Where are my friends? The effects of real and imagined online communities on user wellbeing

UNIVERSITY OF OXFORD
Where are my friends? The
effects of real and imagined
online communities on user
wellbeing and happiness
St. Cross College
Oxford Internet Institute, August 2011

Abstract
Everyday social relationships are one of the most well documented factors influencing
physical health, wellbeing, and happiness. Benefits include decreased mortality, lowered
levels of distress, increased feelings of security and self worth, an increased sense of
belonging, a source of regulation for maintaining healthy behaviors, as well as enhanced
social participation and commitment to community.
Over one billion people are a member of at least one online social network, which are
designed to forge and maintain social ties. I set out to explore how online interactions
impact the health and wellbeing of average users.
This research puts forth a new theory, known as the theory of perceived companionship,
which postulates that online environments present unique affordances that facilitate the
development of companionate relationships known to improve health. Hypotheses were
tested through an online survey of 61 Twitter users, which evaluated network structure,
expectations for interactions, and explicit reactions following online correspondences
along a number of metrics known to boost well-being. This data was analyzed to better
understand the associations between community membership, direct interactions, and
subsequent user reactions. By identifying instances known to increase happiness,
researchers and developers may incorporate the results to inform the creation of digital
tools that consider the psychological impact of online use and advance its known benefits.

Acknowledgements
I wish to thank my advisor, Dr. Eric Meyer, for his unwavering support and
guidance. I am also grateful to Dr. John Powell, Fadhila Mazanderai, Dr. Louise
Locock and everyone at the Health Experiences Research Group for taking
interest in my project and providing expert advice. To Dr. Theodore Zeldin:
Thank you for emphasizing the importance of human connectedness and the
specialness of human-to-human interactions. These ideas served as a primary
inspiration for my research and I hope that my results stimulate the development
of digital tools that connect individuals in a meaningful way. Thank you to my
colleagues for disseminating the online survey, and to Dr. Kevin Wright and Dr.
Kathleen Griffiths, who were gracious enough to provide research materials and
recommendations.
"The happiest people are those who think the most interesting thoughts.”
– William Lyon Phelps

Table of Contents
Chapter 1 Introduction p. 1
1.1 The Internet, social support, and positive health outcomes p. 2
1.2 Research questions p. 4
Chapter 2 Literature p. 7
2.1 Social support p. 7
2.2 Companionship p. 9
2.3 Perceived social support, companionship, and social capital p. 11
2.4 The Internet’s ability to foster perceived social support and companionship p. 13
2.5 Fostering perceived social support online: design challenges for digital tools p. 14
2.6 Summary p. 15
Chapter 3 Method p. 17
3.1 Theoretical framework p. 17
3.2 Methodological approach p. 18
3.3 Twitter survey p. 19
3.3.1 Background p. 19
Chapter 4 Results p. 25
Chapter 5 Discussion p. 50
Appendix p. 55
References p. 67

1
Chapter One
Introduction
In 2010, The Lancet published a study (Lester et al., 2010) that examined
whether mobile phone communication between health care providers and HIV
patients in Kenya would improve adherence to drug regimens and result in
suppressed viral loads. 581 participants were randomized to either an
experimental or control group. Every Monday morning, the intervention group
received a text message from a health worker that simply asked, “How are you?”
Patients were instructed to reply that they were either doing well, “Sawa” in
Kiswahili, or “Shida,” that they had a problem. If a subject responded with the
latter, a nurse would contact that individual during clinical hours. Out of 11,983
responses to SMS inquiries, 7,812 stated that they were well while only 391
reported a problem.
Although the majority of subjects did not interact with a health worker except to
convey that they were doing well, the intervention group displayed higher
adherence to the drug regimen and increased viral suppression compared with the
control, which received traditional health services. When subjects were asked
what they thought about the program, many expressed that they felt “like
someone cares.” Even though the patients did not know the health provider who
had reached out to them, these short, impersonal messages fostered a clear sense
of comfort, resulting in positive health outcomes. This thesis explores how

2
supportive mechanisms function in mediated interpersonal communication1
, with
a specific focus on the Internet, to benefit both those who may be ill as well as
individuals who are healthy.
1.1 The Internet, social support, and positive health outcomes
The link between social ties and well-being is firmly established (Kawarchi and
Berkman, 2001). Social support is one of the most well documented psychosocial
factors influencing physical and mental health (McCormick, 1999; Rook, 1987).
Benefits include decreased mortality, lowered levels of distress, increased feelings
of security and self worth, a heightened sense of belonging, a source of regulation
for maintaining healthy behaviours, as well as enhanced social participation and
commitment to community (Shklovski, Kraut, and Cummings, 2006). However,
the precise definition of social support is unclear. Overall, it is a type of
relationship transaction between individuals (Zimet et al., 1988). Shumaker and
Brownwell characterized social support as the interchange of resources between
actors that results in enhanced well-being for one or more of the individuals
(Zimet et al., 1988). Although the intended outcome is apparent, the kinds of
resources enacted to achieve this end are unspecified. Furthermore, research has
demonstrated that the provision of tangible resources may not directly lead to
stated health benefits. Rather, positive health outcomes are the result of how the
activity is perceived and interpreted by an individual, a concept known as
perceived social support. (Shklovski, Kraut, and Cummings, 2007.)
1
Mediated communication is defined as a process by which a message is transmitted via some
intermediary, such as a networked computer or mobile phone (Pavlik and McIntosh, 2003).

3
The Internet has been lauded for its ability to foster feelings of perceived social
support across a multitude of online environments (Caplan and Turner, 2007;
Wright 2000; Morahan-Martin and Schumacher 2003; White and Dorman 2001;
Barrera et al., 2002; Yoder and Stutzman, 2011). For instance, a recent study by
the Pew Research Center (Hampton, Goulet, Rainie, Purcell, 2011) concluded
that Facebook users possess higher levels of perceived social support than the
average American. To quantify this outcome, an individual who uses Facebook
multiple times per day accrues about half of the boost in total support as
someone gains from being married or living with a partner. As the Internet
becomes increasingly embedded in our lives, it is imperative that software
developers and researchers establish a detailed understanding of how online use
affects health in order to strategically advance its known benefits.
The broad goal of this study is to inform theories regarding how the Internet can
be employed to improve and maintain health through cultivating feelings of
support among users. The World Health Organization defines health as “a state
of complete physical, mental, and social well-being and not merely the absence of
disease or infirmity (2011). My primary area of interest is mental health, which is
known to impact physical health, and defined as a “state of well-being in which
every individual realizes his or her own potential, can cope with the normal
stresses of life, can work productively and fruitfully, and is able to make a
contribution to her or his community.” (World Health Organization, 2011). As
such, mental health refers to an optimum level of well-being rather than a
psychological impairment. The implications of this study are directed at those
who are traditionally considered healthy, or without any identifiable illness. The
research explores ways in which the Internet may be used to elevate feelings that
promote well-being, such as connectedness, self-esteem and self-worth, among

4
others (Cheng and Furnham 2002; Wright 2000). Findings may be appropriated
to inform the creation of digital tools that consider the psychological impact of
mediated interpersonal engagement and aim to enhance the lives of objectively
healthy individuals.
1.2 Research Questions
This study seeks to identify the specific mechanisms that foster perceived social
support online. Through evaluating literature from the areas of eHealth,
psychology, and social dynamics of the Internet, I developed a distinct underlying
mechanism called perceived companionship that, when enacted in the prescribed
context, has the potential to improve user well-being through online interactions.
I tested this theoretical framework through an online survey of Twitter users.
Twitter is an online social networking service that allows subscribers to send and
read messages called tweets (Twitter, 2011). Tweets are text-based posts of up to
140 characters that are published on a user’s profile page in the sequential order
in which they were received and are publicly visible by default. Twitter users can
subscribe to receive other users’ tweets, a process known as “following,” while
those who follow are “followees.” Subscribers are called “followers” and can reply
to tweets posted by those they follow. In addition, they can re-post messages
published by other users on their own Twitter account, which publicly assigns
credit to the originator of the message. This action is known as a “retweet.”
Twitter was created in 2006 and currently has over 200 millions users who
generate more than 350 million tweets per day (Shonfeld, 2011).
The Twitter survey was designed to test the following hypotheses:
H1: A reciprocal online interaction has the potential to improve user well-
being

5
H2: A potentially reciprocal online interaction has the ability to improve
user well-being
A reciprocal interaction is defined as one in which, for any action undertaken by
an individual, there is the opportunity for others to do the same in return
(Byman, Jarvela, and Hakkinen, 2005). I selected Twitter to test these
hypotheses because any action by a Twitter user has the potential to be
reciprocated by another individual. Specifically, reciprocal interactions were
delimited as those where the subject tweets and another user acknowledges that
subjects’ contribution by electing to follow that user, directly reply to their post,
or retweet.2
As previously stated, social support is the objective transaction of
resources while perceived social support is an individual’s subjective notion that
resources are conceivably available for use (Shklovski, Kraut, and Cummings,
2006.) In this study, the concept of perceived companionship extends the
traditional theory of perceived social support. Furthermore, as with perceived
social support, the beneficial outcomes associated with perceived companionship
remain contingent on an explicit exchange between individuals, or the potential
for an exchange to occur in the future.
In H1, the term “potential” is synonymous with capacity; a reciprocal online
interaction has the capacity to improve user well-being. For H2, potential refers
to an exchange that may occur in the future and is aligned with the notion of
perception as it relates to perceived social support. In other words, the mere
knowledge that a reciprocal online interaction could develop is predicted to
improve user well-being. H2 focuses on individuals who have previously engaged
in a reciprocal online exchange and accrued subsequent positive affect, consisting
2
For additional information, see Appendix: Section 1

6
of positive emotions such as happiness and optimism. Consequently, these users
would develop an expectation that if a reciprocal connection were to occur, they
would experience a boost in well-being. This anticipation is consistent with the
notion of perceived social support, whereby health benefits are derived from the
understanding that support could and would be enacted if the user so desired,
rather than through the direct transaction of supportive resources.
Figure 1.1 The role of positive reinforcement in fostering perceived social
support on Twitter
Survey items were designed to measure (1) how users perceive their Twitter
community (Who are the individuals that comprise a subject’s Twitter network
and what is their relationship to the subject?), (2) expectations and hopes for
interactions on Twitter, and (3) general and specific reactions to, and perceptions
of, Twitter interactions (including but not limited to gaining a follower, receiving
a reply, and having a post retweeted.) In order to quantify the adequacy of boosts
in well-being and directly test the hypotheses, actions were deemed particularly
notable if over 50% of subjects exhibited the measured effect. However, responses
were also discussed from a qualitative perspective in order to more deeply explore
the implications of reported data.
Subject(tweets(
Another(user(
acknowledges(
tweet(
Subject(
experiences(a(
boost(in(well8
being( Cultivates feelings of
perceived support

7
Chapter 2
Literature
The literature explores social support, perceived social support, and their
associated health outcomes. In addition, it discusses the role of companionship as
a related mechanism that, along with perceived social support, is part of a single
global construct known to improve well-being (Newcomb,1990). Lastly, the review
identifies the Internet’s specific affordances that make it an advantageous
environment for fostering perceived social support and companionship among
users (Walther, 2007).
This study adheres to the assertion that social support, companionship, and other
descriptions of human connectedness, including loneliness3
, all reflect certain
shared processes (Newcomb, 1990.) Although these mechanisms have primarily
been studied independently, they are part of a high-order construct of general
social attachment (Newcomb, 1990). Correspondingly, metrics used to evaluate
companionship and social support must address a more basic need for individuals
to connect with a personal social network (Newcomb and Bentler, 1986). This
study considers social support, companionship, and related mechanisms
associated with well-being from a holistic perspective, as different sides of the
same concept.
2.1 Perceived Social Support
There is not a singular theory that wholly encapsulates researchers’
understanding of social support (Lewkowicz, et al., 2008). Although most agree
3
For additional information, see Appendix: Section 2

8
that it involves some kind of relationship transaction between individuals, the
nature of the transaction has been particularized in a variety of ways (Zimet et
al.,1988). For example, Barnes and Duck assert that social support is an exchange
of verbal and nonverbal messages that transmit emotion or information in order
to reduce ambiguity or stress (1994). Others delineate the theory by the type of
support provided, such as emotional support (comfort, friendship, love),
informational support, esteem support, tangible aid (instrumental or material
help), or social network support (McCormick, 1999). More generally, it may be
the feeling of being loved, cared for, or esteemed by others. Evidently these
definitions range in specificity and incorporate a number of characteristics that
are known to enhance well-being. Consequently, Vaux (1988) concluded that any
single definition of social support will prove inadequate in encompassing its many
facets.
Health Benefits: Despite these varied definitions, the positive health benefits
associated with perceived social support are incontrovertible. Support is known to
positively influence both physical and mental health through, for example,
decreased morbidity and mortality and improved psychological well-being (Zimet
et al., 1988).
Importance of individual perception: According to Heller et al (1986), the
objective support activity is not as important as how it is interpreted, which
directly influences the outcome and overall satisfaction with the support
provided. To underscore the value of perception, Hawkley and Cacioppo state
(2010, p. 224),
“Humans are such meaning-making creatures that we perceive social
relationships where no objectifiable relationship exists (e.g., between author
and reader, between an individual and God) or where no reciprocity is

9
possible (e.g., in parasocial relationships with television characters).
Conversely, we perceive social isolation when social opportunities and
relationships do exist but we lack the capacity to harness the power of social
connectedness in everyday life.”
Because the definition of social support is nebulous and highly dependent on an
individual’s subjective interpretation of an exchange or relationship, a “systematic
exploration into the perceived attraction of online social support is especially
warranted.” (Walther and Boyd, 2002; pp. 153)
2.2. Companionship
Companionship has been characterized as part of the inclusive concept of
perceived social support and, alternatively, as a distinct independent construct
(Diener, 1994). Notwithstanding the lack of consensus around its definition, social
support is consistently contextualized as a mechanism employed to alleviate
personal problems and emotional distress (Caplan and Turner, 2007).
Appropriately, much of the related research focuses on individuals who are in
obvious need of support, such as the ill, the elderly, and those experiencing major
life upheaval (Wright, 2000). Companionship, on the other hand, is motivated by
the wish to experience rewards, such as positive affect and stimulation, through
interpersonal exchange (Thoits, 1982). In addition, it is the primary way in which
people bring pleasure and excitement into their lives (Rook, 1987). Rook (1987)
posits that companionship is a relationship formed around shared leisure
activities that are undertaken for the intrinsic goal of enjoyment. Activities are
described as those that provide benefits, such as humor, recreation, and affection,
and occur on a continuous basis as an integral part of our lives (Rook, 1987).

10
Based on this analysis, the primary differences between social support and
companionship are the intent behind the interactions, namely to ameliorate a
problem versus enjoyment, and the ways in which they improve well-being (Rook,
1987; Wright 2000). To illustrate this concept, if well-being is conceptualized as a
spectrum with zero as an individual’s equilibrium level of contentment, social
support is employed to bring people from a negative level of well-being to
equilibrium, or their usual level of contentment. Conversely, companionship
boosts an individual beyond their balanced level, resulting in relatively higher
levels of positive affect (Thoits, 1982).
Figure 2.1. The role of social support and companionship; conceptualizing
well-being as a spectrum
Health Benefits: Companionate interactions produce many of the same health
benefits as those accrued through social support in times of distress (Wright,
2000), including reduced stress, lower levels of morbidity and mortality, and
improved psychological wellbeing (Wright, 2000).
Despite the fundamental difference between the two concepts, the analogous
health effects produced by these mechanisms are consistent with the idea that

11
social support and companionship are part of the same construct (Newcomb,
1990). According to Wright (2000), it would be difficult to find a solely
supportive or a solely companionate exchange. Some researchers consider
companion relationships to be more beneficial than those deemed socially
supportive because they are proactive rather than reactive. In this way, actors
must have mutual respect for one another since they elect to foster a connection
for the sake of being in touch, rather than to elicit a specific kind of support, such
as tangible or emotional aid, and fill an unmet need (Wright, 2000).
Companionship was found to be the strongest predictor of relationship
satisfaction and loneliness. It may also function as a maintenance mechanism;
although social support might restore self-esteem on a specific occasion,
companionship could preserve these feelings for an extended duration. Overall,
companionship fosters positive affect, which contributes to greater resilience and
optimism, thus shaping how individuals perceive the world (Cohen and Pressman,
2005). Low positive affect and a lack of pleasurable activities have been
implicated in the inception and maintenance of some psychological disorders, such
as depression (Rook, 2011). In accordance with perceived social support, the
health sustaining value of companionship may emanate from the perception that
these connections exist, which is separate from the direct benefits derived from
interpersonal interactions (McCormick, 1999).
2.3 Perceived social support, companionship, and social capital
Social capital includes the notions of perceived social support and companionship
and is defined as the actual or virtual resources amassed through relationships
between people (Steinfeld, Ellison, and Lampe, 2008), Social capital results in a
number of personal advantages, including improved health. For example, access to
individuals outside of one’s close social circle provides exposure to non-redundant

12
information (MacKenzie and Harpham, 2006), which is directly related to
measures of psychological well-being such as self esteem and satisfaction with life
(Steinfeld, Ellison, and Lampe, 2008). This connection to a wider community is
the result of weak ties, or contacts that convey useful information or new
perspectives but do not provide emotional support (Granovetter, 1973). Weak ties
foster a specific type of social capital known as bridging social capital and
members of weak tie networks are thought to be outward looking since their
social networks include people from a range of backgrounds (Steinfeld, Ellison,
and Lampe, 2008). The Internet has been shown to cultivate bridging social
capital because it allows users to maintain relatively large networks from which
they can draw resources.
2.4 The Internet’s ability to foster perceived social support and
companionship
As previously stated, perceived social support and companionship are based on
subjective interpretations of a relationship. Online, the layout of websites can be
manipulated to alter user perception (Lewkowicz et al., 2008). For example,
everything from the placement of images, to the color, shape and typography can
affect users’ psychological responses to these features (Lee et al., 2004). Because
of the malleability of online environments and their direct effect on subjective
interpretation, I assert that the Internet is a favorable domain for fostering
perceived responses, such as social support and companionship. For that reason,
websites may be specifically designed to cultivate positive feelings, thus leading to
related health benefits.
I applied Walther’s hyperpersonal communication model to address why online
spaces are advantageous for producing perceived responses (Walther, 2007). The
Internet affords users a distinct environment that cannot be duplicated in the

13
face-to-face context through attributes such as distance, anonymity, interaction
management, and access. Dynamics between the sender, receiver, channel, and
feedback systems are directly impacted by attributes associated with computer-
mediated communication. For example, online contact can promote the
development of relationships and result in exaggerated impressions of
conversation partners. Specifically, interlocutors will note message elements that
indicate minimum levels of similarity or desirability and idealize partners based
on these limited cues (Walther, 2007).
In both face-to-face and online dialogues, actors are motivated to mitigate
ambiguity, create impressions, and develop positive views of one another (Walther
and Boyd, 2002). However, online interactions may be exceptionally information
poor and subjects might lack reliable verbal and physical cues to formulate these
conceptions. As a result, users will assess the limited signals available and
extrapolate to fill any information deficits in order to construct a coherent,
subjective perception of others and enhance relational outcomes. Perception
development may also be particularly straightforward online because relationships
are often less complex than in the offline environment (Walther and Boyd, 2002).
For example, in an online support group, the only feature uniting interactants
may be their source of personal discomfort. In this way, the connection is uniplex
rather than multiplex; this simplicity, combined with a lack of cues and desire to
develop favorable views of one another, may enable users to readily form positive
perceptions of conversation partners. Consequently, individuals might be more
likely to perceive online dialogues as supportive or companionate than those with
added contextual cues.
Numerous studies have explored the effect of Internet use on perceived social
support and companionship (Wright, 2000; Barrera et al., 2002; Drentea and

14
Moren-Cross, 2005). Steinfield, Ellison, and Lampe (2008) tested the impact of
online social network use on social capital and self-esteem. The study concluded
that online activity is positively associated with a person’s sense of self worth and
measures of psychosocial development. Despite these positive findings, conflicting
evidence has indicated that Internet use may increase social isolation and inhibit
the development of meaningful relationships in real life (McCormick, 1999).
2.5 Fostering perceived social support online: design challenges for
digital tools
The design and evaluation of health technologies are interdisciplinary processes
(Ahern, Patrick, Phalen, and Neiley, 2006). Scientific, policy, and commercial
communities play an integral role in various phases of development and each
stakeholder contributes a different perspective to the planning, understanding,
and evaluating of new health tools. Academia and industry have each worked to
develop effective digital products that improve health. However, software
development and health services research, comprised of industry and academia
respectively, are distinct fields; software developers are traditionally involved with
creating tangible digital tools, while researchers focus on evaluating interventions.
However, as technology has advanced, these roles have become increasingly
heterogeneous and less strictly defined. As a result, academics and developers
have taken on many of the same responsibilities. For example, researchers have
employed methodologies that require constant collaboration with key stakeholders
in order to actively promote health and implement incremental improvements to
suit the evolving needs of a community (Pagliari, 2007). Analogously, software
developers may continually evaluate user needs through a similarly iterative
process in order to create health applications.

15
Despite these overlapping methodologies, developers and researchers have tended
to work in parallel rather than as a cohesive unit. Consequently, the field of
health technology has been criticized for its lack of user involvement in the design
of eHealth applications, dearth of evidence demonstrating impact, and difficulties
in bringing new technology to adoption. Because of this disconnect between fields,
it is difficult to translate research into practice. While technology holds enormous
potential to improve well-being, creating effective products requires joint thinking
between the two groups to ensure that they are high quality and user-directed.
My research aims to deconstruct the concept of perceived social support online in
order to create a coherent theory of practice. By examining the present state of
research and testing a new theory through an online survey, I intend to develop a
normative standard that can be easily appropriated by both researchers and
software developers in order to inform the creation of efficacious digital tools that
enhance health and well-being.
2.6 Summary
This study is about social support, tested through Twitter. Previous literature
has evaluated Twitter as a communication tool (Pear Analytics, 2009), such as in
crisis situations (Huberman and Romero, 2009), as an alternative news source
(Kwak, Lee, Park, and Moon, 2010), a backchannel communication tool (Nely,
2009), and for its ability to track real-time trends (Sakaki, Okazaki, Matsuo,
2010). Twitter has only recently been explored as a potential means for increasing
happiness (Goncalves, Ruan, and Mao, 2011; Dodds, et al., 2011). Existing
evidence focuses on how mood states flow through a network. In addition, Dodds
et al. (2011) correlated 50 million messages with nation-wide happiness polls in
order to ascertain user reactions to tweets. My study will extend this burgeoning

16
field of research, comprised of just a handful of studies, by identifying specific
interactions that elicit positive emotions through self-reported responses.
This research reframes how perceived social support is conceptualized and
operationalized in the online space and assesses this idea in a largely unexamined
environment. The resulting conclusions represent a progression in the field of
Internet psychology and exposes opportunities for a future stream of research
dedicated to more deeply understanding how and why we interact online,
specifically in information-poor environments, and the ways in which this
knowledge can be employed to advance health and happiness.

17
Chapter 3
Method
3.1 Theoretical framework: perceived companionship
As defined in this study, the notion of perceived companionship is unique to
interactions conducted through mediated communication. In the offline
environment, companions may be synonymous with friends or leisure activity
partners (McCormick, 1999). Correspondingly, perceived companions also
engage, or have the potential to engage, in direct interactions. However, the
traditional notion of a friend is missing from the exchange. Applying Rook’s
definition of companionship (1987), rather than participating in shared
activities, a user may connect with a perceived companion through a one-off
interaction, or a series of short, information poor interactions. I posit that these
correspondences may result in the same health benefits associated with
traditional companionate relationships. In this way, the companionship aspect
of perceived companionship refers primarily to the purpose or intent of the
interaction, namely a leisure activity undertaken for the sake of enjoyment, and
the resultant positive outcomes.
The perceived component of the mediated interaction is twofold: (1) Like
perceived social support, it can be the potential for future interactions or the
perception that social support, in this case companionship, is available. (2)
However, perception also refers to the essence of the exchange. In order for an
individual to accrue benefits through interactions on Twitter, such as boosts in
self-esteem or self-worth, the recipient must have some baseline level of respect

18
for the other actor involved (Rook, August, Sorkin, 2009). For example, if a
user retweets my post but I know that the post was never read, I might not
experience a boost in well-being; the health benefit emanates from the fact that
I believe this actor noticed and interpreted my tweet, and felt it was significant
enough to retweet and share with their followers.
This process is largely perceived in the mind of the user. Twitter is an
information poor environment; communications and profiles consist of 140
characters or less, which makes it difficult for users to possess a deep
understanding of others, (unless they obtain additional information through
outside sources), and fairly impossible to share detailed messages through tweets.
Regardless of whether the interaction is with a friend, stranger, or otherwise, the
hypothesized health benefits resulting from interactions with, and perceptions of,
one’s Twitter network are based off of a cognitive process whereby the subject
believes that another user has deciphered the published information and deemed
it relevant, which compels them to initiate action directed at the subject. This
series is facilitated by the nature of mediated contexts; computer mediated
communication leads to more extreme impressions than in the face-to-face
environment and more positive relations over time (Walther, 2007). Consequently,
the subject may be more inclined to perceive activities as supportive or
companionate even if they were not intended as such.
3.2 Methodological approach
According to Lewkowicz et al. (2008), there is a lack of conceptual understanding
of social support and a poor level of innovation when designing computer-based
programs to cultivate feelings of perceived social support online. Because software
developers and researchers are members of distinct communities (Pagliari, 2007),
I believe that to bridge this gap, it is most beneficial to understand the

19
underlying mechanisms that manifest themselves in online interventions and are
known to improve health, such as perceived social support and companionship, so
that this information may be easily appropriated by all relevant stakeholders to
develop digital tools that effectively enhance well-being.4
As such, this represents
an innovative approach to eHealth research.
3.3. Twitter survey
3.3.1 Background
I selected Twitter as a platform to test the theory of perceived companionship
and the notion that a (potentially) reciprocal interaction has the capacity to
improve well-being. Lewkowicz et al. (2008) found that there must be an
opportunity for a reciprocal interaction to occur in order for a relationship to feel
and/or be supportive. Furthermore, individuals must be aware that they are
relation to one another for a social or exchange opportunity to exist (Lewkowicz
et al., 2008). On Twitter, members of the same network have the ability to
engage in these types of reciprocal exchanges with their contacts. In addition, the
process of following and gaining followers produces a built-in community where
the direct connection between the subject and his or her contacts is evident to
the user and the Twitter network. In addition, the Twitter feed, which contains
updated tweets from followees, continually reminds users which Twitter
subscribers are a part of their personal network.
4
For a systematic review of related literature, see Appendix: Section 3

20
Figure 3.1. Example of a Twitter feed
Survey construction and implementation
The online survey was hosted on Survey Gizmo (www.surveygizmo.com) for a 2-
week period in July 2011 and consisted of 42 items5
. 61 subjects were recruited
through requests for participation on Facebook and Twitter.
Key variables
The survey assessed specific feelings using a 5-item Likert scale.6
5
For a full list of survey items, see Appendix: Section 4
Continual updates from
followees in subject’s
Twitter network

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H1: An online interaction has the potential to improve well-being
Independent variables: Survey items assessed baseline demographic
characteristics, including educational background, gender, as well as country of
residence and origin. In order to gain an understanding of individuals’ Twitter use
and the structural configurations of their personal networks, questions measured
number of followers and followees, time since account activation, how often the
user tweets and “checks” their Twitter account, (defined as reviewing the Twitter
feed but not directly interacting with other users), and the percent of Twitter
interactions that occur with individuals who the subject considers to be a friend
and/or someone they interact with via other modes of communication.
Dependent variables: Dependent variables were selected to provide a qualitative
understanding of:
(1) User perception of their Twitter community (What is the relationship
between the subject and contacts who comprise their personal Twitter network?):
Part 1 assesses the objective and perceived structure of, and relationship to, one’s
Twitter network.
(2) Expectations and hopes for interactions on Twitter: Part 2 identifies the type
of support provided by the network. Items were designed to assess user reactions
to specific Twitter interactions and the Twitter community more generally, with
metrics adapted from surveys included in traditional support and companionship
literature. These questionnaires are widely used in related research and include
the Social Support Questionnaire (Sarason, Sarason, Shearin & Pierce 1987),
6
Survey items were both positively keyed (“If I want to have lunch with someone, I can easily
find someone to join me”) and negatively keyed (“I don’t often get invited to do things with
others”)

22
companionship satisfaction survey (Wright, 2000), UCLA Loneliness Scale
(Russell, Peplau, and Cutrona, 2011), and network satisfaction scale (Wright,
2000).
(3) Part 3 records user reactions to specific activities on Twitter as well as general
feelings about their Twitter networks. User reactions were aggregated along five
dimensions known to reflect well-being for three specific types of Twitter
interactions, including gaining a follower, having a post retweeted, or receiving a
reply. In addition, responses were ascertained for situations when a user
contributes to the Twitter community through a tweet, but is not acknowledged
by another user. According to Rook, August, and Sorkin, (2009) companionate
interactions may be detrimental if an individual faces rejection or exclusion by
others. The latter scenario is designed to measure if a lack of acknowledgement
would negatively affect well-being. Key words were selected to assess perceived
social support, companionship, and loneliness. Metrics included recognized and
appreciated, related to the concept of self-worth and self esteem; connected,
which is part of bridging social capital; loneliness or feeling embedded within a
community; capable, reflecting feelings of empowerment and self-esteem; as well
as annoyed, which is a stressor and known detriment to health (Thoits, 1982).

23
Figure 3.2. Categorization of metrics: user reactions to Twitter interactions
user well-being
In order to operationalize H2, I stratified the sample into Frequent and Less
Frequent user groups and compared results across survey items. Frequent users
were characterized as those who reportedly check their Twitter account once per
day or more often, and tweet once per day or more often. Less Frequent users
were defined as individuals who check their account at most once per week and
tweet at most once per week. In addition, I included subjects who check their
multiple times per day, but tweet once per week or less, in the Less Frequent user
group; although these subjects check their account often, they likely engage in
fewer reciprocal interactions because they rarely contribute directly to the
community through tweeting. Like H1, I assessed (1) how users perceive their

24
Twitter community, (2) expectations and hopes for interactions on Twitter, and
(3) reactions to, and perceptions of, Twitter interactions.

25
Chapter 4
Results
This study was deployed as a pilot. Given the relatively small sample size (n=61),
dependent categorical variables were compared using response modes in order to
develop a qualitative portrayal of participants’ Twitter use. This information was
evaluated in order to postulate the effects of subjects’ perception of, and
engagement with, their Twitter community on observed results. Although control
variables and demographics were ascertained, the sample was too small to
statistically compare outcomes. Because a quantitative analysis could not be
performed, these variables merely provided added depth to the cases. To simplify
comparisons between response categories, 5-item Likert scale items were
condensed into three classifications, including agree, neutral, and disagree. In
order to compare the adequacy of boosts, a 50% threshold was applied to
reported responses, whereby any category with a response rate of 50% or greater
was deemed particularly notable.
4.1 Sample characteristics (independent variables)
61 participants were split evenly between males and females (51% and 49%
respectively). Most were between the ages of 25 and 34 years (57%), followed by
35 to 54 years (25%), and 18 and 24 years (18%). The sample was highly
educated (69% had obtained a postgraduate degree) while the majority of
subjects resided in the UK (44%) and the US (35%), with the remainder located
throughout the world. The survey specifically targeted individuals who utilize
Twitter; 44% of subjects had established an account over 2 years ago, 26%

26
between 1 and 2 years, 18% 6 months to one year ago, and the remaining 11%
had active for less than 6 months. 36% of the sample tweeted multiple times per
day, 21% once each day, 21% once each week, 8% once per month, and 13%
rarely. 62% of users checked their accounts multiple times per day, 31% once each
day or week, and 6% once per month, rarely, or never. Overall, most respondents
tweeted often, although less frequently than they checked their accounts. The
majority of subjects said they consider fewer than 25% of Twitter contacts with
whom they interact with through the service to be friends (54.1%), while 25% of
subjects stated that 26% to 50% of interactions take place with friends.
Furthermore, 67% of respondents concluded that they communicate with fewer
than 25% of the users they follow on Twitter through additional modes of contact
(including email, Facebook, and face to face).
Figure 4.1. Frequency of tweets and checking accounts
62%
15%
16%
2% 5%
How often do you check your account?
n = 61
Multiple times per day
Once each day
Once each week
Once each month
Rarely

27
Figure 4.2. Characteristics of Twitter contacts
36%
22%
21%
8% 13%
How often do you tweet?
n = 61
Multiple times per day
Once each day
Once each week
Once each month
Rarely
3%
54%25%
5%
13%
Out of the contacts you interact with most frequently on
Twitter, what percent do you consider friends?
n = 61
None
Less than 25%
26%-50%
51%-75%
76%-100%

28
I. User perception of their Twitter community
40% of respondents claimed that they do not interact on Twitter and only
monitor their contacts’ activity. As previously stated, most interactions occur
with individuals who are not considered friends and users connect with fewer
than 25% of followers through additional modes of communication. In addition,
about half of respondents considered their interactions to be with members of
their professional network and 67% state that contacts are not part of their close
personal community. Only 23% of subjects found Twitter interactions to be
personal, 54% felt they were not personal, and 23% were neutral. Based on this
data, the objective relationship between users and their Twitter contacts is
unclear. However, it seems that the majority of contacts are not friends in the
traditional sense, because most do not communicate through additional channels
(such as email, Facebook, face to face, etc.), the interactions are perceived to be
impersonal, and Twitter contacts are not considered part of the subjects’ close
personal community.
To capture participants’ latent perceptions surrounding the concept of friendship
and closeness in a Twitter connection, I applied metrics that evaluate
1%
67%
20%
10%
2%
Out of your followers, what percent do you communicate with
via other modes of contact (email, face-to-face, Facebook, etc)?
n = 61
None
Less than 25%
26%-50%
51%-75%
76%-100%

29
responsiveness of the community, individual expectations for the interactions, and
general feelings toward the Twitter network. The abovementioned responses
indicate that a subjects’ community is likely to be constructed of weak ties.
However, 73.3% of respondents claimed that they share many interests with their
Twitter contacts, while only 8.3% disagreed with this notion. Although the
majority of the community might not be personally connected to the subject as a
friend or through other modes of communication, the user perceives Twitter
contacts to be highly similar to themselves. This is consistent with Walther’s
hyperpersonal communication model, which states that online interlocutors will
develop exaggerated positive perceptions of others based on cues that indicate
minimum levels of similarity or desirability. (However, these similarities could be
attributed to a connection with the professional network.)
65% of subjects agreed that if someone tweeted that they were having a problem,
they would feel concern for that person (27% were neutral and 8% disagreed) and
47% said they would contact that user through Twitter (23.3% were neutral and
30% disagreed.) Only 40% of participants agreed that if they posed a problem on
Twitter they were confident someone would respond (32% disagreed and 28.4%
were neutral.) Although this data is not statistically significant, it demonstrates
that Twitter networks are comprised of weak ties yet users feel concern for
individuals within their Twitter community. In addition, contacts are not
perceived to be responsive in addressing subjects’ problems through Twitter.

30
Figure 4.3. User perception of Twitter community
II. Expectations and hopes for Twitter interactions
The majority of users reported that Twitter does not provide them with
emotional support (70.1%) and if they are down in the dumps, they cannot count
on interactions with their Twitter network to make them feel better (70.5%). This
is consistent with the theory of companionship, which includes activities designed
for leisure rather than the provision of emotional aid. In terms of expectations,
only 13% of participants expect their tweets to be acknowledged by another user
and 55% do not care if a tweet is recognized (20% neutral and 25% disagreed.)
Additionally, most users reported neutral feelings (~60% to 70%) when asked how
they would react if they tweeted and did not receive a response. Despite the lack
of expectation or apparent care, 50% of subjects claimed that they hoped other
users would acknowledge their tweets. This discrepancy might suggest that
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
Agree Neutral Disagree
PercentofRespondents
Structure and Perception of Twitter Community
Professional network
Close personal community
Interactions are personal
I would feel concern if
someone tweeted a problem
If someone posted problem I
would contact them
Share many interests with
contacts

31
although users do not expect their tweets to be acknowledged and do not
necessarily care if they are recognized, they are still hopeful that someone will
elect to reply, retweet, or follow their account. Furthermore, 75% of subjects were
comforted by the fact that someone might acknowledge their contribution (3%
disagree and 22% neutral.)
If tweets are not acknowledged, it is unlikely that the user will experience any
negative outcomes, evidenced by data demonstrating that most users felt neutral
if they did not receive a response following a tweet and the majority do not care
if a tweet is acknowledged. If a user tweets that they are having a problem, 40%
believed that someone on Twitter would respond, while the remaining 59% were
neutral or disagreed. Additionally, 41.6% stated that if they pose a question on
Twitter, they are confident someone would respond (35% disagree.) All in all,
Twitter users are not confident that someone will respond to their tweets but 75%
of subjects are comforted by the fact that someone might acknowledge their
contribution to the Twitter community. Although the specific impact of this
comforting presence cannot be quantified through the Twitter survey, it is
consistent with the notion of perceived companionship, whereby the potential for
a companionate interaction increases well-being.

32
Figure 4.4. Expectations and hopes for Twitter interactions
III. Explicit reactions to, and perceptions of, Twitter interactions:
Explicit reactions: The data illustrates that when a user gains a follower, is
retweeted, or replied to, there is a boost in well-being that varies between the
specified metrics. If a user is notified about gaining a follower, 62% felt
recognized, 46% felt connected, 61% felt appreciated, 79% did not feel annoyed,
21% felt the same as before receiving the notification, and 24% felt capable.
When someone replies to a subject’s tweet, 88% felt recognized, 88% felt
connected, 77% felt appreciated, 80% did not feel annoyed, 8% felt the same as
before the notification (44% were neutral and 30% disagreed), and 41% felt more
capable (51% neutral.) If a post is retweeted, 81% felt recognized, 78% felt
connected, 82% felt appreciated, 87% would not feel annoyed, 10% would feel the
same (50% neutral), and 46% felt more capable (47% neutral). If a tweet is not
acknowledged, the majority of participants were neutral when asked if they felt
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Expectations and Hopes for Twitter Interactions
Comforting that someone
might acknowledge my
contribution
Hope for acknowledgement
from another user
If I am having a problem,
someone will respond on
Twitter
Do not care if tweet is
acknowledged
Pose a question, confident
someone will respond

33
recognized, capable, connected, or appreciated (~60% to ~70%). However, 40% of
subjects agreed that they would feel annoyed (40% neutral and 20% disagreed).
Overall, these statistics imply that users’ feelings are affected by notifications
about gaining a follower, being retweeted and receiving a reply along the
dimensions known to improve well-being. However, user well-being is relatively
unharmed if a response is not garnered.
In terms of explicit reactions, feeling recognized, appreciated, and connected
garnered the strongest positive responses, as well as the most polarized. Based on
comparisons to frequencies of Neutral and Disagree, replying to a tweet resulted
in the most pronounced objective increase along these dimensions (with 54, 54,
and 47 users in agreement respectively) and retweeting followed with 50, 42, and
50 subjects in support of feeling recognized, connected, and appreciated.
Furthermore, the responses for neutral were relatively low and few or zero users
disagreed with these assertions. Gaining a follower yields increases in feeling
recognized, connected, and appreciated, although they are not as high as for the
replying and retweet categories. In addition, most subjects were indifferent
(neutral) or disagreed with the metrics, stating that they would feel the same as
before receiving the notification across all three categories. This is in direct
support of H1, which posits that reciprocal online interactions have the potential
to improve well-being. For retweeting, about half of users felt capable while the
other half were neutral, while replying yielded 42% as more capable and 42%
neutral. Overall, the majority of subjects did not find notifications to be
annoying, or submitted a neutral response.
In order to better understand this data, I compared ratios of the frequency of
users who agreed and those who were neutral along the specified dimensions. (I
did not include those who disagreed because there were relatively few responses

34
for this category.) For feeling recognized, replies garnered the highest
agree/neutral ratio (7.714), followed by retweets (4.55) and follows (3.31). Replies
increased feelings of connectedness by the largest amount, followed by retweets
and follows. Retweets made users feel most appreciated, followed closely by
replies and then follows. The majority of users reported feeling neutral about a
heightened sense of capability, but retweets provided a ratio of 1, .8 for replies
and .44 for gaining a follower. Overall, following resulted in the highest feelings of
recognition, replying resulted in highest connectedness and recognition, and
retweets resulted in highest levels of appreciation and recognition.

35
4.5. Explicit reactions to Twitter interactions
0
20
40
60
80
100
Gains a Follower
Recognized
Connected
Appreciated
Capable
0
20
40
60
80
100
Receives Reply
Recognized
Connected
Appreciated
Capable
0
20
40
60
80
100
Retweeted
Recognized
Connected
Appreciated
Capable

36
General perception of Twitter interactions: Users reported higher feelings of
connectedness through engaging with a Twitter network (61.7% agreed), it is
considered an outlet to express ideas (68.3% agreed), and interactions are
viewed as a source of stimulation (68%) and rewarding (67%). Although
stimulation, connectedness, and feeling rewarded are associated with well-being,
only 23% reported a boost in well-being when they tweet, and 46% outright
disagreed with this notion. However, when another user acknowledges the tweet,
63.9% of subjects showed a boost in well-being (26% neutral and 9.8%
disagree). This finding may result from the fact that following is a one-way
interaction, since, most often, user’s may join a subject’s Twitter network
automatically without any action by the subject or need for approval. However,
replying or retweeting requires that another user directly respond to a post that
the participant has published. Consequently, these may be considered more
interactive categories and subsequently resulted in greater increases across all
metrics except, “I would feel the same as before receiving the notification.” Out
of the five active feelings measured through the survey, capable consistently
garnered the lowest level of agreement and most respondent’s reported feeling
neutral.

37
Figure 4.6. General perception of Twitter interactions
user wellbeing
This hypothesis was tested using a stratified sample consisting of Less Frequent
and Frequent Twitter users. I deconstructed the evaluation categories in order to
effectively compare results between the two groups:
1. How users perceive their Twitter community: Identified structural and
perceived differences between Frequent and Less Frequent users
2. Expectations and hopes for Twitter interactions: Compared feelings of
support emanating from Twitter network and interactions
3. Reactions to, and perceptions of Twitter interactions: Compared the
perceived ability of the Twitter network to result in specified outcomes
Sample characteristics (independent variables): 35 subjects were characterized as
Frequent users (check their account at least once per day and tweet at least once
0.00%
20.00%
40.00%
60.00%
80.00%
100.00%
Boosts in Wellbeing for Tweeting and When Tweet is
Acknowledged
Boost in wellbeing when
tweet
Boost in wellbeing when
tweet is acknowledged

38
per day) while 26 subjects were deemed Less Frequent users (check their account
at most multiple times per day and tweet at most once per week). Users who
check their account multiple times per day but tweet at most once per week were
categorized as Less Frequent users because they do not attempt to directly
engage with their Twitter network on a daily basis, but rather monitor their
accounts through reading Twitter feeds (defined as “checking the account.”) On
average, Frequent users had more followers (613) than followees (499) while Less
Frequent users had fewer followers (103) than followees (139).
Figure 4.7. Structural configuration of Twitter networks
Frequent and Less Frequent users were split fairly equally along the dimensions
that evaluated friendship on Twitter; the majority of the stratified sample agreed
that they consider less than 25% of contacts with whom they interact with on
Twitter to be friends, and most communicate with less than 25% of contacts via
additional channels.
-2000
0
2000
4000
6000
8000
10000
NumberofContacts
Frequent Users Less Frequent Users
Number of Followers and Followees: Less Frequent
and Frequent Users
Followers
Followees

39
Figure 4.8. Perception of friendship on Twitter (Less Frequent and
Frequent users)
1. How users perceive their Twitter community:
65% of Less Frequent users monitor other contacts’ activity, but do not interact
directly (only 23% of Frequent users agreed with this statement.) Both groups
0%
60%20%
6%
14%
0%
69%
20%
11% 0%
Frequent Users
None
Less than 25%
26%-50%
51%-75%
76%-100%
Outside circle: Out of followers, what percent do you communicate with via other channels?
Inside circle: What percent of contacts with whom you interact on Twitter are considered friends?
8%
46%31%
4%
11%
4%
65%
19%
8%
4%
Less Frequent Users
None
Less than 25%
26%-50%
51%-75%
76%-100%
Outside circle: Out of followers, what percent do you communicate with via other channels?
Inside circle: What percent of contacts with whom you interact on Twitter are considered friends?

40
reported high rates of similarity with their Twitter community (65% Less
Frequent; 77% Frequent) and the groups had analogous rates for those contacts
they consider to be part of their professional network (46.15% Less Frequent;
48.57% Frequent.) Regarding the closeness of Twitter connections, 8% of Less
Frequent users stated that contacts are part of their close personal community
and interactions are personal, while Frequent users noted that 23% were part of a
close community and 34% engage in personal interactions. Both scored low on the
provision of emotional support (16% Less Frequent; 23% Frequent). If someone
tweeted a problem, 50% of Less Frequent users would feel concern and 35% would
contact that user, while 74% of Frequent users would feel concern and 54% would
contact the user. Overall, Less Frequent users were not as likely to feel concern
for another contact or reach out if that contact was having a problem. Based on
this data, the only structural differences in the network configurations were the
average number of followers and followees for the two groups, the fact that most
Less Frequent users monitor activity and do not directly interact on Twitter (65%
compared to 23% for Less Frequent), and average Twitter use, namely how often
subjects tweet and/or check their account.

41
Figure 4.9. Structural and perceived differences in Twitter network (Less
Frequent and Frequent users)
2. Expectations and hopes for Twitter interactions
The groups reported similar findings for “hope for acknowledgment,” (48% Less
Frequent; 51% Frequent.) However, only 54% of Less Frequent users are
comforted by the fact that someone might acknowledge their contribution,
compared with 89% for Frequent users. In addition the majority of Less Frequent
users do not care if the tweet is acknowledged (69% Less Frequent; 43% Frequent)
and fewer expect a response (4% Less Frequent; 20% Frequent). Regarding
network responsiveness and perceived support, 15% of Less Frequent users believe
someone would respond if they tweet a problem compared with 57% of Frequent
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
Professionalnetwork
Closepersonal
community
Interactionsarepersonal
Iwouldfeelconcernif
someonetweeteda
problem
Ifsomeoneposteda
problem,Iwouldcontact
them
Sharemanyinterestswith
contacts
Monitorbutdonot
interact
Networkprovides
emotionalsupport
Percent of Users Who Agree with Stated Metrics: Frequent
and Less Frequent Groups
Frequent
Less Frequent

42
users. In addition, only 22% of Less Frequent subjects believe that someone will
respond to their question, compared with 53% of Frequent users. In addition,
none of the Less Frequent users reported that Twitter could help them feel better
if they were down in the dumps, compared with of 17% Frequent users. It is clear
that both groups do not rely on their Twitter network for social support and, for
the most part, do not care or expect their tweets to be acknowledged by their
contacts (although Frequent users care 30% more than Less Frequent users).
Both groups hoped for acknowledgment from other users and are comforted by
the fact that someone may recognize their contribution to the Twitter community
(although Frequent users are more comforted than Less Frequent). In addition,
Frequent users are more inclined to believe that their Twitter community will
respond if they are having a problem or pose a question. This discrepancy may be
due to the fact that 77% of Frequent users interact on Twitter and do not just
monitor other contacts’ activity, while 65% of Less Frequent users monitor and do
not interact. Consequently, engaging with Twitter contacts may enact the
reinforcing mechanisms (exhibited in Figure 1.1) and compel Frequent users to
continue interacting with their network.

43
Figure 4.10. Expectations and hopes for Twitter interactions (Frequent and
Less Frequent users)
III. Reaction to, and perceptions of, Twitter interactions:
Regarding specific reactions to gaining a follower, receiving a reply, and being
retweeted, Frequent users reported higher levels of agreement along all metrics
except “I would feel the same” (Less Frequent users were more likely to agree
that following, replying, retweeting, or not receiving a response from another
user, would result in them feeling the same as before receiving the notification.)
Overall, Frequent users showed significantly higher levels of agreement for feeling
recognized when they gain a follower than Less Frequent users (83% for Frequent;
58% for Less Frequent). However, when the interaction consisted of a reply or a
retweet, both groups reported relatively high levels of agreement for recognized,
connected, and appreciated, and Less Frequent users exhibited significant
increases in agreement for a retweet or reply. According to Figure 4.11, which
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
Rewarding
Outlettoexpress
ideas
Sourceofstimulation
WhenItweet,feel
boostinwellbeing
Whentweetis
acknowledged,feel
boostinwellbeing
Networkissourceof
encouragement
Feelconnectedto
largercommunity
Percent of Users Who Agree with Stated Metrics: Frequent and
Less Frequent Groups
Frequent
Less Frequent

44
compares reactions between the stratified sample, the increase is represented by
the change from blue responses to green and orange responses. The graph
visualizes the difference in rates of agreement for Frequent and Less Frequent
Users across three scenarios (gaining a follower, receiving a reply, and being
retweeted) for recognized, connected, and appreciated (which were shown to
garner the highest response rates for metrics associated with positive affect when
testing H1). The effect is especially pronounced for Less Frequent users in
reported feelings of connectedness, followed by the second largest increase in
feelings of recognition.
Figure 4.11. Reactions to gaining a follower, receiving a reply, and being
retweeted (Frequent and Less Frequent users)
In terms of capability, the groups both showed relatively low levels of agreement
compared with other metrics. However, receiving a reply and being retweeted
resulted in higher feelings of capability for both user groups.
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
Recognized Connected Appreciated
Percent of Users Who Agree with Stated Metrics: Frequent
Follow: Frequent
Follow: Less Frequent
Reply: Frequent
Reply: Less Frequent
Retweet: Frequent
Retweet: Less Frequent

45
Figure 4.12. Reaction to feeling capable (Less Frequent and Frequent
users)
Data regarding whether gaining a follower, receiving a reply, or being retweeted
did not affect the user (“I feel the same as before receiving the notification”)
demonstrated that Less Frequent users are more likely to disagree with this
statement (Figure 4.13). In other words, Less Frequent users are more likely to
acknowledge that these online interactions produce some effect. These reports are
particularly distinct for receiving a reply and being retweeted. Overall, gaining a
follower is least likely to affect users.
0.00%
20.00%
40.00%
60.00%
80.00%
100.00%
Gain follower Receive reply Retweeted
Percent of Users Who Feel More Capable: Frequent and Less
Frequent Groups
Frequent
Less Frequent

46
Figure 4.13. Users who feel the same after receiving a notification (Less
Frequent and Frequent users)
General reactions to Twitter community: Frequent users experienced higher levels
of agreement along all metrics, including feeling rewarded, stimulated, and
connected to a larger community. Using the 50% threshold for significance, both
groups believe utilizing Twitter is stimulating, while Less Frequent users had
fewer than 50% in agreement across the remaining dimensions. In addition, more
Frequent users felt Twitter is an outlet to express ideas compared with Less
Frequent users. Both groups reported relatively low levels of agreement for the
item measuring whether subjects experienced boosts in wellbeing when they
tweet. However, when the tweet is acknowledged, both agree that they feel a
boost in wellbeing.
In order to more precisely compare responses between Frequent and Less
Frequent users, I categorized reactions based on whether subjects agreed, were
neutral, or disagreed. The dimensions that displayed the largest discrepancy
between responses, including feeling rewarded, connected to a larger community,
and that Twitter is an outlet to express ideas, may be the result of reciprocal
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
Gain follower Receive reply Retweeted
Percent of Users Who Feel the Same When Receive
Notification: Frequent and Less Frequent Groups
Agree: Frequent
Disagree: Frequent
Agree: Less Frequent
Disagree: Less Frequent

47
interactions; since the Less Frequent group does not usually interact through
Twitter, they likely experience these interpersonal benefits less often than the
Frequent users, or not at all. However, both groups scored low on “When I tweet,
I feel a boost in wellbeing,” but relatively similarly on “When my tweet is
acknowledged, I feel a boost in wellbeing.” This supports the notion that actual
or perceived benefits are related to reciprocity rather than the one-way projection
of information.
4.14. General reaction to Twitter network (Frequent and Less Frequent
users)
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
Rewarding
Outlettoexpressideas
Sourceofstimulation
WhenItweet,feelboostin
wellbeing
Whentweetis
acknowledged,feelboostin
wellbeing
Networkissourceof
encouragement
Feelconnectedtolarger
community
UseTwittertowastetime
Sociallyisolated
Percent of Users Who Agree with Stated Metrics: Frequent and
Less Frequent Groups
Frequent
Less Frequent

48
Less Frequent users were more likely to disagree with all metrics except “I use
Twitter as a way to waste time” and using Twitter leads to social isolation. The
level of disagreement among this group significantly decreased between “When I
tweet I feel a boost in wellbeing” and “When a user acknowledges my tweet I feel
a boost in wellbeing.” Less Frequent users showed higher levels of disagreement
for feeling connected to a larger community and that engaging with their Twitter
network is rewarding. Response rates were most similar for “When I tweet I feel a
boost in wellbeing,” “Twitter is a way to waste time,” and Twitter makes me feel
“socially isolated.”
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
Rewarding
Sourceofstimulation
wellbeing
Whentweetisacknowledged,
feelboostinwellbeing
Networkissourceof
encouragement
community
Sociallyisolated
Percent of Users Who Feel Neutral About Stated Metrics:
Frequent and Less Frequent Groups
Frequent
Less Frequent

49
Figure 4.15. Users who disagree with metrics (Frequent and Less Frequent)
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00% Rewarding
Sourceofstimulation
wellbeing
Whentweetis
acknowledged,feelboostin
wellbeing
Networkissourceof
encouragement
community
Sociallyisolated
Percent of Users Who Disagree with Stated Metrics: Frequent
Frequent
Less Frequent

50
Chapter 5
Discussion
This study confirmed the proposed hypotheses. Reciprocal online interactions
enhanced well-being for users, most notably through a perceived boost when a
tweet is acknowledged as well as based on feelings of heightened recognition,
connectedness, and appreciation, especially in the retweet and reply scenarios. In
support of the second hypothesis, Frequent users expected their Twitter networks
to be more responsive than Less Frequent counterparts, and demonstrated higher
levels of positive sentiments across all metrics. However, as a pilot study with
only 61 subjects, the results are promising but cannot be generalized. For
example, gender (Haines, Begges, and Hurlbert, 2008), age, baseline levels of
social support (Kaul and Lakey, 2003), measures of introversion and extroversion,
as well as general disposition, are known to affect feelings of support and modify
the influence of social contexts (Steinfield, Ellison, and Lampe, 2008). In
addition, certain types of individuals might gravitate toward Twitter, thereby
skewing results. Without a detailed assessment of subjects’ underlying
characteristics, it is difficult to identify the specific processes that led to observed
outcomes. Nevertheless, noted boosts suggest the possibility for more in-depth
work dedicated to understanding the associations between network configurations,
direct interactions, and subsequent user reactions.
The data is largely consistent with the theory of perceived companionship. First,
it is likely that Twitter users do not know their contacts on a personal level.
Therefore, the friendship bond that traditionally underpins companionate

51
interactions is, in fact, nonexistent (Rook, 1987). In addition, connections are not
pursued for social support and subjects feel affinity toward their network based
on shared interests. However, the data does not indicate whether these positive
outcomes are in response to the content of the tweets or, alternatively, a reaction
to the perceived intent of the user, as postulated by the theory of perceived
companionship.7
The diminished importance of message content is an integral
component of this construct because it applies heightened significance to the role
of subjective perception in online communication. Moving forward, research
should evaluate why users exhibit certain responses to specific types of
interactions in order to understand the affordances of various forms of exchange.
Companionship is generally measured by whether subjects believe they interact
with people who share their interests, have individuals they can get together with
and have fun, and overall satisfaction with these relationships (Wright, 2000). It
has also been discussed in the context of parasocial interactions, whereby
relations between humans and social robots are evaluated for their ability to
provide comfort or stimulation (Leite et al., 2010). The theory of perceived
companionship explores the possibility of a type of relationship that falls
somewhere between a parasocial and companionate interaction. As previously
stated, researchers have just begun to investigate Twitter’s capability as a
powerful medium for fostering feelings of perceived social support (Dodds et al.,
2011). The data indicates that, although users might not be directly connected
through additional channels, subjects may nonetheless accrue some personal
benefit through interactions with these weak ties, especially during interactive
exchanges such as retweets and replies. Wellman, Gruzd, and Takhteyev (2011)
evaluated real and “imagined” communities on Twitter and found that both
7
Anecdotal accounts were obtained for select survey respondents that described a tweet or
exchange that they found particularly meaningful. (Appendx: Section 7).

52
friends and non-friends are equally connected to the subjects’ network and
display varying degrees of interpersonal commitment. In this study, the inference
that interactions between purported weak ties produces a boost in well-being
merits further exploration into how network membership affects the perception of
interactions and subsequent health outcomes.
As mere snapshots of subject perception, it is difficult to understand if and how
elevated levels of well-being may impact users beyond isolated interactions.
Despite this dearth of information, the fact that more than 50% of Less Frequent
and Frequent users exhibited boosts along a number of dimensions suggests that
the potential for positive feelings to be channeled toward beneficial activities.
Consider the following quotations:
“Transient happy moods lead people to seek out others and engage with the
environment, to be more venturesome, more open, and more sensitive to their
individuals” (Lyubomirsky, King, and Diener, 2005; pp. 836)
“We perceive social isolation when social opportunities and relationships do exist,
but we lack the capacity to harness the power of social connectedness in everyday
life (Hawkley and Cacioppo, 2010; pp. 224)”
“Our biggest problems have no technological solution. We have come through the
industrial age, the information age. Now we need to prepare ourselves for what I
call the human engineering age and address the relationships which enable
societies to work” (Herbert, 2011).
According to Heinz Wolff, we cannot employ technology to “address the
relationships that enable societies to work” (Herbert, 2011). However, instead of
serving as a solution, mediated communication may be used as a tool to inspire
feelings that enable individuals to lead more productive lives. Emboldened by

53
even slight boosts in optimism, energy, belongingness, and self-efficacy, people
may be compelled to take advantage of opportunities to further enhance their
well-being or the happiness of others. Findings may also be applied toward
measures aimed at preventing negative affect and associated psychological
disorders. Specifically, a lack of pleasurable experiences have been cited as the
cause of some mental illnesses, such as depression, and regular small bursts in
well-being could help individuals maintain elevated levels of contentment
(Lyubomirsky, King, and Diener, 2005). The World Health Organization
estimates that by the year 2030, depression will be the number one source of
disability in both developed and developing countries (World Health
Organization, 2008). In the United Kingdom, the government has undertaken a
nation-wide initiative to measure the well-being of 200,000 citizens in order to
inform a range of public policies (UK National Statistics, 2011). For example,
according to the cabinet secretary, improving the mental health and well-being of
the unemployed could motivate them to find work (Ramesh, 2007), which would
benefit society at-large. As such, there is a clear need for ways to effectively
enhance well-being and increase happiness.
In light of the findings, it might be more accurate to frame the hypotheses in
terms of mediated communication rather than online communication, focusing on
environments that, like Twitter and text messaging, may be analogously
information-poor. Subjects’ perceived similarity and care for Twitter contacts
considered to be weak ties, demonstrated boosts in well-being, heightened
responses associated with replying and rewteeting, a lack of expectation regarding
the responsiveness of the community, minimal disappointment when a post is not
recognized by others, and increased feelings of comfort, among other notable
findings, suggest the possibility for a much larger, more impactful conclusion

54
regarding how and why we communicate online and ways in which
correspondences and programs may be directed to improve health.

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