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Social media facilitates the formation of identity bubbles that reinforce shared identities, social homophily, and reliance on the information shared within the bubbles. Currently, researchers need more social psychological measures to assess how people perceive their relationships with online networks and information shared on social media. This article reports the development and Finnish and English validations of the identity bubble reinforcement scale (IBRS), consisting of subscales on social identification, homophily, and information bias. Studies 1 and 2 (N = 1200, N = 160; age 15-25 years) validated the 6-item measure (IBRS-6) in Finland and Study 3 replicated the results with both 6-item and 9-item (IBRS-9) measures in the United States (N = 501, age 18-25 years). Across all 3 studies and 2 countries, the IBRS showed good construct validity and correlated positively with social media use and group-behavior measures. The IBRS is an applicable measure for social media group behavior, and its subscales can also be used separately.
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Shared identity and shared information in social media:
development and validation of the identity bubble
reinforcement scale
Markus Kaakinen, Anu Sirola, Iina Savolainen, and Atte Oksanen
Faculty of Social Sciences, University of Tampere, Tampere, Finland
ABSTRACT
Social media facilitates the formation of identity bubbles that
reinforce shared identities, social homophily, and reliance on
the information shared within the bubbles. Currently, research-
ers need more social psychological measures to assess how
people perceive their relationships with online networks and
information shared on social media. This article reports the
development and Finnish and English validations of the iden-
tity bubble reinforcement scale (IBRS), consisting of subscales
on social identification, homophily, and information bias.
Studies 1 and 2 (N= 1200, N= 160; age 1525 years) validated
the 6-item measure (IBRS-6) in Finland and Study 3 replicated
the results with both 6-item and 9-item (IBRS-9) measures in
the United States (N= 501, age 1825 years). Across all 3
studies and 2 countries, the IBRS showed good construct valid-
ity and correlated positively with social media use and group-
behavior measures. The IBRS is an applicable measure for social
media group behavior, and its subscales can also be used
separately.
The development of social media has quickly transformed social interactions
taking place every day. The concept of social media covers diverse types of
Web 2.0-based technologies consisting of user-generated content (Kaplan &
Haenlein, 2010). Different social networking sites are prominent examples of
social media platforms, which allow individuals to interact with other users,
as well as provide tools to maintain and form social networks (Boyd &
Ellison, 2007; Wilson, Gosling, & Graham, 2012). With the introduction of
social media sites and platforms, such as Facebook, Twitter, and Instagram,
people are able to reach other like-minded people at unprecedented rates.
Social media thus responds to a basic human need to interact and seek
companionship (Baumeister & Leary, 1995).
The social selectivity and social media platformsfiltering technologies can
lead to formation of psychosocial bubbles that limit the diversity of social
contacts and information exposure online. In earlier literature, these bubbles
CONTACT Atte Oksanen atte.oksanen@uta.fi Faculty of Social Sciences, University of Tampere, Tampere
33014, Finland
Supplemental data for this article can be accessed here.
MEDIA PSYCHOLOGY
https://doi.org/10.1080/15213269.2018.1544910
© 2018 Taylor & Francis Group, LLC
are referred to as echo chambers (i.e., social interaction limited to like-
minded communication; see, e.g., Zollo et al., 2017)orfilter bubbles (i.e.,
customized online reality due to algorithmic filtering technology; Pariser,
2011), for example. These perspectives were grounded on computer science
and on structural measures of online behavior and social networks.
Currently, researchers need more social psychological measures to assess
how people perceive their relationships with online networks and informa-
tion shared on social media. The identity bubble reinforcement model
(IBRM) by Keipi, Näsi, Oksanen, and Räsänen (2017) was the first attempt
to integrate a social psychological perspective into the discussion of social
media bubbles. In contrast to previous attempts in computer science (e.g.,
Pariser, 2011; Zollo et al., 2017), the IBRM sought more specifically to
understand human motivation and social psychological aspects of the
phenomenon.
According to the IBRM, the expanded possibilities for communication and
social network formation in social media allow individuals to search for social
interactions with others who share and validate their identities (Keipi et al.,
2017). This identity-driven online use can lead to identity bubbles, which are
manifested in three elements: identification with online social networks (social
identification), a tendency to interact with like-minded others (homophily),
and reliance on like-minded information on social media (information bias).
Within social media, these three elements are correlated and together they
reflect the identity bubble reinforcement process.
This theorization is supported by other research findings on online social
networks and their importance in shaping interaction, participation, and
information consumption online (Bakshy, Messing, & Adamic, 2015; Jost
et al., 2018). Furthermore, social media, indeed, tends to gather users into
social cliques of similar individuals. Earlier computational analyses have
shown how these social aggregates are formed around shared group member-
ships and ideologies (i.e., political factions) and they significantly influence
the way users engage with information online (Bakshy et al., 2015; Bessi et al.,
2015; Boutyline & Willer, 2017; Del Vicario et al., 2016; Zollo et al., 2017).
The IBRM, however, relies on the perspective that individual differences
contribute to the tendency to be involved in these like-minded social cliques
or bubbles.
Social identification
Over the past century, researchers in the social psychological research tradi-
tion have extensively studied group-forming behavior. People not only form
groups very quickly, even on minimal bases, but they also tend to prioritize
their own in-group. These basic ideas were already very well established in
Muzafer Sherifs experiments in boyssummer camps (Sherif, Harvey, White,
2M. KAAKINEN ET AL.
Hood, & Sherif, 1961). Later, Henri Tajfel and his colleagues established
a series of minimal group experiments, noting that it takes very little for
people to start identifying with their group and favoring it over the perceived
out-group (Tajfel, Billig, Bundy, & Flament, 1971).
The minimal group experiments led to the formation of social identity
theory proposed by Henri Tajfel and his colleague John C. Turner in the late
1970s (Tajfel & Turner, 1979; Turner & Reynolds, 2010). Social identity theory
proposes that individuals internalize a group membership so that it becomes
an aspect of their self-concept, thus making identity dependent upon connect-
edness to relevant social groups (Tajfel & Turner, 1979). An essential part of
a social identity approach is self-categorization theory, which emphasizes how
individuals cognitively describe themselves in terms of a group identity, rather
than a personal identity; that is, become depersonalized (Turner, 1985;Turner,
Oakes, Haslam, & McGarty, 1994).
The role of online social identification has been shown in a number of
studies on social media. Younger generations of online users have been
shown to identify with online groups and communities, sometimes even
more strongly than with traditional offline peer groups (Lehdonvirta &
Räsänen, 2011; Mikal, Rice, Kent, & Uchino, 2016). Those who strongly
identify with their online social networks also tend to social-categorize, in
other words, they perceive themselves as online community members instead
of having a personalized identity, in online interaction (Guo & Li, 2016; Jans,
Leach, Garcia, & Postmes, 2015).
Chung (2013) found that preferring online groups over offline contacts is
especially prominent among those who are dissatisfied with offline relations
and who tend to form intimate relationships online. Online social bonds and
strong social identification with online communities are consequently related
to increased use of the Internet (Flanagin, Hocevar, & Samahito, 2014; Mikal
et al., 2016). Social identification online can also be problematic because
people tend to feel obligated to increase their social media use in response to
increased group-level social activity, which may lead to compulsive use of the
Internet (Turel, 2015; Turel & Osatuyi, 2017).
Social identification has been operationalized and measured in various
manners (for review, see Leach, van Zomeren, Zebel, Vliek, & Ouwerkerk,
2008). In a widely referenced measure created by Leach et al. (2008), aspects
of social identification are considered under the dimensions of self-
investment and self-definition. The self-investment dimension of social iden-
tification consists of in-group-related solidarity, satisfaction, and centrality,
whereas the self-description dimension includes individual self-stereotyping
and in-group homogeneity subdimensions. Of these dimensions, the self-
investment dimension can be seen as closer to the traditional social identity
theory approach, whereas self-definition resembles self-categorization (Leach
et al., 2008; Postmes, Haslam, & Jans, 2013).
MEDIA PSYCHOLOGY 3
Homophily
People are likely to form social relationships with others who are similar to
them. This is called homophily (McPherson, Smith-Lovin, & Cook, 2001).
Homophily is based on shared background factors such as social or economic
status or geographic region, or attitudinal factors such as similar thoughts or
values (McCroskey, McCroskey, & Richmond, 2006). Homophily markedly
shapes human social networks (McPherson et al., 2001), and similar indivi-
duals seem to form social relationships that are more stable over time (Hartl,
Laursen, & Cillessen, 2015). Homophily in social relations tends to increase
when possibilities for social selectivity increase (Bahns, Pickett, & Crandal,
2011). Because social media provides new possibilities for social network
formation and management, it is not surprising that people often choose to
interact with similar others online (Kang & Chung, 2017; Oksanen, Hawdon,
& Räsänen, 2014). In addition, discussions taking place in social media often
emerge around shared ideologies or emotional valence (Himelboim, Smith, &
Shneiderman, 2013; Himelboim et al., 2016).
Personal networks on social media have an impact on what kind of
content and information users encounter online, even though the algorithmic
filtering technology of online platforms also plays a role (Bakshy et al., 2015;
Himelboim et al., 2013). Because the online experience is modified by ones
social networks, homophilic social relations lead to reduced informational
diversity. Reduced diversity may contribute to the formation of homophilic
social aggregates, or echo chambers, which reinforce polarization and con-
flicts between different social and ideological cliques online (Boutyline &
Willer, 2017; Densley & Peterson, 2017; Zollo et al., 2017).
Perceived similarity also affects the way people interact online. The per-
ception of congruence between personally held and public opinion online
makes people more willing to participate in interaction and share their views
(Gearhart & Zhang, 2015; Liu, Rui, & Cui, 2017; Zerback & Fawzi, 2017).
Thus, it is likely that social homophily online is related to increased activity
on social media.
Homophily is measured as homogeneity in individual social networks
(McPherson et al., 2001;Robinson&Aikens,2010). McCroskey, Richmond, and
Daly (1975)createdamultidimensionalperceivedhomophilyscale.Intheper-
ceived homophily scale, homophily is assessed as individual perceptions of simi-
larity in terms of background and attitudinal factors (McCroskey et al., 2006).
Information bias
The third aspect of the IBRM is information bias. It consists of selective
exposure to opinion-congruent information and the perception that infor-
mation is reliable on social media. Both of these are influenced by social and
4M. KAAKINEN ET AL.
cognitive factors that shape the way people approach information and eval-
uate its credibility. As indicated in the motivated reasoning literature, indi-
viduals are more motivated to confirm their established attitudes than to
reach accurate conclusions (Westen, Blagov, Harenski, Kilts, & Hamann,
2006; Ziva, 1990). In addition, people tend to overestimate the popularity
of their attitudes and downplay opposing evidence (Kuru, Pasek, & Traugott,
2017).
Social media and technology enable high selectivity over consumed informa-
tion. Thus, social media is likely to enhance motivated reasoning, exposure to
like-minded information, and the tendency to ignore attitude-threatening infor-
mation. In addition, motivated reasoning can be accentuated by online plat-
formsalgorithmic filtering, which adjusts the social media environment
according to usersformer interests and preferences (Pariser, 2011). Social
media provides users practically unlimited information and the ability to make
decisions very rapidly. In these circumstances, people tend to trust other peoples
opinions and evaluations of information credibility (Flanagin, 2017; Hocevar,
Flanagin, & Metzger, 2014; Metzger, Flanagin, & Medders, 2010). Series of
experiments have shown that social endorsements play a major role in how
people consume social media content (Messing & Westwood, 2014).
Social identification and homophily both contribute to perceptions of online
information credibility. People who strongly identify with their social media
networks are likely to see online information as more reliable. In-group mem-
bers are in general seen as trustworthy and competent (see, e.g., Leach, Ellemers,
& Barreto, 2007). In addition, information coming from ones in-group or
similar others is perceived as more reliable (Flanagin, 2017;Flanaginetal.,
2014;Hocevaretal.,2014; Shin, Van der Heide, Beyea, Dai, & Prchal, 2017).
Furthermore, people are, in general, prone to overestimate the degree to
which others, and especially other in-group members, agree with their thoughts
and behaviors (Holtz & Miller, 1985; Jones, 2004; Rogers, Moore, & Norton,
2017; Ross, Greene, & House, 1977). According to the theory of in-group
projection (Mummendey & Wenzel, 1999), individuals tend to project further
the characteristics of their in-group in relation to more inclusive superordinate
groups (Imhoff, Dotsch, Bianchi, Banse, & Wigboldus, 2011;Mummendey&
Wenzel, 1999). On social media, these social psychological mechanisms indicate
that users would overestimate the perceived homogeneity of their online in-
groups and further project group characteristics to a wider group of social media
users, leading to an inflated perception of similarity.
Together the perceived credibility and selective exposure elements of
information bias lead to online experiences in which users strongly rely on
like-minded social media communication. Thus, our presumption is that
social media information bias is manifested in individual tendencies to be
mainly exposed to information that agrees with ones established views and
to trust and value social media as a source of information.
MEDIA PSYCHOLOGY 5
Research overview
The aim of this series of studies was to develop and validate the IBRS. The
scale was developed during three studies that took place in 2017. The scale
was theoretically based on both the IBRM (Keipi et al., 2017) and empirical
and theoretical perspectives established within social psychology (e.g., Leach
et al., 2008; McCroskey et al., 2006) and research on computer-mediated
communication (Bakshy et al., 2015; Boutyline & Willer, 2017; Flanagin,
2017; Flanagin et al., 2014; Zollo et al., 2017). It measures the cognitive
process of relating oneself with online social networks.
Based on reviewed literature, we hypothesized that the IBRS will consist of
three correlated factors: social identification, homophily, and information bias.
Furthermore, these three social elements reflect a second-order factor (see
Burke Jarvis, MacKenzie, & Podsakoff, 2003) of an identity bubble (i.e.,
a personal tendency to relate oneself with online social cliques). Identity
bubbles can be viewed as self-reinforcing, as those who strongly relate them-
selves with online social cliques are also more likely to display social identifica-
tion, homophily, and reliance on similar-minded information in their future
behavior (Keipi et al., 2017; see also Hogg & Rinella, 2018;Jostetal.,2018).
As reviewed, high involvement in online social networks is related to
increased (Mikal et al., 2016), and even to compulsive (Turel, 2015; Turel
& Osatuyi, 2017), Internet use, because those most involved in online com-
munities often feel obligated to respond to othersactivity. Thus, we hypothe-
sized that the IBRS should be positively associated with increased social
media activity and compulsive Internet use. In addition, the IBRS should
be positively associated with reciprocity in group activity (Mikal et al., 2016;
Turel, 2015; Turel & Osatuyi, 2017) and with other forms of group behavior;
that is, self-categorization (Guo & Li, 2016; Jans et al., 2015) and group
influence (Flanagin, 2017; Flanagin et al., 2014; Hocevar et al., 2014; Shin
et al., 2017) in online group experiments. Furthermore, these forms of online
group behavior should be correlated with the IBRS; a users shared group
membership with similar others is especially expected to be emphasized.
To test our hypotheses, we first investigated the validity and reliability of the
IBRS with three samples from two countries (Finland & the United States).
Then we analyzed the hypothesized associations between the IBRS and our
validation variables. The country selection was based on the fact that both the
United States and Finland are highly technologically developed countries, but at
the same time, societally and culturally distinct. Although some authors of
earlier studies have pointed out major cultural differences (the Finnish being
quieter and more reserved than their American counterparts; e.g., Sallinen-
Kuparinen, McCroskey, & Richmond, 1991), other authors of social media
research have also pointed out major similarities, especially in youth behavior
and how social media is used among adolescents and young adults, and what
6M. KAAKINEN ET AL.
kind of factors explain risks encountered online (Keipi et al., 2017; Näsi et al.,
2014). Theoretically and empirically, it was important to show whether the
IBRS could be validated in different cultural settings.
Method
Participants
We first conducted a small pilot study by collecting a convenience sample (N
= 60, M
age
= 24.17, SD
age
= 3.57, 86% female) consisting of university
students to construct and validate the IBRS in Finnish. Studies 1 and 2
were targeted to Finnish young people aged 15 to 25. Participants of Study
1 were recruited from a pool of volunteer respondents administrated by
Survey Sampling International in MarchApril 2017. This demographically
balanced sample (N= 1,200, M
age
= 21.29, SD
age
= 2.85, 50% female) matches
the Finnish population aged 15 to 25 in terms of age, gender, and living area.
Participants of Study 2 were recruited from Finnish discussion forums and
social networking sites in AprilJune 2017 (N= 160, M
age
= 22.48, SD
age
= 2.58, 57% female).
Study 3 was targeted to respondents in Amazons Mechanical Turk, which
enables collection of high-quality data at low costs. Mechanical Turk data
have been increasingly used as a source of research participants in the social
sciences and psychology (Buhrmester, Kwang, & Gosling, 2011; Paolacci &
Chandler, 2014). Study 3 participants were 18- to 25-year-olds located in the
United States (N= 501, M
age
= 22.84, SD
age
= 1.85). The participants were
from 48 states, with the highest response rates coming from California
(10.16%) and New York (9.16%).
Procedure
YouGamble online surveys measured social media use and online behavior
from a social psychological perspective. The surveys were part of a Finnish
research project on gambling among young people that expanded to
a comparative cross-national project. In this article, we only report data
and measures that are relevant for the validation of the IBRS.
The study format was approved by The Academic Ethics Committee of the
Tampere Region. All participants agreed voluntarily to take part in the
YouGamble online surveys regarding gambling, and they were informed
about the aims of the study.
The surveys were conducted using LimeSurvey software, and they were
optimized for computers and mobile devices. A pilot study was conducted
before Study 1 to get comments from the Finnish University students on the
IBRS questions. All the students filled out the question form anonymously,
MEDIA PSYCHOLOGY 7
and they were given an opportunity to comment on survey questions. We
also discussed questions with them in a group discussion.
Studies 13 were identical in layout and order of questions. Study 2 replicated
the Study 1 questionnaire, but four additional questions were added. Study 3 was
conducted for the purpose of validating the IBRS in English. Thus, it was
significantly shorter because it only included measures that were needed for
the validation. These validation measures were the same as those in Studies 1
and 2, and some additional questions were used as attention checks. The median
response time for Study 1 was 15.50 min and for Study 2 it was 17.83 min. The
shorter Study 3 had a median response time of 6.27 min.
In the first section of the surveys, respondents were asked about their back-
ground factors (e.g., age & gender) and their habits of social media use. After
that, respondents were assigned to a vignette experiment with a 2 ×2×2×2
factorial design. The design included one two-level between-subject factor
(group condition or control condition) and three two-level within-subject
factors (majority opinion, stance on gambling, and used narration).
In the vignette experiment, respondents were asked to evaluate how they
would react (i.e., like, dislike, or not react at all) toward the manipulated
gambling-related social media content presented to them in the vignette
scenarios. In addition, respondents were presented with six items in which
they were asked to indicate how interesting the content would appear to them
in a real social media situation, such as How likely would you find the
message interestingor How likely would you share the link in social
media.The answer options had a scale of 1 (not at all likely)to10(very likely).
As a between-subject factor, respondents were randomly assigned to two
groups for the vignette experiment. For the salient group identity condition,
respondents were told they were assigned into a Group C consisting of
similar respondents, based on their answers to previous questions. Those in
the control condition were given no group information.
In case of within-subject factors, we manipulated the majority opinion,
expressed stance on gambling, and the used narration in the vignettes. In the
case of majority opinion, respondents were shown a manipulated distribution
of other respondentsearlier reactions. In half of the vignettes, a majority
(about 85%) had liked the content, whereas in the other half the majority had
disliked the content. The distribution of minority opinion (likes or dislikes)
in the vignettes was about 13%, and for those who had not stated an opinion
(no reaction) it was about 2%. For those in the salient identity condition, this
distribution was framed as their in-group membersearlier reactions.
We also manipulated the expressed stance on gambling (pro or anti) in the
vignettes. Half of the vignettes expressed a positive stance on gambling (i.e.,
discussed positive gambling-related phenomena), whereas the other half had
a negative stance (i.e., discussed negative gambling-related phenomena). In
case of the used narration in the vignette contents, half of the vignettes were
8M. KAAKINEN ET AL.
manipulated to utilize a subjective experience-driven argumentation, whereas
the other half utilized an objective fact-driven argumentation. The exact
manipulations of the within-subject factors are presented in Table A1 (sup-
plementary material).
The 2 ×2×2 within-subject factorial design resulted in eight vignette
scenarios, of which each participant was shown four. The factorial structure
was designed so that each type of content (pro/anti, experience-driven/fact-
driven) was liked and disliked once (see Atzmüller & Steiner, 2010). Thus,
the group did not favor any form of gambling orientation or narration.
Corresponding measures and controls
IBRS
Based on the review of literature and existing instruments concerning the
IBRM, social identity, homophily, and information biases, 16 items were
created to measure the three dimensions of IBRS (Table A2 in supplementary
material). Many of the generated items were adapted from earlier measures, but
new items were also formulated on the basis of reviewed literature. Created
items were discussed and modified within the author group to reach consensus
and to enhance the face validity of created measures. All items had a scale
ranging from 1 (does not describe me at all)to10(describes me completely). In
Study 3, the six-item version of IBRS (IBRS-6) was extended with three addi-
tional items (IBRS-9) to facilitate using each subdimension as separate mea-
sures. The three additional items were formulated (one for each IBRS
dimension) on the basis of earlier measures and reviewed literature.
Social media activity
Social media activity was measured with two items in which respondents were
asked to indicate how often they shared content on social media and how often
they posted pictures of themselves on social media. Both items had the
following reply options: 0 = never,1=less than once a year,2=at least
once a year,3=at least once a month,4=more than once a month,5=once
aweek,6=more than once a week,and7=daily. The Cronbachsalpha
coefficients for these items were .76 in Study 1, .83 in Study 2, and .71 in Study
3, and they were summed up as a count variable for further analyses (Table 1).
Compulsive Internet use
Compulsive Internet use was measured with the compulsive Internet use
scale (Meerkerk, Van Den Eijnden, Vermulst, & Garretsen, 2009). This
measure consists of 14 items concerning excessive Internet use with
responses ranging from 0 (never)to4(very often) with a higher score
indicating excessive Internet use. All items in the measure were summed
up as a count variable (Table 1).
MEDIA PSYCHOLOGY 9
Self-categorization
The self-categorization measure was adapted from Leach et al.s(2008) group-
level self-definition scale. The measure was included in Studies 2 and 3 after the
vignette experiment (self-categorization was not measured in Study 1). The
measure consists of components of individual self-stereotyping and in-group
homogeneity. Individual self-stereotyping was measured with two items: (a)
I have a lot in common with the average group member (or average respondent
in control condition), and (b) I am similar to the average group member. In-
group homogeneity was also measured with two items: (a) Group members have
a lot in common with each other, and (b) group members are very similar to
each other. All the items had a response range from 1 (strongly disagree) to 10
(strongly agree). The scale had good internal consistency (Cronbachs alpha = .89
in Study 2 and .87 in Study 3), and items were summed up to a count variable for
further analyses (Table 1). The original self-categorization measure by Leach
et al. (2008), however, is two-dimensional. This should be noted even though the
scale showed good internal consistency.
Group influence and responding to group activity
Group influence and responding to group activity measures were derived
from the vignette experiment (Table 1). After each vignette, respondents
Table 1. Descriptive statistics on measures.
Study 1 Study 2 Study 3
Variables Range M SD M SD M SD
Continuous variables
IBRS-6 660 27.79 9.97 27.57 10.01 35.85 10.04
Social identification 220 10.60 4.92 11.32 5.11 11.56 4.53
Homophily 220 9.09 4.20 8.91 4.50 14.06 3.78
Information bias 220 8.10 3.56 7.34 3.45 10.24 3.90
IBRS-9 990 . . . . 54.88 14.90
Social identification 330 . . . . 17.40 6.67
Homophily 330 . . . . 20.87 5.49
Information bias 330 . . . . 17.06 5.14
Social media activity 014 5.25 3.08 5.42 3.37 7.00 3.22
Compulsive Internet use 056 18.79 11.13 18.15 11.30 22.08 12.14
Self-categorization
Group condition 440 . . 16.52 7.60 19.97 8.24
Control condition 440 . . 15.96 7.61 19.86 7.84
Group influence
Group condition 108108 2.14 11.95 0.94 9.77 3.07 13.85
Control condition 108108 1.44 10.56 1.12 10.59 3.26 12.64
Responding to activity
Group condition 04 2.17 1.57 1.76 1.57 2.43 1.46
Control condition 04 1.90 1.60 1.81 1.62 2.55 1.44
Categorical variables Coding n % n % n %
Experimental condition
Control 0 563 46.92 85 53.12 252 50.20
Group 1 637 53.08 75 46.88 249 49.80
Note. The descriptive analyses are based on the following sample sizes: Study 1: N= 1,200, Study 2: N= 160,
Study 3: N= 501; M = mean. SD = standard deviation. IBRS = identity bubble reinforcement scale.
10 M. KAAKINEN ET AL.
were asked about the levels of their interest toward the presented vignette
content with six survey items (see the procedure section for further details).
All items had a scale ranging from 1 to 10, and these questions were summed
up to a composite variable. The within-subject effect of group influence was
calculated as the level of interest toward content that other group members
(respondents in the control condition) had liked, minus the interest in
vignettes that others had disliked (for a similar calculation, see Atzmüller &
Steiner, 2010). Thus, a higher value indicated higher interest in content that
others had evaluated in a positive manner. Responding to a group activity
was calculated as a sum of all responses (likes or dislikes) to presented
vignettes. In all vignettes, other group members (or respondents) were
shown as highly active in terms of liking or disliking the content, with only
a small minority (about 2%) not indicating their positive or negative opi-
nions. Thus, a small number of given responses indicated low reciprocity to
othersactivity.
Statistical techniques
In the pilot study, principal component analysis was used to reduce the
number of items for the final scale. In the analysis, the number of
components was chosen on the basis of theory, eigenvalues (> 1), and
a scree plot test (Cattell, 1966). An oblique rotation (promax) was then
conducted with the suggested number of components. The item reduction
was based on face validity, item loadings, explained variance, and interitem
correlations.
In Studies 13, the construct validity of the IBRS was assessed by con-
ducting confirmatory factor analysis (CFA) with maximum likelihood esti-
mation. In the hypothesized three-factor models, all items were allowed to
load solely on the hypothesized factor, and error terms were not allowed to
correlate with each other. In all models, the three hypothesized IBRS factors
were allowed to correlate with each other.
To assess the fit of estimated CFA models, we report widely used fit
indices including root mean squared error of approximation (RMSEA)
with 90% confidence interval, standardized root mean squared residual
(SRMR), comparative fit index (CFI), and Tucker-Lewis index (TLI) esti-
mates (Table A3 in supplementary materials). Cut-off criteria used for these
estimates include values of .06 for RMSEA, .08 for SRMR, and .95 for CFI
and TLI, as suggested by Hu and Bentler (1999). In addition, the χ
2
statistic,
along with degrees of freedom and corresponding significance test, are
reported. However, these measures are hard to interpret because they are
highly dependent on sample size (Hu & Bentler, 1999). For model compar-
ison, we also estimated competing CFA models with a one-factor solution (all
items loading on one general factor) and three different two-factor solutions
MEDIA PSYCHOLOGY 11
(with one original factor retained and the remaining two combined). We also
report the reliability of the IBRS scales and their subscales (Table A4 in
supplementary materials) as Spearman-Brown coefficients for two-item sub-
scales (see Eisinga, Te Grotenhuis, & Pelzer, 2013) and as McDonalds omega
(total) coefficients for the multidimensional IBRS and its three-item subscales
(in Study 3; see Dunn, Baguley, & Brunsden, 2014; Watkins, 2017). In the
pilot study, however, we report scale reliabilities as Cronbachs alphas due to
the relatively small sample size.
The convergent validity was estimated by assessing the hypothesized
associations between the IBRS and social media activity, compulsive
Internet use, and online group behavior (self-categorization, group influence,
and responding to group activity) in a social media vignette experiment (for
a correlation matrix and additional analyses for the IBRS subscales and our
validation variables, see Tables A5A8 in supplementary material).
Associations were estimated as standardized regression coefficients (not
controlling for third factors), and for group behavior they were calculated
separately for respondents in the group condition and respondents in the
control condition.
Finally, we tested the measurement invariance of the IBRS-6 between our
study samples. This was done by comparing three nested measurement models
(see, e.g., Little, Slegers, & Card, 2007; Putnick & Bornstein, 2016). In the first
model, no parameter constraints were set. In Model 2, all the factor loadings
were constrained to be equal between our study samples. In Model 3, factor
loadings and intercepts were set to be equal across our study samples. Thus,
Model 2 tested the metric invariance of our IBRS-6 (weak factorial invariance)
and Model 3 tested the scalar invariance (strong factorial invariance). For
Models 2 and 3, we analyzed the change in the chi-square statistic and CFI.
A nonsignificant change in the chi-square statistic (with 95% confidence inter-
val) and a decrease in CFI of less than .01 were considered as evidence for the
tested measurement invariance (Putnick & Bornstein, 2016).
Results
Development and piloting
Sixteen items in total were formulated to measure the hypothesized three
components the IBRS (Table A2 in supplementary materials). In the pilot
study, we used principal component analysis to test our hypothesized three-
factor model and to reduce the number of items down to the six most
appropriate ones to measure our scales three subdimensions. The principle
component analysis supported our three-factor solution. The selection of the
remaining six items was based on face validity, item loadings, explained
variance, and interitem correlations. In other words, we chose items that
12 M. KAAKINEN ET AL.
best captured the essence of our theoretical model, loaded on hypothesized
factors, were sufficiently explained by the three-factor model, and resulted in
subscales with appropriate internal consistency. The resulting set of items
and their formulations are presented in Table 2. The three-component
solution accounted for 81% of the variance of the final six items, and factor
loadings on the components varied between .41 and .90. The Cronbachs
alpha for the final scale was .77, and it was .78, .77, and .61 for the subscales
of social identification, homophily, and information bias, respectively.
Study 1
CFA
To test the construct validity of the three-factor solution suggested by our
theoretical framework, CFA was performed on the six IBRS items selected in
the pilot study (Table 2). According to fit statistics (Table A3 in supplemen-
tary materials), we observed a good fit between the data and our model, χ
2
(6.94)/6 = 1.16, p= .327, RMSEA = .011, 90% CI [.000, .040], SRMR = .006,
CFI = 1.000, and TLI = .999. The fit was better than for competing one- and
two-factor models. Standardized factor loadings (.66 at lowest) and factor
covariances are presented in Figure 1. The reliability estimates of the IBRS
and its subdimensions are reported in Table A4 (supplementary materials).
Table 2. Item formulations for IBRS-6 and IBRS-9.
Item
No Item Formulation
Adapted/Self-
Developed
IBRS-6
Social identification 1 In social media, I belong to a community or
communities that are important part of my
identity.
Leach et al. (2008)
2 In social media, I belong to a community or
communities that Im proud of.
Leach et al. (2008)
Homophily 3 In social media, I prefer interacting with people
who are like me.
McCroskey et al.
(2006); McCroskey
et al. (1975)
4 In social media, I prefer interacting with people
who share similar interests with me.
Self-developed
Information bias 5 In social media, I trust the information that is
shared with me.
Self-developed
6 In social media, I feel that people think like me. Self-developed
IBRS-9: added items
Social identification 7 In social media, I belong to a community or
communities that I can commit to.
Leach et al. (2008)
Homophily 8 In social media, I prefer interacting with people
who share my values.
Self-developed
Information bias 9 In social media, I can keep myself well
informed.
Self-developed
Note. IBRS = identity bubble reinforcement scale. The scale for all the items was from 1 (does not describe me
at all)to10(describes me completely). References are provided only for items adapted from earlier
measures. Item No = item number.
MEDIA PSYCHOLOGY 13
Convergent validity
The convergent validity of the IBRS was estimated by calculating its correlation
with social media activity, compulsive Internet use, group influence, and
responding to group activity in a social media vignette experiment (Table 3).
Figure 1. Standardized factor loadings and factor covariances for identity bubble reinforcement
scale (IBRS-6) in study 1.
Table 3. Associations between IBRS-6 and IBRS-9 and validation measures estimated separately
for each validation variable.
IBRS-6 IBRS-9
Study 1 Study 2 Study 3
Validation measures β95% CI β95% CI β95% CI β95% CI
Social media activity .40*** [.35, .46] .32*** [.18, .47] .42*** [.34, .50] .43*** [.35, .51]
Compulsive Internet use .26*** [.21, .32] .25*** [.10, .41] .25*** [.16, .34] .26*** [.17, .34]
Self-categorization
Group condition . . .30** [.07, .52] .36*** [.24, .49] .31*** [.18, .44]
Control condition . . .21 [.01, .44] .31*** [.20, .42] .33*** [.22, .44]
Group influence
Group condition .10* [.02, .18] .05 [.18, 29] .02 [.12, .16] .00 [.14, .13]
Control condition .04 [.04, .11] .18 [.06, .43] .00 [.12, .11] .00 [.12, .11]
Responding to activity
Group condition .14*** [.06, .22] .21 [.01, .42] .16* [.03, .29] .18** [.05, .31]
Control condition .13*** [.05, .22] .07 [.15, .30] .10 [.02, .22] .10 [.02, .22]
Note. IBRS = identity bubble reinforcement scale. Analyses are based on the following sample sizes: Study 1:
N= 1,200, Study 2: N= 160, Study 3: N= 501; β= standardized regression coefficient, 95% CI = 95%
confidence interval. ***p< .001. **p< .01. *p< .05.
14 M. KAAKINEN ET AL.
We observed significant positive correlations between the IBRS and social media
activity (β=.40,p< .000) and compulsive Internet use (β=.26,p<.000).Inthe
vignette experiment, the IBRS was positively correlated with group influence in
the group condition (β=.10,p= .019), but not in the control condition (β=.04,
p= .309). In addition, the IBRS was positively correlated with responding to
group activity in the group condition (β=.14,p< .001) and the control
condition (β= .14, p= .001).
Study 2: replication of Study 1
CFA
Here, CFA was conducted with an additional data set to replicate the measures
in Study 1. According to fit statistics (Table A3 in supplementary materials), the
fit between the data and the three-factor model was good in the second sample,
as well, χ
2
(2.51)/6 = 0.42, p= .867, RMSEA = .000, 90% CI [.000, .053],
SRMR = .009, CFI = 1.000, and TLI = 1.030. The fit was better than for
competing models. Standardized factor loadings (.64 at lowest) and factor
covariances are presented in Figure 2, and reliability coefficients of the IBRS
and its subdimensions are reported in Table A4 (supplementary materials).
Figure 2. Standardized factor loadings and factor covariances for identity bubble reinforcement
scale (IBRS-6) in Study 2.
MEDIA PSYCHOLOGY 15
Convergent validity
We observed significant positive correlations between the IBRS and social
media activity (β= .32, p< .000) and compulsive Internet use (β= .25,
p= .001; Table 3). The correlation between the IBRS and self-categorization
was significant and positive for respondents in the group condition (β= .30,
p= .011) and positive but nonsignificant for those in the control condition
(β= .21, p= .062). However, the IBRS was not significantly correlated to
group influence in either group condition (β= .05, p= .651) or control
condition (β= .18, p= .138). The association between the IBRS and respond-
ing to group activity was positive but nonsignificant in the group condition
(β= .21, p= .065) and control condition (β= .07, p= .513).
Study 3: English validation of the IBRS
Confirmatory factor analysis
In Study 3, we tested the validity of both the six-item version of the IBRS
(IBRS-6) and the nine-item version (IBRS-9) consisting of original items plus
one additional item for each subscale (Table 2). Based on earlier measures
and the reviewed theory, along with our reflection on the findings of our
pilot study, we hypothesized these items to enhance the face validity of our
measure and to facilitate the usage of the IBRS subdimensions as separate
measures in future studies (for which at least three indicator variables are
required per dimension).
The IBRS-6 had an acceptable fit between the data and our three-factor
model, χ
2
(13.110)/6 = 2.185, p= .041, RMSEA = .049, 90% CI [.009, .085],
SRMR = .016, CFI = .994, TLI = .984, and the fit was better than for competing
models (Table A3 in supplementary materials). Standardized factor loadings
(.62 at lowest) and factor covariances are presented in Figure 3.
According to fit statistics, there was also an acceptable fit between the IBRS-
9andourdata,χ
2
(50.77)/24 = 2.12, p= .001, RMSEA = .047, 90% CI [.029,
.065], SRMR = .027, CFI = .988, TLI = .982. In case of both the IBRS-6 and the
IBRS-9, the result of the χ
2
test was significant, but we interpret the overall
model fit to be acceptable because all the smaller sample size dependent fit
indices suggested by Hu and Bentler (1999) showed excellent fit. Here again,
the fit between the data and the hypothesized three-factor model was better
than that for competing models (Table A3 in supplementary materials).
Standardized factor loadings (.69 at lowest) and factor covariances are pre-
sented in Figure 4. The reliability estimates of the IBRS-6 and the IBRS-9 and
their subdimensions are reported in Table A4 (supplementary materials).
Convergent validity
The IBRS-6 had a significant positive correlation with social media activity
(β= .42, p< .001) and compulsive Internet use (β= .25, p< .000). In case of
16 M. KAAKINEN ET AL.
the IBRS-9, correlation coefficients were .43 (p < .001) for social media
activity and .26 (p< .001) for compulsive Internet use (Table 3).
The IBRS-6 was positively associated with self-categorization in group
condition (β= .36, p< .001) and control condition (β= .31, p< .001).
However, the IBRS-6 was not associated with group influence in either group
condition (β= .02, p= .781) or control condition (β= .00, p= .934). There
was, however, a significant positive correlation between the IBRS-6 and
responding to group activity in the group condition (β= .16, p= .018) but
not for control condition (β= .10, p= .104). The same was true in the case of
the IBRS-9 because the scale was only significantly correlated with respond-
ing to group activity in the group condition (β= .18, p= .007).
Testing the measurement invariance of the IBRS-6 between the study
samples
To test the IBRS-6s measurement invariance between our study samples, we
conducted three nested measurement models. The first model did not pose
any restrictions for the parameters between groups. In the second model, all
the factor loadings for our latent variables were constrained to be equal
Figure 3. Standardized factor loadings and factor covariances for identity bubble reinforcement
scale (IBRS-6) in Study 3.
MEDIA PSYCHOLOGY 17
between our samples, and in the third model all factor loadings and inter-
cepts were restricted to be equal. The corresponding fit indices for the model
comparisons are reported in Table 4. We found strong evidence for metric
invariance because there was no significant change in the chi-square statistic
(p= .757) after the factor loadings we set to be equal across samples (Model
2), and the decrease in the CFI statistic was less than .01. The evidence for
scalar invariance, in turn, was mixed because the decrease in the CFI statistic
Figure 4. Standardized factor loadings and factor covariances for identity bubble reinforcement
scale (IBRS-9) in Study 3.
Table 4. The model comparison indices for measurement invariance estimation.
Models df χ
2
χ
2
diff. df diff pr(χ
2
diff.) CFI CFI diff. RMSEA RMSEA diff. TLI TLI diff
Model 1 18 22.56 . . . .999 . .020 .997
Model 2 24 25.96 3.40 6 .757 .999 .001 .011 .009 .999 .002
Model 3 30 54.17 28.21 6 < .001 .994 .006 .036 .016 .991 .006
Df = degrees of freedom, χ
2
= chi-square statistic, χ
2
diff. = the change in chi-square statistic, df diff. = the
change in degrees of freedom, pr(χ
2
diff.) = the statistical significance of the change in chi-square statistic,
CFI = comparative fit index, CFI diff. = the change in comparative fit index, RMSEA = Root Mean Square
Error of Approximation, RMSEA diff. = the change in Root Mean Square Error of Approximation,
TLI = TuckerLewis index, TLI diff. = the change in TuckerLewis index.
18 M. KAAKINEN ET AL.
was less than .01 after the fixed factor loadings and intercepts were con-
strained. However, the change in the chi-square statistic was significant
(p< .001).
As the IBRS was administered after an experimental manipulation in our
vignette experiment, we also tested the measurement invariance between the
group identity and control conditions. The tests supported both metric
invariance and scalar invariance.
Discussion
This article is based on a series of studies that validated the IBRS for both
Finnish and English. The development of the scale was connected to the
ongoing discussion about different online bubbles (Abisheva, Garcia, &
Schweitzer, 2016; Bozdag, Gao, Houben, & Warnier, 2014;Nguyen,Hui,
Harper, Teryeen, & Konstan, 2014; Pariser, 2011; Zuiderveen Borgesius
et al., 2016). The phenomenon of social media bubbles is by no means
trivial, because social networks are major drivers of social media experi-
ences and online media consumption (Bakshy et al., 2015;Jostetal.,
2018). It has been well documented in previous studies that online inter-
actions tend to center around social cliques or bubbles that are manifested
in shared-group ideologies (e.g., political factions, world views) with
a strong reliance on similar-minded social networks as sources of reliable
information (Bessi et al., 2015;Bakshyetal.,2015; Boutyline & Willer,
2017; Del Vicario et al., 2016; Zollo et al., 2017). The IBRM by Keipi et al.
(2017) further elaborates how these elements manifest at the individual
level as identity bubbles.
Although earlier discussion of filter bubbles and echo chambers has
focused on perspectives originally developed in computer science, our work
is grounded on social psychology. The starting point was grounded in the
IBRM (Keipi et al., 2017). Based on earlier literature, we hypothesized that
online identity bubbles are reflected in social identification to online net-
works, online social homophily, and a tendency to rely on like-minded
information online (i.e., information bias). Our empirical approach sought
to create the first measure for examining individual points of view in relating
to social media networks. This kind of approach extends the existing view of
online bubbles by focusing on the social psychological side of the phenom-
enon, and it provides insight in understanding the underlying social factors
of online bubbles and their identity processes.
The three-factor structure of the IBRS was validated in three separate
studies. We also tested the measurement invariance of the IBRS-6 measure
between the study samples. We found strong evidence for configural invar-
iance (the same theory-based model showed a good fit in all study samples)
and metric invariance (equal factor loading between study samples) and
MEDIA PSYCHOLOGY 19
mixed evidence for scalar invariance. In addition, Studies 1 and 2 first
showed that the six-item version had good reliability in Finnish. Study 3
validated the IBRS-6 in English, with equally good reliability. We also
extended the IBRS-6 to a nine-item version in Study 3. Finally, the validated
IBRS-9 had good reliability and functional three-item subscales of social
identification, homophily, and information bias.
In all three studies, both the IBRS-6 and the IBRS-9 were associated with
social media activity and compulsive Internet use. We also found that the
IBRS-6 and the IBRS-9 were associated with self-categorization and respond-
ing to group activity in case of shared group identity in our vignette experi-
ments. However, the IBRS was associated with group influence in shared
group condition only in Study 1. This finding is interesting and demands
some reflection. In the vignette experiment, the shared group membership
was primed by a sentence indicating that respondents had been placed in
a group based on their earlier responses to the survey. A stronger group
priming, of course, could have induced a stronger effect. In addition, the
primed group membership was based on placement instead of self-selection.
In the case of IBRS, however, a self-selected group membership might have
been a more relevant factor. These points could also explain why there was
no significant difference in self-categorization between the group condition
and control condition in Study 2 and Study 3 (see the overlapping confidence
intervals in Table 3).
Overall, our findings underline the validity and potential usability of both
the IBRS-6 and the IBRS-9 scales. Our findings are consistent with prior
literature on social media and group identity processes (e.g., Flanagin, 2017;
Mikal et al., 2016; Shin et al., 2017; Turel & Osatuyi, 2017).
On social media, people often make fast decisions in communication and
content evaluation, and they tend to rely on their friendsopinions and
evaluations (Flanagin, 2017; Hocevar et al., 2014; Metzger et al., 2010).
Thus, online social networks and group memberships have an impact on
which content people trust and encounter on social media (Keipi et al., 2017).
Although this does not necessarily narrow peoples worldview, it may create
antagonism between different online groups (Abisheva et al., 2016). Hence, it
is important to understand differences among identity bubbles. In addition,
following Nobel Prize winner Daniel Kahnemans(2011) distinction between
System 1 and System 2 thinking, we suggest that characteristics of social
media tend to enforce fast and intuitive System 1 thinking compared to slow
and calculative System 2 thinking. System 1 thinking may also lead to various
spontaneous perceptual and logical errors.
As the IBRS measures individual experiences of ones relation to social
media and online networks in general, its generalizability and applicability
are not limited to any particular type of social media platform. From a users
point of view, social media covers various kinds of platforms that are
20 M. KAAKINEN ET AL.
typically not used in isolation. Platforms and their technological affordances
can also be interconnected, which makes it difficult to fully separate different
platforms. Also, preferring or being exposed to one-sided contacts or infor-
mation on some platforms does not necessarily indicate that this is the case
concerning an individuals social media experiences in general.
The IBRS should be seen as an important addition to the literature concern-
ing social media and identity. Many social media sites and applications have
facilitated both social identification and homophily. People are also increasingly
dependent on information gained online. These aspects underline the general
need to understand processes of identity reinforcement in an online environ-
ment. The IBRS essentially measures the tendency to affiliate and rely on others
on social media at a single point of time in this reinforcement process. People
with high IBRS scores have a stronger sense of social identification and homo-
phily and stronger reliance on information provided by their friends.
Our results also showed that people with a high tendency for online identity
bubbles are active, and even compulsive, social media users. Together, these
results help us understand the phenomenon of identity bubbles in social media.
By tying together three underlying social mechanisms influencing individuals
attitudes, relationships, and opinion forming, the IBR concept can provide
additional, well founded, and theoretically sound information on how different
social and interactional processes actualize in social media contexts. The con-
cept of an identity bubble might be applied to understanding a variety of
phenomena, ranging from subjective well-being and mental health to studies
on consumer behavior, politics, and social movements. Our study also demon-
strated that the phenomenon has cross-cultural relevance; social media usage
among young people is similar in different cultures and its outcomes function
in a consistent manner across cultures.
Limitations and strengths
Although our results were encouraging, there are some limitations. Because
the IBRS measures identity bubble reinforcement phenomenon only at one
point in time, in the future, it will be important to assess the temporal
stability of the IBRS in longitudinal settings to explore the dynamic reinfor-
cement process. It might also be important to analyze how self-reported
information gained from the IBRS is associated with naturally occurring
online behavior by using social media data sets. The developed scales are
relatively short because they only consist of six or nine items. However, our
aim here was to develop and validate a short scale because it may perform as
well as longer scales and have practical advantages. The applicability of the
IBRS for different social media studies is a major strength of the scale.
Another major strength is that we have already replicated our results with
MEDIA PSYCHOLOGY 21
three different studies in two countries. Further studies should continue
cross-cultural investigations.
Conclusion
Currently, social processes in social media, and their wider consequences, are
widely discussed. There is a need for social psychological measures examining
how individuals relate to and rely on their social media interactions. Together,
our three studies validate both IBRS-6 for Finnish and English and IBRS-9 for
English as measures for social media userstendency toward being involved in
online bubbles. The IBRS contributes to the research of online bubbles at least in
two important ways: First, unlike earlier studies scrutinizing explicit social
networks structures in social media, the IBRS is the first measure to emphasize
the individualsown perception of their relations to social media
networks. Second, the IBRS can be used in survey studies examining the social
media use of certain populations of interest in general instead of being limited to
specific online platforms and discussion topics. Perhaps the most comprehen-
sive understanding of current social mechanisms in social media could be
derived using the IBRS in combination with social network analysis. The IBRS
could be applied to a variety of research fields, including social sciences, psy-
chology, and economics. It is also possible to use subscales of social identifica-
tion, homophily, and information bias separately. Our results, based on
translating the IBRS-6 into English, are encouraging for its use in other lan-
guages as well.
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
This work was supported by the Finnish Foundation for Alcohol Studies [Problem Gambling
and Social Media Project].
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... During the time of social isolation and physical distancing, the role of the Internet as a source of social interaction becomes increasingly important. Humans are generally motivated to find similar others and join communities that fit their interests and worldviews [52,53]. In an online environment, these social needs are easily facilitated as users can seek information and create networks and communities that validate their interests and identities [53,54]. ...
... Humans are generally motivated to find similar others and join communities that fit their interests and worldviews [52,53]. In an online environment, these social needs are easily facilitated as users can seek information and create networks and communities that validate their interests and identities [53,54]. The ability of online environments to bring together like-minded individuals is prone to decrease the diversity of exposed content and information via the user's personal online preferences and platforms' algorithmic filtering technologies [53]. ...
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People are fundamentally motivated to establish a shared reality with others to validate their identity and experiences. Guided by social identity theory, we examine how social identity processes, such as self-categorization and depersonalization, create a shared identity and a sense of shared reality. Research demonstrates that internal states such as attitudes, feelings, and emotions are often shared among members of a group. Furthermore, research has shown that self-uncertainty motivates people to establish shared realities through group identification, often with highly entitative groups that are associated with a self-saturating reality that is shared absolutely. Finally, we review research on how group-defining norms that serve as the bases of these identity-related shared realities are constructed and communicated through group-membership based influence.
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There is an increasing concern regarding the emergence of harmful compulsive use patterns among some social networking sites (SNSs) users. Although SNSs are efficient social interaction tools, there is a dearth of research that seeks to understand peer-influence determinants of compulsive SNS use. This study attempts to bridge this gap and tests a model that accounts for the effects of peer-influence mechanisms on compulsive SNS use. Because mindfulness is important for the observation of and cognitive deliberation regarding peer influences, the model also accounts for possible moderating effects of mindfulness. To test this model we conducted a two-wave survey of 155 SNS users from the US. Findings based on hierarchical regression models show that while observed increase in peer SNS use augments compulsive SNS use, social pressure self-efficacy reduces it. Trait mindfulness was found to accentuate these influences and serve as a double-edged sword: it strengthened the inhibiting effect of social pressure self-efficacy on compulsive SNS use, but increased the positive effect of observed peer use increase on compulsive SNS use. Moreover, about 11% of our sample met presumed rudimentary clinical compulsiveness screening criteria. Logistic regression showed that the likelihood of meeting these criteria is reduced with increases in SNS experience and social pressure self-efficacy. These findings can serve as a basis for the development of interventions that target such factors.
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People believe that future others' preferences and beliefs will change to align with their own. People holding a particular view (e.g., support of President Trump) are more likely to believe that future others will share their view than to believe that future others will have an opposing view (e.g., opposition to President Trump). Six studies demonstrated this belief in a favorable future (BFF) for political views, scientific beliefs, and entertainment and product preferences. BFF is greater in magnitude than the tendency to believe that current others share one's views (false-consensus effect), arises across cultures, is distinct from general optimism, is strongest when people perceive their views as being objective rather than subjective, and can affect (but is distinct from) beliefs about favorable future policy changes. A lab experiment involving monetary bets on the future popularity of politicians and a field experiment involving political donations ( N = 660,542) demonstrated that BFF can influence people's behavior today.
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Mass and interpersonal communication are rapidly converging as people integrate an assortment of Internet-based tools into their communication repertoires. This convergence prompts dramatic changes in the conditions that once were presumed to distinguish mass from interpersonal communication, most notably differences in communication directionality and scale, audience size and identification, and a host of cues that signal source credibility. This article proposes a number of features of technological convergence in this context—including shifts in message control, audience scale, and source, receiver, and temporal ambiguity—and describes illustrative implications for social influence processes. These features highlight areas that traditional mass and interpersonal communication perspectives cannot fully describe alone, and suggest new methods and directions for the examination of online social influence.
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In what traits do people interact with others who are similar to them in completely anonymous online communication? Can those traits contribute to greater exchange of opinion and information across the sociodemographic boundaries that often limit interaction between social strata? To answer this question concerning online homophily, we combined survey data on 7,287 users (aged 18 and above) of a Korean online dating advice platform with their behavioral data from June 2015 to August 2015 and explored whether advice exchange occurred between users with similar sociodemographic and personality traits. On this platform, two types of interactions occurred as follows: (1) responses to a randomly distributed problem submitted by an advice seeker and (2) the seeker's indication of approval of any of the responses given. The study found that (1) a receiver was more likely to respond to problems submitted by seekers of a comparable age and that (2) seekers were more likely to approve of a response if the seeker and receiver had similar educational backgrounds. By contrast, homophily based on personality traits was not observed even though some personality traits significantly affected the likelihood of both response and approval. Our findings suggest that online communication may breed sociodemographic homophily, whether based on age or education, more than expected or intended while not easily fostering alternative forms of homophily, such as personality homophily, which can potentially cut across borders dividing sociodemographic groups.
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Objective: To demonstrate that Coefficient omega, a model-based estimate, is more a more appropriate index of reliability than coefficient alpha for the multidimensional scales that are commonly employed by neuropsychologists. Method: As an illustration, a structural model of an overarching general factor and four first-order factors for the WAIS-IV based on the standardization sample of 2200 participants was identified and omega coefficients were subsequently computed for WAIS-IV composite scores. Results: Alpha coefficients were ≥ .90 and omega coefficients ranged from .75 to .88 for WAIS-IV factor index scores, indicating that the blend of general and group factor variance in each index score created a reliable multidimensional composite. However, the amalgam of variance from general and group factors did not allow the precision of Full Scale IQ (FSIQ) and factor index scores to be disentangled. In contrast, omega hierarchical coefficients were low for all four factor index scores (.10-.41), indicating that most of the reliable variance of each factor index score was due to the general intelligence factor. In contrast, the omega hierarchical coefficient for the FSIQ score was .84. Conclusions: Meaningful interpretation of WAIS-IV factor index scores as unambiguous indicators of group factors is imprecise, thereby fostering unreliable identification of neurocognitive strengths and weaknesses, whereas the WAIS-IV FSIQ score can be interpreted as a reliable measure of general intelligence. It was concluded that neuropsychologists should base their clinical decisions on reliable scores as indexed by coefficient omega.