excellent fit of the six-factor model, χ
2/df
= 1.381, GFI = 0.951, AGFI = 0.934, NFI = 0.940, CFI = 0.982, RMSEA = 0.031. All items for the factors loaded significantly with standardized values more than 0.60, and this satisfied the necessary condition for content validity (Nunnally
1978). The results of the fit indices and content validity confirm the scale can be replicated or used for further research in the field. Table
5 shows the average variance extracted (AVE) of each factor, and all the values of AVE were above 0.5 which confirmed the convergent validity requirements of the scale (Fornell and Larcker
1981). The diagonal values in Table
5 represent the squared root of AVE values; in all the cases it was more than the squared correlation of the respective constructs. This confirms the discriminant validity of the construct (Sánchez-Franco and Roldán
2005).
Table 5
√AVE and squared inter-correlation of items on the Selfitis Behavior Scale
Subjective conformity (1) | 0.503 |
0.709
| | | | | |
Self-confidence (2) | 0.571 | 0.179 | 0.756 | | | | |
Attention seeking (3) | 0.641 | 0.544 | 0.181 | 0.801 | | | |
Mood modification (4) | 0.598 | 0.100 | 0.431 | 0.293 | 0.774 | | |
Environmental enhancement (5) | 0.572 | 0.527 | 0.391 | 0.525 | 0.480 | 0.756 | |
Social competition (6) | 0.551 | 0.580 | 0.098 | 0.591 | 0.172 | 0.546 | 0.743 |
Results of MANOVA
The results of MANOVA demonstrated that the factors differed across the three category levels of selfitis intensity (Wilks’ λ = 0.374;
f = 41.53 (12,784);
p < 0.001). The mean results relating to the mean difference of individual factors across intensity level (ANOVA) are shown in Table
6.Among the factors, subjective conformity was identified to differ extensively across the three intensity levels of selfitis followed by social competition and attention seeking. It was observed that all the factors significantly varied across the three categories of selfitis intensity. Table
7 shows the mean and standard deviation of the factors. Examining the mean scores shows that self-confidence and mood modification had the highest mean scores in borderline condition, subjective conformity had the highest mean score in the acute condition, and attention seeking, environmental enhancement, and social competition had the highest mean scores in the chronic condition. Table
8 shows the Scheffe’s post-hoc mean comparison of the factors across the three category levels. Among the comparisons, subjective conformity was observed to have the highest mean difference between the acute and borderline categories, and social competition was identified to have the highest mean difference between the borderline and chronic categories.
Table 6
Analysis of variance for the identified factors on the Selfitis Behavior Scale
Self-confidence | 3.67 | 17.334 | .001 | 0.080 | 1.000 |
Attention seeking | 3.49 | 43.619 | .001 | 0.180 | 1.000 |
Mood modification | 3.61 | 17.724 | .001 | 0.082 | 1.000 |
Environmental enhancement | 3.58 | 4.556 | .011 | 0.022 | 0.773 |
Subjective conformity | 3.04 | 78.112 | .001 | 0.282 | 1.000 |
Social competition | 3.64 | 57.956 | .001 | 0.226 | 1.000 |
Table 7
Analysis of variance for the identified factors on the Selfitis Behavior Scale
Self-confidence | 3.95*
| 0.526 | 3.54 | 0.802 | 3.48 | 0.751 |
Attention seeking | 3.08 | 0.823 | 3.53 | 0.817 | 4.00*
| 0.550 |
Mood modification | 3.89*
| 0.574 | 3.41 | 0.771 | 3.56 | 0.736 |
Environmental enhancement | 3.50 | 0.690 | 3.52 | 0.724 | 3.76*
| 0.778 |
Subjective conformity | 2.37 | 0.894 | 3.57*
| 0.775 | 3.09 | 0.829 |
Social competition | 3.24 | 0.768 | 3.62 | 0.632 | 4.20*
| 0.637 |
Table 8
Scheffe’s post-hoc mean differences across the three intensity categories in the Selfitis Behavior Scale
Self-confidence | Borderline | Acute | .415*
| .082 | .001 |
Chronic | .471*
| .093 | .001 |
Acute | Borderline | −.415*
| .082 | .001 |
Chronic | .056 | .089 | .529 |
Chronic | Borderline | −.471*
| .093 | .001 |
Acute | −.056 | .089 | .529 |
Attention seeking | Borderline | Acute | −.459*
| .088 | .001 |
Chronic | −.927*
| .100 | .001 |
Acute | Borderline | .459*
| .088 | .001 |
Chronic | −.468*
| .096 | .001 |
Chronic | Borderline | .927*
| .100 | .001 |
Acute | .468*
| .096 | .001 |
Mood modification | Borderline | Acute | .481*
| .082 | .001 |
chronic | .326*
| .092 | .001 |
Acute | Borderline | −.481*
| .082 | .001 |
Chronic | −.155 | .089 | .081 |
chronic | Borderline | −.326*
| .092 | .001 |
Acute | .155 | .089 | .081 |
Environmental enhancement | Borderline | Acute | −.019 | .085 | .821 |
Chronic | −.262*
| .095 | .006 |
Acute | Borderline | .019 | .085 | .821 |
Chronic | −.242*
| .092 | .009 |
chronic | Borderline | .262*
| .095 | .006 |
Acute | .242*
| .092 | .009 |
Subjective conformity | borderline | Acute | −1.206*
| .097 | .001 |
Chronic | −.728*
| .109 | .001 |
Acute | Borderline | 1.206*
| .097 | .001 |
Chronic | .478*
| .105 | .001 |
Chronic | Borderline | .728*
| .109 | .001 |
Acute | −.478*
| .105 | .001 |
Social competition | Borderline | Acute | −.389*
| .079 | .001 |
Chronic | −.963*
| .089 | .001 |
Acute | Borderline | .389*
| .079 | .001 |
Chronic | −.574*
| .086 | .001 |
Chronic | Borderline | .963*
| .089 | .001 |
Acute | .574*
| .086 | .001 |
Discussion
The present study explored the factors that underlie selfitis and developed a new psychometric scale—the Selfitis Behavior Scale (SBS). Using focus group interviews to generate scale components followed by statistical testing (using the dimension reduction technique), six components of selfitis were identified: environmental enhancement, social competition, attention seeking, mood modification, self-confidence, and social conformity. The MANOVA results confirmed that the six factors significantly differed across selfitis intensity level (i.e., borderline, acute, and chronic) in total as well as within the groups. The SBS appears to be a useful addition to the literature and will be helpful to future research examining the psychometric properties of selfitis. The Scheffe’s test identified that most of the high significant differences within-factor came from borderline and chronic intensity categories. This also validates that there is an appreciative deviation between the lowest and highest ranges of intensity categories. Selfitis is a new construct in which future researchers may investigate further in relation to selfitis addiction and/or compulsion. Future studies could therefore psychometrically investigate the SBS with specificity to intensity level. In the following sections, the importance of the six factors underlying selfitis is individually discussed.
Limitations, Future Research, and Conclusions
The study is not without its limitations. All the data were self-report and are subject to many well-known biases (including social desirability and memory recall). The sample was a self-selecting convenience sample of Indian students and therefore is non-representative of Indian or other populations and cultures. The vast majority of the sample (90%) was below the age of 25 years; therefore, future research should attempt to examine the selfitis across different age groups and populations using more representative samples.
Initially, the taking of selfies was considered as a fad activity, but its increasing engagement and importance given by industry and academics has established it as having a strong cultural importance—at least at the time of writing. Moreover, selfie-taking has become a major leisure activity with the help of enhanced social media functions. Improving technology along with universal connectivity via mobile devices has facilitated users to post, upload, and share their selfies via social media. Since the first paper on technological addictions (Griffiths
1995), researchers have investigated various facets of excess related to technology and its applications. As with internet addiction, the concepts of “selfitis” and “selfie addiction” started as a hoax, but recent research including the present paper has begun to empirically validate its existence.
The present research conducted focus group interviews to better understand the sub-components of selfitis. Using these data, the SBS was validated and the selfie-taking behavior was examined in relation to three intensity types (i.e., borderline, acute, and chronic conditions). The qualitative focus group data from participants strongly implied the presence of “selfie addiction” although the SBS does not specifically assess selfie addiction. Furthermore, through the analysis of the quantitative data, six factors underlying selfitis among participants were identified (i.e., self-confidence, attention seeking, mood modification, environmental enhancement, subjective conformity, and social competition). It was also demonstrated that the importance of these factors differed among those classed as borderline, acute, or chronic selfie-takers.
The present research is a novel addition to the research literature examining technology-related disorders. In addition to the psychological consequences (which may be both positive and negative), the present study provides important insights for practitioners and researchers. Although the present research is primarily exploratory in nature, the findings provide the basis for future empirical research. This study arguably validates the concept of “selfitis” and provides benchmark data for other researchers to investigate the concept more thoroughly and in different contexts. The concept of selfie-taking might evolve over time as technology advances, but the six identified factors that appear to underlie selfitis in the present study are potentially useful in understanding such human-computer interaction across mobile electronic devices. Further psychological research is needed into other factors that are likely to play a role in the acquisition, development and maintenance of selfitis including personality traits, motivations, cognition, and attitudes. Overall, the findings in the present paper demonstrate that the SBS appears to a reliable and valid instrument for assessing selfitis but that confirmatory studies are needed to validate the concept more rigorously.
Acknowledgements
This study was not funded by any organization.
Compliance with Ethical Standards
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.