Socializing and networking was transformed in the technological era by the introduction
of social networking sites (SNSs). These online sites contain an abundance of information
about individual preferences, interests, types, and frequency of social interactions,
etc. However, scientific studies that have utilized SNS activity data to aid our understanding
of mental health disorders are scarce. This is partly due to the practicalities of
accessing SNS data and methodological issues of large-scale data collection, but also
because the construct validity of SNS measures is unknown. By and large, the literature
to date has attempted to link various SNSs measures to various mental health symptomologies,
mostly collected using self-report measures rather than data generated by SNSs. Although
such research has demonstrated some preliminary and putative associations between
SNS activity and mental health measures, the current literature is still in its infancy
and arguably lacks rigor in design, offering limited insights into its theoretical
significance and plausibility. In this review, we will provide an account of the theoretical
importance of using data generated from SNSs in mental health research and provide
a brief overview of the literature published in this area to date.
Introduction
The world of socializing and networking was reinvented in the technological era by
the introduction of online SNSs and other forms of digital social media such as MySpace,
Bebo, Hi5, Facebook, Twitter, YouTube, Google, Instagram, and Vine. The shift into
the digital online environment has left social networking users with digital footprints
that generate a relatively unique set of identifiers, both in online and offline worlds.
In 2012, Facebook (one such SNS) reached a staggering one billion monthly users (1)
meaning that approximately 1/7th of the world’s population were regular Facebook users.
Usage is especially high among young people. Livingstone et al. (2) reported that
26% of 9- to 10-year-olds, 49% of 11- to 12-year-olds, 73% of 13- to 14-year-olds,
and 82% of 15- to 16-year-olds have their own profile on an SNS. Moreover, 51% of
13- to 18-year-olds log on to their online social networking profile at least once
a day, 34% log on more than once a day, and 22% check it more than 10 times per day
(3). These figures provide insight into the extremely popular online culture of SNSs,
especially among young people.
Why Study Social Networking Sites in Mental Health Research?
The extraordinary popularity of online SNSs does not alone warrant their use for research
purposes; therefore, it is important to understand what SNSs add to existing methodology
in the field of mental health research. SNS activity logs leave behind a digital trail
of quantifiable and objective data that can be arguably valuable to researchers. It
may be thought of as analogous or complementary to observing individuals in their
natural environment more so than conventional self-report measures of present and
past behaviors, which may result in reporting bias in adolescents (4). Such biases
could be in the form of reporting distorted behaviors, which are in fact demand characteristics,
which may be less problematic in large datasets and also in digital trails of SNSs.
As such, creating and maintaining friendships, interactions with friends, cyber-bullying,
specific interests, etc., could potentially be assessed more directly and with greater
accuracy and precision using online information. Issues surrounding self-report methods
such as test–retest reliability may be overcome by using online activity logs. Some
SNSs enable users to have a virtual existence with personal sociodemographic details
(e.g., name, age, current, and previous towns/countries of residence all available
to view). Such advances in social networking and internet technology offer mental
health researchers new tools and opportunities for large-scale data collection, analysis,
and interpretation that were previously not possible.
Several studies have shown evidence that social network profiles convey fairly accurate
personality portrayals rather than idealized virtual identities of profile owners
(5). Most typically, friendships are formed in an offline-to-online sequence; peer-reviewed
statements about their friend’s offline interests and values support the accuracy
of their online identities [reviewed by Wilson et al. (6)]. While arguably some profile
users might engage in self-enhancement and narcissistic self-promotion, research has
shown that independent raters can accurately detect such profiles as narcissistic
behaviors (7). Facebook recently reported that 8.7% of Facebook user profiles were
“fake” (8). However, only ~1.5% were actually defined as “undesirable profiles” (i.e.,
profiles that breached Facebook terms and conditions). The remaining fake profiles
included such things as duplicate profiles for business and organization purposes
or to create non-human profiles (e.g., for family pets). The 1.5% “undesirable profiles”
are generally used to send spam messages or corrupted content to other FB users. Although
this percentage of undesirable fake profiles is low, one possible way to minimize
this occurrence is to establish study recruitment procedures that are initially offline
and then acquire the verified Facebook users online profile data.
The sheer scale of SNSs popularity can be considered as a strong scientific asset
as it provides a high dimensional and dense log of behavioral information, which can
enhance the power to detect small effects of complex behaviors associated with mental
illness. SNSs can capture additional and unique information of how people lead their
lives, offline and online. Both sets of experiences could be very similar for some
individuals but very different for others. It may well be that data collected from
SNSs is a different method of measuring the same behaviors; alternatively, a whole
range of new behaviors could be observed. Previous technological advances such as
the television, music players, and games consoles have all been very passive in their
nature and it was difficult to derive data from them, whereas SNSs require some level
of participation from the user. Moreover, it may not be enough to simply look at online
data in general (e.g., Google searches) because families often share a computer and
so individual differences may not be detected. Online SNSs allow for this “statistical
noise” in the data be reduced and allow one-to-one mapping of individuals online and
offline behavior. The interaction between online and offline behaviors may also be
of interest to researchers and provide unique insights into understanding mental health.
Another advantage of measuring data from SNSs is the rapid and dense collection of
data within extraordinarily smaller timescales that are highly cost-effective [e.g.,
methodology used by Kosinski et al. (9)].
Given that a large proportion of lifetime mental health problems develop in adolescence
and young adulthood (10), early intervention and prevention that are targeted at young
people would provide personal and economic benefits. Early detection and assessment
of mental illnesses would help to reduce poorer life outcomes (10). Early intervention
can have a significant impact on those who experience mental health problems, whether
this come from discoveries of early biomarkers or emerging deviations in behaviors.
Emerging research indicates that intervening early can interrupt the negative course
of some mental illnesses and may, in some cases, lessen long-term disability. Changes
in online behaviors may offer novel insights and naturalistic measures, “red flags”
to inform prevention and early detection strategies.
What Work has been Done so Far?
Although there has been some limited and preliminary work on the relationship between
SNSs and mental health, there appears to be a scarcity of mental health literature
that extracts large-scale data from SNSs in an attempt to better understand mental
health disorders. Literature does exist that employs self-report questionnaires to
gather data about SNS usage. This is important because it provides rationale for future
research using data generated by SNSs. SNSs have the potential to produce data on
hundreds of thousands, if not millions of people from different parts of the world.
Although such large datasets have the potential to produce spurious associations [e.g.,
(11)], these can be dealt with using hypothesis-driven statistical analysis. Furthermore,
SNSs usage data can be easily shared with other researchers and so would move forward
the drive to share datasets in an effort to replicate research and reduce the number
of false positives.
Using relatively small sample sizes (n > 500) and self-report methods of Facebook
use, some research studies have found that various parameters on Facebook (e.g., friend
count, social support, and time spent on Facebook) related to depressive symptoms
or well-being (12–15); although this is not entirely supported (16). Researchers have
also found that various Facebook parameters such as status updates were able to predict
depressive symptoms (17–19). Additionally, Good et al. (20) found that looking back
over old posts and photos on a user’s own profile had a positive effect on well-being
and that reminiscing had more of a positive effect on well-being for those who had
mental health problems in the past compared to those with no previous mental health
problems. More recently, Frison and Eggermont (21) found that passive Facebook use
(consuming other peoples information without interacting with them) was associated
with depressed mood in girls and active public Facebook use (interacting with other
friends such that the interactions are visible to others, e.g., status updates) was
associated with depressed mood in boys.
Other than the predominantly cross-sectional studies, a limited number of research
studies use stronger research designs such as longitudinal and experimental. Longitudinal
studies have an advantage over cross-sectional work in that they allow for an exploration
into change over time, causality, and the association between variables at different
time points. For example, Kross et al. (22), over a period of 2 weeks, investigated
the relationship between Facebook use and subjective well-being five times a day.
They found that increased use of Facebook at one time-point predicted lower well-being
at the next time point. Also by text messaging participants, Verduyn et al. (23) found
that passive Facebook use (consuming information from Facebook without interacting
with other users) was associated with lower well-being. Furthermore, an experimental
study by Sagioglou and Greitemeyer (24) asked participants about their well-being
immediately after Facebook use and found that longer use predicted lower mood. This
finding is particularly relevant as it implies causality as Facebook use was measured
directly prior to well-being.
There is considerable variability in the quality of the work that has been produced
using self-report methods of SNS usage and mental health. One of the main reasons
for this is due to the lack of validity in some of the reported measures. For example,
Burke et al. (25) found that there was a significant correlation between self-report
friend count and actual friend count (r = 0.96) and also between self-report time
spent on Facebook and actual time spent on Facebook (r = 0.45). Junco (26) also investigated
the difference between actual Facebook use (monitored by computer monitoring software)
and self-reported use. Although time spent on Facebook was correlated for the two
measures (r = 0.6), there was a significant difference between them. That is, participants
overestimated the time spent on Facebook (mean difference = 123 min per day). It may
be that self-report is a more reliable measure for simple measures such as friend
count but when looking at more complex measures it becomes less reliable.
Very few studies have taken a computational approach to utilizing Facebook parameters.
Kosinski et al. (9) used the Facebook “like” feature, which allows users to specify
what he/she likes or has an interest in (e.g., type of music, music bands, movies,
interests, past times, places, etc.). Participants consented for researchers to extract
data about their profile that is automatically collected by Facebook. These researchers
found that what someone “likes” on Facebook can be used to predict, with a relatively
high degree of accuracy, his/her sexual orientation (0.88), race (0.95), and voting
preferences (0.85). There are only a few studies in mental health research, which
have used more complex online behavior traits generated by SNS data. For example,
Burke et al. (25) and Burke et al. (27) discuss the concept of social support and
how it can be measured through Facebook behaviors such as the type and frequency of
the content produced and consumed. This is important because increased social support
has been linked to a decrease in depressive symptoms [e.g., Brown et al. (28)]. An
interesting example of online data (not generated by SNSs) is given by Ayers et al.
(29) who found that Google searches for certain mental health disorders varied by
the seasons of the year.
It is not possible to reach any conclusions based on the limited amount of literature
in relation to mental health research. However, this is a very important area to examine
in much greater detail. Lessons can be learned from Facebook research in psychological
and social science contexts and used in designs for mental health research.
Summary
Online SNSs are increasingly popular in people’s everyday lives and as they leave
behind a cumulative digital trail of activity data they should be of considerable
interest to mental health researchers. There is a growing body of literature around
the association between behaviors on SNSs and mental health, but research that uses
activity history on SNSs and how that links to mental health is scarce. This activity
log of information is important because it has the potential to provide researchers
with large amounts of data, which is not only easy to obtain but less dependent on
a research funding as the data collection costs are vastly smaller. The problem with
these large datasets is that self-report studies that should inform hypotheses and
research questions are often poorly designed. Moreover, due to the novelty of the
data collection method, there are unanswered questions about its construct validity
and what research using this method can add to theoretical constructs in the field.
Conflict of Interest Statement
The authors declare that the research was conducted in the absence of any commercial
or financial relationships that could be construed as a potential conflict of interest.