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  <front>
    <journal-meta />
    <article-meta>
      <title-group>
        <article-title>Replicating and Extending a Facebook Uses &amp; Gratifications Study: Five Years Later</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <string-name>Ian Oakley</string-name>
          <email>ian.r.oakley@gmail.com</email>
          <xref ref-type="aff" rid="aff0">0</xref>
          <xref ref-type="aff" rid="aff1">1</xref>
          <xref ref-type="aff" rid="aff2">2</xref>
        </contrib>
        <aff id="aff0">
          <label>0</label>
          <institution>ACM Classification Keywords H.5.m. Information interfaces and presentation (e.</institution>
          <addr-line>g., HCI): Miscellaneous</addr-line>
        </aff>
        <aff id="aff1">
          <label>1</label>
          <institution>Madeira Interactive Technologies Institute University of Madeira Campus da Penteada</institution>
          ,
          <addr-line>Funchal</addr-line>
          ,
          <country country="PT">Portugal</country>
        </aff>
        <aff id="aff2">
          <label>2</label>
          <institution>Tasos Spiliotopoulos Madeira Interactive Technologies Institute University of Madeira Campus da Penteada</institution>
          ,
          <addr-line>Funchal</addr-line>
          ,
          <country country="PT">Portugal</country>
        </aff>
      </contrib-group>
      <pub-date>
        <year>2013</year>
      </pub-date>
      <abstract>
        <p>Social media change rapidly: new technological features become available and new communication practices emerge at a seemingly ever-accelerating pace. These dynamics raise questions about the validity of applying findings from past research to understand current systems. This paper explores this issue by a 2012 replication and extension of a prominent 2007 Uses and Gratifications (U&amp;G) study on Facebook. The current study effectively built on the previous work by employing the same questionnaire items to measure and determine gratifications for using Facebook. Reassuringly, there was a high degree of similarity. However, an open-ended question that allowed participants to expand on the suggested set of gratifications yielded a large number of suggestions, indicating that a more comprehensive U&amp;G study on Facebook may identify novel motivations for use, reflecting the increased scale, reach, and functionality of the site. The original study was also extended with the collection of empirical, numerical data derived from the Facebook API describing detailed Facebook usage and personal network structure. Motivations, challenges, successes and limitations of the replication and its extension are discussed.</p>
      </abstract>
    </article-meta>
  </front>
  <body>
    <sec id="sec-1">
      <title>Questions for eliciting</title>
      <p>items to be included in a</p>
    </sec>
    <sec id="sec-2">
      <title>U&amp;G study.</title>
      <p>• What is the first thing that
comes to mind when you
think about what you enjoy
most when using</p>
      <p>Facebook?
• What other words describe
what you enjoy about using</p>
      <p>
        Facebook?
• Using single,
easy-tounderstand terms, what do
you use Facebook for?
• What uses of Facebook are
most important to you?
position among SNSs, has a continuously evolving
feature set and one billion monthly active users [
        <xref ref-type="bibr" rid="ref2">4</xref>
        ].
Given this diversity, an effective way of understanding
Facebook is by exploring motives for using the service
via theoretical frameworks such as Uses and
Gratifications (U&amp;G) [
        <xref ref-type="bibr" rid="ref1 ref5">3, 7</xref>
        ].
      </p>
      <p>
        U&amp;G is a theoretical framework for studying motives
and outcomes – fundamentally, the “how” and “why”
of media use [
        <xref ref-type="bibr" rid="ref1">3</xref>
        ]. A typical U&amp;G study employs a survey
instrument (or occasionally interviews [
        <xref ref-type="bibr" rid="ref6">8</xref>
        ] or focus
groups [
        <xref ref-type="bibr" rid="ref8">2, 10</xref>
        ]) for the collection of all relevant data.
However, researchers have argued that more
datadriven methods for the collection of U&amp;G data can
enhance the analytical power of the approach [
        <xref ref-type="bibr" rid="ref5">7</xref>
        ].
Apart from enabling the collection of a much larger set
of data, the benefits of a data-centric study that follows
a computational approach to measuring Facebook use
would include freedom from issues such as recall bias
[1], interviewer effects [
        <xref ref-type="bibr" rid="ref4">6</xref>
        ], and other sources of
measurement error that may accompany survey
research (see [
        <xref ref-type="bibr" rid="ref3">5</xref>
        ]), and assure the collection of
accurate measures of users’ activity, broken down by
specific Facebook features. In fact, as a theoretical
framework, U&amp;G does not mandate that any particular
empirical methods be used and, therefore, the inclusion
of computationally captured data in the U&amp;G framework
of analysis is a viable option [
        <xref ref-type="bibr" rid="ref5">7</xref>
        ].
      </p>
      <p>
        In our forthcoming CHI 2013 paper [
        <xref ref-type="bibr" rid="ref7">9</xref>
        ] we have aimed
at expanding the analytic framework of U&amp;G theory to
include network antecedents, as well as a more
comprehensive and accurate measure of Facebook
usage. In addition, we expanded the methodological
scope of U&amp;G by combining a typical survey tool with
data captured using the Facebook API. In doing so, we
built upon the results of a highly-cited Facebook U&amp;G
study by Joinson [
        <xref ref-type="bibr" rid="ref1">3</xref>
        ] conducted in 2007. Our study was
designed so that it is not “yet another U&amp;G study”, but
purposely and explicitly builds on the findings of
Joinson to the extent that it can be considered a
replication and extension of that work. This paper
presents an experience report based on this replication
and extension.
      </p>
    </sec>
    <sec id="sec-3">
      <title>The Original Study</title>
      <p>In July 2007, Facebook had recently moved outside the
US-academic environment and had 30 million signed
users. In his paper, Joinson employed a two-stage
approach to studying uses and gratifications.
In the first stage, 137 Facebook users were asked to
generate words or phrases to describe how they used
Facebook, and what they enjoyed about their use. The
questions used for this are shown in the sidebar.
These phrases were coded into 46 items, which were
completed by 241 Facebook users in stage 2. In
particular, participants were asked to rate, using a
7point Likert scale, the 46 uses and gratifications derived
from stage 1 using the metric, “How important are the
following uses of Facebook to you personally?”. The
scale was anchored at 1 (very unimportant) and 7
(very important). Participants also completed an item
related to their use of Facebook privacy settings,
specifically if they had changed the default settings,
and if so, the degree to which they had made them
more private or more open.</p>
      <p>Factor analysis identified seven unique uses and
gratifications: social connection, shared identities,
photographs, content, social investigation, social
network surfing and status updating. Of the 46 items
used in the factor analysis, 4 did not load on any of the
factors and 14 did not meet factor purity criteria and
were discarded, thus leaving a total of 28 items to
describe the factors. User demographics, site visit
patterns and the use of privacy settings were
associated with different uses and gratifications.</p>
    </sec>
    <sec id="sec-4">
      <title>Our Study and How it Compares</title>
      <p>The goal of our study was two-fold. First, to combine
the established framework of U&amp;G theory with detailed
usage and network data captured from an online social
network service. Second, considering the dynamic and
evolving nature of Facebook and the continuous
introduction of new features, we aimed at investigating
the extent to which the uses and gratifications
identified in the 2007 study stand the test of time. For
both goals, a direct comparison with the results of the
previous study was deemed desirable and it was
decided to build on those results instead of starting a
U&amp;G study from scratch. However, we were not
explicitly interested in replicating the study as faithfully
as possible (e.g., for validating the results), but simply
using the same factors in our analysis because we
considered that the two-stage process that was
employed ensured accuracy and comprehensiveness of
the identified items. Thus, we skipped the first stage of
Joinson’s study and instead utilized the 28 items he
originally identified in a replication and extension of the
second part.</p>
      <p>In our study, participants were recruited with a request
to complete an online survey. Recruiting was done
differently that in Joinson’s study, with approximately
1/3 of participants being recruited through posts on
social network sites, 1/3 through posts to online
forums, mailing lists and online study repositories, and
1/3 through a Facebook ad campaign. Participants had
to explicitly click a link to login with their Facebook
credentials and access the survey, which is an
equivalent action to installing a Facebook application.
This combination of recruitment methods led to a
sample that was more diverse in terms of demographic
and geographic distribution, compared to Joinson’s and
to similar studies that typically take place within
universities and study students. Since motives for
Facebook use will likely vary substantially across
cultures, ages, and educational backgrounds, the
diversity of the sample used in this work may better
match the traditionally exploratory nature of U&amp;G
studies. However, we should acknowledge a higher
selfselection bias in our sample, since participants had to
login with their Facebook credentials. On the other
hand, this same process may have discouraged
spurious participants (e.g., careless, dishonest, or
mischievous web surfers). The size of our sample (208
participants) is comparable to Joinson’s.</p>
      <p>After logging in, participants were directed to an online
survey capturing demographics and presenting 28
questions regarding their gratifications from Facebook,
corresponding to the items identified by Joinson. Two
questions examining attitudes towards privacy similar
to Joinson’s were also employed. Finally, participants
were given the opportunity to expand on the suggested
set of gratifications by answering an open-ended
question that asked “Are there any other ways (not
mentioned above) that you use Facebook for?”.
In the meantime, the Facebook API was used to access
a range of usage information for each participant. This
included 11 variables, such as number of status</p>
    </sec>
    <sec id="sec-5">
      <title>Suggestions for items to</title>
      <p>be included in future</p>
    </sec>
    <sec id="sec-6">
      <title>Facebook U&amp;G studies.</title>
      <p>Keeping up with news in
general, keeping up with
news from specific locations,
keeping up with news from
specific online news sources,
following music bands,
following specific news
sources, following certain
personalities (celebrities),
following certain personalities
(work-related), following
organizations (e.g., theaters,
clubs), entertainment and
time-passing by following
links suggested by friends,
sending messages,
remembering birthdays,
promoting work,
sharing/viewing videos,
sharing music, chatting,
video chatting, using email,
maintaining professional
relations, personal image
control, organizing around
school homework, seeing who
is in a relationship with
whom, linking to and
promoting personal blogs,
running Facebook Pages to
connect with people with
similar interests or fans.
updates made, likes given, check-ins made, and groups
joined. In addition, the participant’s Facebook
friendship network was also collected enabling the
calculation of 8 personal network metrics, such as size,
density, and number of connected components.
An exploratory factor analysis was conducted on the 28
items, yielding seven factors, corresponding to motives
for Facebook use, which are similar to those identified
by Joinson. The differences between the factors
identified in the two studies are in five items that did
not load clearly, and the reinterpretation of the factor
“Status updates” as “Newsfeed” to better reflect its
constituent questions. In addition, a single item was
moved to another factor.</p>
      <p>Furthermore, the responses to the question “Are there
any other ways (not mentioned above) that you use
Facebook for?” yielded answers that suggest the
inclusion of some new items to future studies, reflecting
the dynamic and evolving nature of Facebook and the
continuous introduction of new features. The most
notable of these suggestions are shown in the sidebar.
It is worth noting that some of these items were
identified in the first stage of Joinson’s study as well,
but were discarded in the second stage due to not
meeting factor purity criteria. Many others, however,
are new reflecting new functionality in the service.</p>
    </sec>
    <sec id="sec-7">
      <title>Extending the Study</title>
      <p>The rest of our study followed a slightly different
approach to the original. In Joinson’s study, as happens
in a typical U&amp;G study, after the gratifications are
gathered, the analysis examines the effect of the
social/psychological antecedents and gratifications on
the uses. However, since this analysis is purely
correlational, it is methodologically sound to reverse
the directionality of analysis and attempt to predict the
gratifications from the variables describing antecedents
and uses, which is the approach adopted in our work.
So, a series of multiple regressions were run with the
seven motives (i.e., factor scores) of Facebook use as
outcome variables, the Facebook usage metrics and
network metrics as predictor variables, and the
demographic variables as controls. Results showed that
all three variable types in this expanded U&amp;G frame of
analysis (covering social antecedents, usage metrics,
and personal network metrics) effectively predicted
motives and highlighted interesting behaviors.
Two additional multiple regressions were run with the
factor scores of the users as predictor variables and the
answers to the two questions regarding privacy as
outcomes. This aimed at further illustrating the power
of this extended framework, by exploring the intricate
nature of privacy in social media and drawing
relationships between privacy attitudes (and acts) and
measures of use and network structure.</p>
    </sec>
    <sec id="sec-8">
      <title>Discussion on Replication</title>
      <p>The results of U&amp;G studies are typically reported in a
way that facilitates replication; the data collection is
clearly described and all the factors, items, and their
loadings are reported. However, we are not aware of
another U&amp;G study that has been replicated (in social
media, at least). In our case, there was no ambiguity
about what happened in the first study and there was
no need to contact the original author. Replicating the
first stage of the original study might have produced
some interesting results and possibly better highlighted
the evolution in Facebook the past five years. However,
doing this seemed out of the overall scope of our study
and could possibly lead to an uneven publication. The
replication of the study was straightforward, but the
extension required a bit more work, as more data were
required. The differences in the sampling method and
the data collection by the Facebook API had both
advantages and disadvantages over the original study.
Neither study can claim that its sample can adequately
generalize to the Facebook population, but for different
reasons each.</p>
    </sec>
    <sec id="sec-9">
      <title>Conclusions</title>
      <p>This paper presented an experience report based on
this replication and extension of a U&amp;G study on
Facebook 5 years later. Our study effectively built on
the results of a previous U&amp;G study, by employing the
items identified in the previous study to determine
gratifications. The gratifications identified were very
similar to those in the previous study, although it is not
clear if it was expected since the same items were used
for the exploratory factor analysis. However, an
openended question that gave participants the opportunity
to expand on the suggested set of gratifications yielded
a large set of suggestions, hinting that a more
comprehensive current U&amp;G study on Facebook could
identify new uses and gratifications, reflecting the
evolution of the service the last few years. The original
study was extended with the collection of a range of
computationally collected data from the Facebook API
covering Facebook usage and personal metrics, that
effectively leverage prior research as a platform from
which to expand the traditional U&amp;G framework of
analysis.</p>
    </sec>
    <sec id="sec-10">
      <title>Acknowledgements</title>
      <p>The work reported in this paper is supported by FCT
research grant SFRH/BD/65908/2009.</p>
      <p>References
[1] Brewer, D. Forgetting in the recall-based elicitation
of personal and social networks. Social Networks 22,
(2000), 29–43.
[2] Dunne, Á., Lawlor, M.-A., and Rowley, J. Young
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Interactive Marketing 4, 1 (2010), 46-58.</p>
    </sec>
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