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Social network sites (SNSs) like Twitter continue to attract users, many of whom turn to these spaces for social support for serious illnesses like cancer. Building on literature that explored the functionality of online spaces for health-related social support, we propose a typology that situates this type of support in an SNS-based open cancer community based on the type (informational or emotional) and the direction (expression or reception) of support. A content analysis applied the typology to a 2-year span of Twitter messages using the popular hashtag "#stupidcancer." Given that emotions form the basis for much of human communication and behavior, including aspects of social support, this content analysis also examined the relationship between emotional expression and online social support in tweets about cancer. Furthermore, this study looked at the various ways in which Twitter allows for message sharing across a user's entire network (not just among the cancer community). This work thus begins to lay the conceptual and empirical groundwork for future research testing the effects of various types of social support in open, interactive online cancer communities.
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Health Communication
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#Stupidcancer: Exploring a Typology of Social
Support and the Role of Emotional Expression in a
Social Media Community
Jessica Gall Myrick, Avery E. Holton, Itai Himelboim & Brad Love
To cite this article: Jessica Gall Myrick, Avery E. Holton, Itai Himelboim & Brad Love (2015):
#Stupidcancer: Exploring a Typology of Social Support and the Role of Emotional Expression in
a Social Media Community, Health Communication, DOI: 10.1080/10410236.2014.981664
To link to this article: http://dx.doi.org/10.1080/10410236.2014.981664
Published online: 09 Oct 2015.
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#Stupidcancer: Exploring a Typology of Social Support and the Role of Emotional
Expression in a Social Media Community
Jessica Gall Myrick
a
, Avery E. Holton
b
, Itai Himelboim
c
and Brad Love
d
a
The Media School, Indiana University;
b
Department of Communication, University of Utah;
c
Department of Advertising and Public Relations,
University of Georgia;
d
Department of Advertising and Public Relations, University of Texas at Austin
ABSTRACT
Social network sites (SNSs) like Twitter continue to attract users, many of whom turn to these spaces for
social support for serious illnesses like cancer. Building on literature that explored the functionality of
online spaces for health-related social support, we propose a typology that situates this type of support
in an SNS-based open cancer community based on the type (informational or emotional) and the
direction (expression or reception) of support. A content analysis applied the typology to a 2-year
span of Twitter messages using the popular hashtag #stupidcancer.Given that emotions form the
basis for much of human communication and behavior, including aspects of social support, this content
analysis also examined the relationship between emotional expression and online social support in
tweets about cancer. Furthermore, this study looked at the various ways in which Twitter allows for
message sharing across a users entire network (not just among the cancer community). This work thus
begins to lay the conceptual and empirical groundwork for future research testing the effects of various
types of social support in open, interactive online cancer communities.
Traditional social support groups required meeting at specific
times, whereas online communities allow those who need
support to meet anytime and to come together no matter
their physical location. As such, individuals are increasingly
turning to computer-mediated forms of social support to deal
with serious illnesses like cancer. The use of such digital
spaces to connect with others can be beneficial for those
impacted by cancer (e.g., Han et al., 2011; Klemm, Reppert,
& Visich, 1998; Klemm & Wheeler, 2005). However, research
on evolving online platforms for social support, such as social
network sites (SNSs), suggests these channels are more open
to the public and include a larger number of connections than
previously studied online social support groups (OSGs). SNSs,
such as the microblogging site Twitter, add new wrinkles to
previously established typologies of social support. Digital
environments are increasingly public and interactive and pre-
sent many avenues for users to ask questions, share informa-
tion, offer encouragement, and express emotions related to
the realities of cancer. The characteristics of SNSs beg for new
approaches to investigating social support in a community
where the boundaries of who is a part of the cancer commu-
nity are broader than ever before.
Thepurposeofthepresentworkistoexploretheintersections
of social support, emotional expression, and message sharing in
an online grass-roots cancer community. Based on existing litera-
ture, we propose a typology for applying previous work on social
support to the limited-length, interactive, and public environment
of SNSs. These SNSs act as de facto social support groups for
cancer patients, physicians, survivors, family members, and others
affected by any type of cancer, whereas previous work analyzing
computer-mediated social support for cancer patients has typi-
cally focused on closed, patient-only platforms limited to specific
subtypes of cancer. We use a content analysis of the #stupidcan-
cerhashtag on Twitter to apply the proposed typology to SNS
content. This content analysis also provides insights into how
social media users express social support, receive social support,
and create emotion-infused content in this environment where
each message is limited to a mere 140 characters.
Studying the relationships between emotional expression
and social support variables provides additional insights as
to how the affective features of microblog posts relate to
message content and message sharing. Given that emotions
form the basis for much of human behavior as well as for
many forms of social support, discerning the nature of the
connection between emotional expression and online social
support is an important contribution of this study.
Furthermore, we analyze the various ways in which users
distribute cancer-focused social support message across an
SNS platform. Twitter allowsitsuserstopostoriginal
content (i.e., tweets), to share otherscontent (i.e.,
retweets), or to favorite specific Twitter messages so that
these messages are broadcast across a usersentirenetwork
and not just to those within a hashtag community. The
findings provide a conceptual and empirical groundwork
for future research testing the effects of social support in
SNS-based cancer communities.
CONTACT Jessicas Gall Myrick jgmyrick@indiana.edu The Media School, College of Arts and Sciences, Indiana University, Ernie Pyle Hall 200, 940 E. Seventh
St., Bloomington, IN 47405.
HEALTH COMMUNICATION, 2015
http://dx.doi.org/10.1080/10410236.2014.981664
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Literature review
Online social support
Human beings are social creatures and often turn to each
other for physical assistance and psychological sustenance
(Fiske, 2010). This relational nature of human life is at the
core of the concept of social support. Shumaker and Brownell
(1984) define social support as an exchange of resources
between at least two individuals perceived by the provider
or the recipient to be intended to enhance the well-being of
the recipient(p. 13). There are numerous links between
social support and physical and mental well-being (Reblin &
Uchino, 2008; Uchino, 2006; Uchino, Cacioppo, & Kiecolt-
Glaser, 1996). Social support can also alleviate the negative
effects of stress (Lieberman & Goldstein, 2005; Sarason,
Sarason, & Pierce, 1990). Moreover, social support offered
via the Internet can likewise contribute to an individuals
overall well-being (Klemm & Wheeler, 2005; White &
Dorman, 2001).
Types of social support
The concept of social support can be divided into subdimen-
sions, specifically informational support and emotional sup-
port (Sherbourne & Stewart, 1991; Uchino et al., 1996).
Informational support is the act of providing information
to aid another individual or group. In the context of SNSs,
information sharing can serve as informational support and
has been shown to help SNS users build their networks and
gain health-relevant knowledge (Eysenbach, 2008). As
Chung (2013) noted, OSGs rely on the clear conveyance of
information and intent between members to function effec-
tively. For instance, Wicks et al. (2012) found that sharing
information in an online epilepsy community within the
PatientsLikeMe SNS helped individuals manage their condi-
tions more effectively. Specific to cancer, Meier et al. (2007)
found that informational support was the most frequent
type of support offered in cancer-related Internet mailing
lists. One way that informational support in an SNS envir-
onment could differ from face-to-face informational support
is that SNS users can share information by including hyper-
linkstooutsidesourcesintheirmessages.Indeed,more
than a quarter of all messages on Twitter contain hyper-
links, which can help Twitter users extend the impact of
their 140-characters-or-less messages (Gao, Zhang, Li, &
Hou, 2012). Additionally, the use of hyperlinks in Twitter
can create denser social networks where interactions
between users become more personal and sustained (Hsu
&Park,2011).
The second component of social supportemotional sup-
portalso has a strong connection to the cancer experience.
Emotional support is the act of acknowledging or validating
another persons feelings or providing reassurance and
encouragement (Sherbourne & Stewart, 1991; Uchino et al.,
1996). Studies have shown that cancer patients rate emotional
support as the most effective kind of social support (Dakof &
Taylor, 1990; Dunkel-Schetter, 1984; Neuling & Winefield,
1988; Yoo et al., 2014).
Within these broad categories of informational and emo-
tional support, researchers have delineated more specific types
of social support in health-related networks. For example,
Han et al. (2011) classified expressions of empathy, offerings
of encouragement, requests of help, offers of prayer, mentions
of Christian religious beliefs, and general religious/spiritual
views as subtypes of emotional support. Additionally, studies
by Shaw, Hawkins, McTavish, Pingree, and Gustafson (2006)
found that insightful disclosure or a sharing of personal
experiences among breast cancer patients also operated as a
form of online social support and that this disclosure was
associated with a range of psychosocial benefits. Researchers
have also identified the expression of gratitude as an impor-
tant form of online social support (Klemm et al., 1998; Klemm
& Wheeler, 2005).
To summarize, there are two broad types of social support
that are applicable in a computer-mediated communication
environment: informational and emotional. Beyond these
broad categories, other scholars have classified more specific
types of social support including information giving, empathy,
encouragement, religious statements, disclosing personal
information, and giving thanks. We next discuss how the
direction of social support, in addition to the type of support,
is also an important component for understanding the ways
in which the cancer community can use SNSs for social
support purposes.
Direction of social support
Social support comes in various forms and, as an interperso-
nal action, comes with a direction. That is, individuals can
give and/or receive social support. Namkoong et al. (2010),
Han et al. (2011), Kim et al. (2012), and Namkoong et al.
(2013) distinguished between message reception and message
expression effects for a small group of breast cancer patients
using a closed online system. They found that expressing
social support benefited members of specific breast cancer
communities who used the closed system to connect with
others in similar situations. The expression of social support
is a dynamic process that ebbs and flows over time in digitally
connected cancer support groups. Yoo et al. (2013) found that
the expression of social support can vary based on individual
differences in age, living situation, comfort level with com-
munication technology, and cancer coping strategies. Given
the dynamic nature of the expression of social support in
online cancer-related networks, it is likely that cancer-related
social support flows in both directions within interactive SNS
platforms.
Social support on SNSs
This study adds to existing research by providing an alternative
classification system for social support expression and reception
in an SNS. A new typology is necessary due to the different
affordances of this spaceopen access to anyone with an
Internet connection, with any type of connection to cancer,
and norms of interactivity and message sharing. For instance,
the aforementioned studies operationalized social support
reception as passive exposure to social support messages.
2J. G. MYRICK ET AL.
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However, in an SNS like Twitter where most users can see and
respond to any other users messages, reception can be more
active. Twitter users who are not formally invited into a group
are able to find others with similar interests via a hashtag (i.e.,
#stupidcancer, #cancersucks) and can then request social sup-
port (be it informational or emotional) or openly acknowledge
provisions of social support. While reading a tweet containing
social support might not fully engage a user who is just brows-
ing through the website and incidentally encounters a cancer-
related message, there exists the opportunity (encouraged by
site features) to reply to or share the tweet containing social
support elements, thereby adding an element of action to pre-
vious conceptualizations of support reception as passive.
Notably, the space for active social support reception or
expression is quite limited on Twitter, which restricts mes-
sages to 140 characters. This feature makes the provision of
hyperlinks to outside content an important form of social
support yet to be thoroughly examined in the context of
online cancer communities. Hyperlinks allow users to
advance their interests in various ideas or opinions, thereby
providing context that compliments short tweets and allows
other users to further engage with the topic at hand (De
Maeyer, 2013;Hsu&Park,2011). While links promote
rapid knowledge sharing and acquisition (Hughes & Palen,
2009), they also allow for bonding between individuals. As
Holton, Baek, Coddington, and Yaschur (2014) illustrated,
Twitter users frequently seek and share information within
single tweets, forming sustainable communities around par-
ticular topics with the mutual exchange of information via
links.
In this regard, Twitter is not only a relatively new avenue
to obtain support and health information (Kim et al., 2012),
but is also an arena where communities of support can be
nourished through the exchange of content and links. Those
individuals who turn to the Internet for health information
often seek support from others in similar situations
(Eysenbach, Powell, Englesakis, Rizo, & Stern, 2004), suggest-
ing that SNS users have the opportunity to do the same.
Indeed, computer-mediated peer-to-peer support groups
have proliferated with the rise of the digital age, and social
media platforms have provided an additional outlet for indi-
viduals concerned about health issues to connect, talk with
each other, share information, and provide and/or receive
support (e.g., Himelboim & Han, 2014; Love et al., 2012).
A number of studies have championed the positive benefits of
OSGs, noting their ability to transcend temporal and geographical
restraints while offering spaces for patients, family members and
friends, physicians, and other health care professionals to connect
(Barak, Boniel-Nissim, & Suler, 2008;Hong,Peña-Purcell,&Ory,
2012). Others have observed that SNSs have emerged as spaces for
real-time conversations, greater heterogeneity among group mem-
bers, and community building (Dizon et al., 2012;Loveetal.,
2012). Individuals, groups, and organizations attempting to buck
the ephemerality of Twitter engagement can use hashtags to
solidify long-standing communities of Twitter users(Bruns &
Moe, 2014, p. 18). Hashtags help create a community wherein
users can share information and social support with each other.
Additionally, hashtags also allow users outside of the community
to observe and potentially join the conversations.
Given the unique properties of Twitter as an interactive
and open online support group for the cancer community
broadly definedwe propose a typology of social support
content for the SNS environment (see Table 1). The typology
is based on two dimensions: (a) type of support (informa-
tional or emotional), and (b) direction of support (expression
or reception). We situate specific subtypes of online social
support (e.g., information giving, empathy, information seek-
ing) into each of the four quadrants created by crossing these
dimensions. Given that the typology has yet to be applied to
SNS content, we propose the following research question:
RQ1: Which forms of social support (using the proposed typology
based on type and direction of social support) will be most
common in a Twitter-based cancer community?
Emotions, emotional expression, and social support
In addition to examining the type and direction of support,
another interesting aspect of social support messages on SNSs is
the sentimental, or emotional, currency community members
exchange with each other. In this context, distinctions can be
made between emotional support and emotional expression.
They are qualitatively different concepts and users have different
motivations for employing each in their online communications.
Emotional support, as described earlier, generally occurs when
sympathy or empathy is expressed or exchanged. Emotional
expression, on the other hand, is a communication about how
the user feels. Cancer is often associated with a plethora of emo-
tions, ranging from fear, anger, and sadness to hope and humor
(Mukherjee, 2010). Expressing these emotions via writing has
been shown to help cancer patients cope with the disease
(Pennebaker, 1997). Han et al. (2008)alsofoundthatfor
women with breast cancer who participated in a closed online
support group, expression of positive emotions reduced their
experiences of negative emotions. Studying the emotional content
of tweets is an especially valuable exercise because the collective
mood of Twitter content is linked with outcomes like stock
market swings, election results, and the spread of disease
(Bollen, Mao, & Zeng, 2011;DiGrazia,McKelvey,Bollen,&
Rojas, 2013;Signorini,Segre,&Polgreen,2011).
In the limited-space environment of SNSs, users may pur-
posefully include some form of emotional expression in a
message as a way to add meaning or forge connections with
others within the space limitations of the platform. Therefore,
emotional expressions may accompany informational forms
of social support in addition to existing alongside emotional
social support messages. It remains to be seen which discrete
Table 1. Proposed typology of online social support in an open interactive
network
Type of Support
Direction of
Support Informational Emotional
Expression Information giving, link providing Encouragement,
religion, empathy
Reception Information seeking, detailed
experiences
Giving thanks
HEALTH COMMUNICATION 3
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emotional expressions are tied with various forms of online
social support expression and reception.
An understanding of the emotional expressions in social
support messages could help inform current and future
research on the potential effects of SNS-based social support
because various emotions have different effects on informa-
tion processing and behavior. According to appraisal theory
(Lazarus, 1991), different discrete emotions are associated
with different signal values and action tendencies (see
Table 2). Furthermore, Nabis(2003) emotions-as-frames
model argues that even very subtle emotional primes can
shape how users respond to subsequent messages. This view
of emotions posits that messages that match the core rela-
tional theme and appraisals inherent in the emotional state of
a user are more accessible than messages inconsistent with the
users emotional state. Nabis work also demonstrates that
emotional reactions to messages influence how individuals
think about potential causes and solutions to problems.
Therefore, tweets containing social support messages related
to cancer may suggest solutions to cancer-related dilemmas
that could then become more or less palatable to other users
in the community, depending on their own emotional states.
Existing research provides clues as to which specific emo-
tions expressed via Twitter may be associated with social
support. A study of an online support group for cancer
caregivers found that hopeful statements were the most com-
mon type of emotion expressed in support group messages,
followed by mentions of being on an emotional rollercoaster
(Klemm & Wheeler, 2005). Additional work has found that
humor can also bring together individuals in a community by
fostering mutual identification and clarifying values (Meyer,
2000). Moreover, humor can help people cope with stressful
situations (Wanzer, Booth-Butterfield, & Booth-Butterfield,
2005), including illnesses (e.g., Christie & Moore, 2005;
Johnson, 2002). For instance, a study of breast cancer patients
found that the use of humor postsurgery predicted less dis-
tress (Carver et al., 1993).
Additionally, because cancer is a potentially deadly disease
filled with uncertainty and anguish, negative emotions such as
anger, fear, and sadness are unavoidable in this context. SNSs
may be effective outlets for expressing these feelings because
they provide users with opportunities to give or receive
encouragement for coping with such negative emotions. The
presence of negative emotional expressions in an SNS-based
cancer community may be one reason other users decide to
provide emotional social support, such as provisions of empa-
thy or encouragement, to other community members. The co-
presence of emotional expressions and these subtypes of social
support is worth investigating because emotional social sup-
port is associated with improved coping during stressful
events and/or health issues (e.g., Han et al., 2011). Based on
the aforementioned literature, we propose the following
research question:
RQ2: What is the relationship between emotional expression
and social support categories within a Twitter-based cancer
community?
Message sharing
One of the unique features of Twitter compared to closed
OSGs is that Twitter messages can be easily and quickly
shared both within the cancer network and to anyone else
who is connected to members of that network. Users can
share messages across Twitter via the retweet and favorite
functions. These allow Twitter users both to express their
interest in a tweet and to amplify the reach of that tweet by
distributing it to additional users in their networks.
Communication research also recognizes the role of emo-
tional content in motivating computer-mediated message
sharing. In an examination of the most shared online New
York Times articles, Berger and Milkman (2012) found that
stories with positive emotional overtones were more likely to
be shared than negative content, but stories that elicited
stronger, high-arousal emotions, regardless of valence, were
more likely to be shared than stories that did not spur strong
emotional reactions. Likewise, in a diary study of consumers
information sharing via mobile phones, more than 40% of all
shared information contained expressions of emotions, with
positive emotions more common than negative emotions in
the mobile messages that were shared (Goh, Ang, Chua, &
Lee, 2009). Based on the importance of emotional expression
in the spread of online content and the coexistence of emo-
tional and social support on SNSs, we ask the following
research question:
RQ3: Which types of discrete emotional expressions and social
support messages are related to message sharing in a Twitter-
based cancer community?
Methods
The specific OSG chosen for this study was created by an
organization called Stupid Cancer. Responding to a perceived
need for broader conversations on cancer, Stupid Cancer
aimed to engage the full spectrum of individuals affected by
cancer. The organization was particularly interested in enga-
ging younger people and those not otherwise well represented
by medical or advocacy groups that tend to be diagnosis
specific or treatment specific (StupidCancer, 2014). For
Stupid Cancers cause, SNSs emerged as an essential tool for
Table 2. Psychological properties of discrete emotions coded for in #stupidcancer tweets.
Emotion Signal Value Function Action Tendency Valence
Hope Chance of improvement in situation Perseverance in the face of challenges Mobilization/vigilance/commitment Positive/mixed
Fear Danger Protection Revise existing plan/create new plan Negative
Happiness/humor Progress toward goal Self-reward Bask/bond Positive
Anger Obstacle Remove obstacle Attack/reject Negative
Sadness/despair Failure Learning/recuperation Review plan/convalesce Negative
Source. Dillard and Peck (2001) and Lazarus (1991).
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several reasons. The organization developed around the same
time SNSs began launching and growing, making digital com-
munity building central to the organizations ethos
(StupidCancer, 2014). Also, its target population of young
adults has been a group more likely than other demographic
cohorts to engage in online social networking (Chou, Hunt,
Beckjord, Moser, & Hesse, 2009). Lastly, with the mission to
broadly reach those affected by cancer regardless of diagnosis
or location on the treatment spectrum, digital media can offer
a unique platform to work around barriers to support and
access. By participating in a hashtag community focused on
#stupidcancer, individuals can break out of diagnosis-specific
silos to gather information about the broader, human experi-
ences of cancer. Moroever, with the potential for anonymity,
SNSs offer a platform for those in the cancer community to
ask and/or answer the delicate questions common to cancer.
These include questions related to issues of sexual function,
physical markers of surgery, or emotional trauma, all of which
are not always easy to discuss face-to-face or in a public way.
By providing a space for discussing and learning about these
issues across cancer experiences, the #stupidcancer hashtag
community offers a wide breadth of online support social
support content.
Sampling procedure
Data were collected using NodeXLsTwitter Search importer
(Hansen, Shneiderman, & Smith, 2011), which was set to
identify the most recent 1,000 Twitter users who included
the hashtag #stupidcancerin their tweets. This collection
method results in topic-networks with the host as the topic.
The Twitter Application Programming Interface (API), when
data collection started, limited the amount of content that can
be downloaded to about 1,000 users per data set. For purposes
of standardization, data were collected every Tuesday,
Thursday, and Saturday at 4:30 p.m., every week for 2 years
(September 15, 2011, to September 17, 2013).
The collection process resulted in 76,806 tweets, includ-
ing many duplicates, an expected phenomenon considering
the popularity of retweeting on Twitter. After removing
duplicates, these tweets were condensed into a list of
unique tweets (n= 18,571), while calculating the frequency
(i.e., total number of retweets plus the total number of
mentions) of each tweet. The distribution of these frequen-
cies followed the power-law degree distribution, a heavily
skewed distribution where a few tweets were retweeted
many times, and others much less often. A simple random
sample was therefore inappropriate as it is likely to miss
the most retweeted messages. Therefor, a stratified sample
procedure was applied based on the 10th percentile points
of the accumulating frequencies. For instance, the first
stratum of tweets was defined so the accumulating fre-
quencies of its tweets added up to about 10% of all tweets.
The number of unique tweets in each stratum differed, but
the number of tweets each captured remained fairly similar
(about 7,000). The power-law distribution is characterized
by a fat tail,meaning low frequencies are more common
than high frequencies. For this reason, tweets with a fre-
quency of 4 captured 20% of the tweets and those with a
frequency of 3 captured about 30%. About 200 tweets were
randomly sampled of each 10% of the data (see Table 3),
providing for a final sample of 1,957 tweets. The resulting
dependent variable is of an order type.
Coding procedure
The authors developed a coding scheme to operationalize
social support and emotional expressions found in tweets
using the #stupidcancerhashtag. Two trained coders per-
formed two pretests on data not included in the sample of this
study establish intercoder reliability (ICR). The first pretest
resulted in an overall ICR of .68, below the acceptable
Krippendorfs alpha of .80 or higher. The coders met with
the secondary author to again go through the coding scheme
and procedure, producing an overall ICR of .80 on a second
pretest. Each of the coders then coded approximately half the
sample used in this study, including an overlapping portion of
20% of the sample to obtain the ICR scores reported in the
following.
Information giving (ICR = .92) was defined as providing
information to others (e.g., when youre nauseous from
chemo, eat ice cream), while information seeking
(ICR = .90) was defined as asking others for guidance
(e.g., anybody know what I should do with this diagno-
sis?). Tweets providing encouragement were those that
told an individual he or she could do something
(ICR = .86). Sharing individual experiences (e.g., Iacci-
dentally swallowed a suppository today, oops)wascoded
as a form of social support because it helps users relate to
each other and realize they are not alone (ICR = .84).
Giving thanks (e.g., Im grateful for the nurseshelp
today) also served as a form of social support because it
can help users recognize their own gratitude (ICR = .90).
Religious support (e.g., Im praying for you)wasanother
form of social support (ICR = .94). Empathetic messages
(e.g., Ifeelyourpain,or Im so sorry)comprised
another form of social support coded for in the study
(ICR = .86).
Five categorizations dealt with emotional expressions.
Messages expressing hope (ICR = .92) included explicit men-
tions of hope, optimism, optimistic, hopeful, or hopefulness,
as well as messages that had a strong implicit link to hope
(e.g., I have a gut feeling things will work out for you in the
end). Expressions of sadness or desperation (ICR = .88)
included messages discussing sadness, despair, or despon-
dency. Likewise, expressing fear (ICR = .88) involved com-
municating fear or anxiety or describing cancer, treatment, or
death as scary. Humor (ICR = .82) expressions included jokes
Table 3. Sampling selection by frequency of retweets.
Strata Frequency of retweets Tweets in sample (n)
90 24 200
80 1123 200
70 710 200
60 56 200
4050 4 400
1030 3 600
02 200
HEALTH COMMUNICATION 5
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about cancer as well as the use of phrases like lolor haha.
Expressions of anger (ICR = .88) included messages stating
frustration (e.g., I hate cancer,”“f.u. cancer,”“Im so mad
right now,etc.). Finally, message sharing, operationalized as
a tweet being retweeted or favorited so that other users who
follow the original user can likewise see the tweet, was auto-
matically coded by NodeXL (range = 1533, M= 8.51,
SD = 18.59).
Results
Descriptive statistics provide an overview of the nature of
thesample(seeTable 4). Nearly two-thirds (64.7%) of all
sampled tweets contained the social support element of
information sharing, and nearly one out of every five tweets
(19.7%) offered hope. About 12% of the tweets discussed
individual detailed experiences with cancer, while more
than three out every five tweets in the sample (60.9%)
included hyperlinks.
The first research question asked which types of online
social support, based on the proposed typology of type and
direction of support, would be most prominent in the current
sample. Two-thirds (66.6%) of all tweets in the sample
expressed some form of informational support (n= 1,304).
The next most common form of social support was informa-
tion reception, present in 12.9% of tweets (n= 252).
Emotional expression, present in 12.0% of tweets, was nearly
as common as information reception (n= 234), while emo-
tional reception was the least frequent type of social support
found in the sample (4.6%, n= 91).
The second research question asked which types of emo-
tional expression were associated with which types of social
support. Multiple hierarchical logistic regressions with
emotions as the predictor variables and separate analyses
for each type of social support were used to analyze the
relationships between these variables (see Table 5). The
results showed that hope was positively related to informa-
tion giving, encouragement, detailed explanations, giving
thanks, and religion, and negatively related to link giving.
Sadness was positively related to detailed explanation, reli-
gion, and empathy, and negatively related to information
giving, giving thanks, and link giving. Fear was positively
related to information seeking, detailed explanations, and
empathy, and negatively related to link presence. Humor
was positively related to giving thanks and detailed expla-
nations, and negatively related to link presence. Finally,
anger was positively related to detailed explanation and
negatively related to link presence.
Table 4. Elements of social support and emotional expression in tweets for
#stupidcancer.
Element Frequency (n) Percentage (%)
Social support
Information sharing 1266 64.7
Link presence 1,192 60.9
Individual experiences 229 11.7
Encouragement 133 6.8
Giving thanks 90 4.6
Empathy 55 2.8
Religious expression 45 2.3
Information seeking 24 1.2
Emotional expression
Hope 385 19.7
Desperation 206 10.5
Anger 61 3.1
Humor 29 1.5
Fear 10 0.5
Table 5. Multiple logistic regressions testing emotional expressions as predictors of different types of social support.
Informational
expression
Information giving Link providing
OR 95% CI OR 95% CI
Hope 1.98* [1.31, 3.00]* .36* [28, .46]*
Fear .00 [.00, -] .13* [.03, .61]*
Humor .00 [.00, -] .27* [.12, .61]*
Anger 1.89 [.73, 4.88] .25* [.14, .46]*
Sadness/Despair .43 [.17, 1.08] .06* [.04, .09]*
Informational
reception
Information seeking Detailed experiences
OR 95% CI OR 95% CI
Hope .83 [.28, 2.47] 4.16* [2.93, 5.89]*
Fear 10.31* [1.23, 86.17]* 12.07* [3.14, 46.48]*
Humor .00 [.00, ] 4.08* [1.62, 10.30]*
Anger .00 [.00, ] 2.85* [1.47, 5.53]*
Sadness/Despair .81 [.19, 3.51] 11.33* [7.86, 16.33]*
Emotional expression
Encouragement Religion Empathy
OR 95% CI OR 95% CI OR 95% CI
Hope 9.15* [6.23, 13.41]* 12.10* [5.80, 25.23]* 1.50* [.66, 3.42]*
Fear 2.38 [.26, 21.95] 8.51 [.86, 83.99] 15.72* [2.64, 93.69]*
Humor .73 [.09, 5.73] .00 [.00, ] .00 [.00, ]
Anger 1.17 [.34, 4.03] .83 [.11, 6.33] 1.80 [.58, 5.59]
Sadness/Despair .36 [.11, 1.16] 9.61* [4.26, 21.66]* 15.11* [8.27, 27.61]*
Emotional reception
Giving thanks
OR 95% CI
Hope 1.82* [1.15, 2.88]*
Fear .00 [.00, ]
Humor 4.29* [1.42, 12.95]*
Anger .00 [.00, ]
Sadness/Despair .22* [.05, .91]*
Note. OR = odds ratio; CI = confidence interval.
*p< .05.
6J. G. MYRICK ET AL.
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The third research question asked about the relationship
between social support, emotional expression, and message
sharing. In the sample, nearly all of the tweets (97.7%) were
shared at least once via either the retweet or favorite mechan-
isms on Twitter. MannWhitney U-tests were used to see
whether there was a significant difference between tweets
that contained each element of social support and emotional
expression based on the outcome of message sharing. This
statistical test, a nonparametric test of differences between
medians, was chosen because the continuous message-sharing
variable did not meet the assumptions of normality required
for parametric analysis (skewness = 15.55, kurtosis = 373.65).
In terms of types of social support associated with message
sharing, tweets including detailed experiences were signifi-
cantly less likely to be shared (Md = 3.53, n= 229) than
were tweets without detailed experiences (Md = 4.07,
n= 1,728), U= 163,236.00, z=1.23, p< .001. Tweets
including religious references were significantly less likely to
be shared (Md = 3.48, n= 45) than were tweets without
religious statements (Md = 3.97, n= 1,912), U= 35,176.00,
z=2.14, p< .05. Tweets including empathy were signifi-
cantly less likely to be shared (Md = 3.48, n= 55) than were
tweets without empathy (Md = 3.98, n= 1,902),
U= 41,844.50, z=2.58, p< .05. Tweets including informa-
tion giving (U= 94,566.50, z=1.54, p= .12), information
seeking U= 21,006.00, z=.47, p= .64), encouragement
(U= 115,467.00, z=1.08, p= .28), giving thanks
(U= 82,340.0, z=.50, p=.62), and link presence
(U= 440,514.50, z=1.29, p= .20) did not differ significantly
with regard to message sharing from tweets without those
variables.
In terms of the emotional expression variables, tweets
including expressions of sadness were significantly less likely
to be shared (Md = 3.30, n= 206) than were tweets without
expressions of sadness (Md = 4.18, n= 1,751), U= 119,436.50,
z=8.10, p< .001. Tweets including expressions of anger
were significantly less likely to be shared (Md = 3.43, n= 61)
than were tweets without anger expressions (Md = 3.98,
n= 1,896), U= 46,770.50, z=2.60, p< .01. Additionally,
tweets including expressions of fear approached significance
for being less likely to be shared (Md = 3.25, n= 10) than
were tweets without fear expressions (Md = 3.96, n= 1,947),
U= 6,658.00, z=1.76, p= .08. Tweets including expressions
of hope (U= 298,494.00, z=.48, p= .63) and humor
(U= 26,085, z=.63, p= .53) did not differ from tweets
without hope and humor, respectively, based on message
sharing frequency.
Discussion
This study makes multiple contributions to the literature on
online social support for individuals affected in some way by
cancer. First, it provides researchers with a typology of social
support that can be applied to modern formats for expressing
and receiving social support. Social support on Twitter is
different from social support in dedicated online support
groups because there is limited space (140 characters) to
express support and Twitter users can very easily (and are
encouraged to) share social support messages with others both
in and outside of the cancer community (i.e., the rest of their
social network). Therefore, it is important that research in this
arena recognize the unique technological affordances and
constraints present in popular SNS sites. Future work could
use experimental and longitudinal designs to test how each
type and direction of online social support influences users
who seek or give social support.
Related to the typology, another important contribution of
this study is a reconceptualization of social support reception
as an active, dynamic process that involves asking for support.
Previous work had conceptualized this activity as passive
reception, or reading, of support-giving messages. Based on
empirical work about the use of social media spaces, we know
that Internet users are not passive consumers of social media.
By expanding the conceptualization of social support recep-
tion to be active, this study will hopefully spur additional
research that addresses and tests the interactivity of Twitter
and similar SNSs as spaces for social support.
Furthermore, the delineation of emotional expression as
something separate from social support in an online interac-
tive cancer community is a meaningful aspect of the present
work. Previous research defined emotional support as
empathic messages, and empathy is a joint affective and
cognitive process. Emotional expression, on the other hand,
includes a wide range of discrete affective processes and does
not always coincide with the provision of social support.
Emotional expression may be used in the shortened space of
Twitter to improve the chances someone will read a message.
Expressions of feelings may also be used to foster social
connections despite the limited number of characters per
message.
This study also makes a contribution to the literature on
computer-mediated social support by analyzing the links
between emotional expression, social support, and message
sharing. Easy message sharing (i.e., retweeting, favoriting) is a
unique feature of modern social media compared to closed
systems. Emotions spread quickly via these online social net-
works. The inclusion of emotional expression alongside can-
cer-related social support messages may help them reach
wider audiences and may help support longer lasting net-
works wherein emotions can be shared freely and recipro-
cated, helping to build stronger bonds between users.
Specific findings from this study also merit discussion. In
the present data, the most common type of social support was
information expression, followed by information reception
and emotional support expression, with emotional support
reception representing the least common type of social sup-
port. All of the emotions measured in the present data were
significantly and negatively related to link giving, possibly
indicating that the limited space available in a microblogging
network may force community members to chose between
expressing their feelings and providing links to additional
information. Emotional expression was also tied to various
types of social support. Despite the inherent distress of the
disease, hope was the most common and fear was the least
common emotion expressed in this sample. This finding sug-
gests that SNSs are a space where members of the cancer
community are largely optimistic, perhaps inline with the
trope of a cancer patient being a fighter (Achterberg,
HEALTH COMMUNICATION 7
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Matthews-Simonton, & Simonton, 1977). Qualitative work
could delve more richly into the purpose, benefits, and costs
of different emotional expressions in this context.
In the sample described here, negative emotional expres-
sions were linked with less message sharing, but there were no
significant associations between positive emotions (hope and
humor) and message sharing. Although the means for mes-
sage sharing were higher when a Tweet included hope and/or
humor, this finding was not significant. The finding that the
expression of negative emotions makes a tweet about cancer
less likely to be shared supports the stated proposition that the
norm for discussions in a cancer community may be to
remain as optimistic as possible. This finding also conforms
to previous work that has found negative emotional content is
less likely to be shared than positive content. Collectively,
these findings underscore the necessity of studying the role
of discrete emotions in order to fully understand the nature of
this content and to form future hypotheses about its possible
effects on SNS users.
Of the types of social support measured in this study, the
inclusion of detailed information giving, religious references,
and empathy predicted less message sharing, while other
forms of social support reception and expression were not
significantly related to message sharing. It is possible that
these forms of social support prompted interactive replies (i.e.,
interpersonal communication) instead of message sharing to
larger audiences. Future work could employ surveys and/or in-
depth interviews to gain insights about the situations where
Twitter users share messages versus reply to them.
Additionally, one should consider that the lack of significance
for these particular analyses could be the result of the use of
nonparametric statistics, which are less powerful than para-
metric tests and therefore may have missed differences
between groups (Pallant, 2010). Future work using larger sam-
ples or resulting in variables that meet assumptions of normal-
ity could help overcome this limitation.
It also important to note some limitations of the present
work. Hashtags help build ad hocpublicsspaces where a
diversity of experience and expertise can coexist around a
topic of shared interest (Bruns & Burgess, 2011). As such,
hashtag communities like #stupidcancer may be comprised of
many types of individuals from patients, family members and
friends, physicians, oncology specialists, and researchers, to
members of the media and beyond. The role of individual
perceptions, experiences, and motivations should be consid-
ered as research in this area advances. The findings here are
also limited to one cancer network studied over a contained
time period and therefore are not generalizable to all SNS-
based cancer communities.
Despite these limitations, our findings have theoretical
implications for the study of online forms of support, infor-
mation seeking, and information sharing. For instance, mod-
els describing or predicting how members of the cancer
community use SNS for social support would be wise to also
include affective factors in their calculations. While many
traditional models of health and communication behavior
have glossed over or oversimplified the role of emotions,
this study contributes to a growing body of literature demon-
strating that discrete emotions have important and different
effects on communication-related outcomes (Nabi, 1999,
2010). For cancer communication, in particular, discrete emo-
tions are an important component of understanding which
messages impact individuals (Dillard & Nabi, 2006).
Conclusion
SNSs like Twitter offer members of the cancer community the
opportunity to connect with each other any time of day or
night from nearly any place in the world. Given the impor-
tance of social support in dealing with the uncertainty, phy-
sical pain, and emotional strain that come along with a cancer
diagnosis, it is important for health communication research-
ers to understand the nuances of this particular online cancer
community. By analyzing 2 years of content from a popular
Twitter-based cancer community, we were able to apply a
newly proposed typology of the type and direction of social
support to actual content. This research provides new insights
into the nature of this content and its interconnectedness with
SNS features like message sharing and the presence of hyper-
links and emotional expressions. As scholars have noted,
social media platforms tend to be viewed as giant spheres
where entire communities meet, rather than as what they
area series of intricately woven and interconnected commu-
nities formed by individuals who have very specific needs,
motivations, and expectations (Bruns & Moe, 2014). By utiliz-
ing new approaches that consider the distinct ways in which
social media communities centered on cancer develop and
thrive, scholars can begin to gain deeper insight as to the
ways in which users turn to these spaces for social support.
This study developed one such approach, offering a direc-
tion that considers the connections between the types and
direction of social support, emotional content, and message
sharing in a cancer-focused social media space. Given that
message sharing can amplify the reach and impact of health-
related messages such as those analyzed here, it is likewise an
important variable to study in the context of Twitter-based
cancer communities, which continue to grow and evolve. The
findings in this study provide a theoretical and empirical
foundation for future work that could employ diverse meth-
ods to test the effects of the type, direction, and emotional
tone of tweets about cancer. The widespread impact of this
disease on individualsof whom an increasing number turn
to social media for information, guidance, and kinship
makes the study of online cancer communities an imperative
and promising area of future research.
Acknowledgments
The authors thank Mindy Krakow and Stacey Overholt from the
University of Utahs Networked Health Lab for their assistance with
coding. We also thank the peer reviewers for their thoughtful comments.
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... However, sadness, hope, and fear are not expected to affect user engagement with cancer stories for several reasons. First, as hope and sadness are characterized by low arousal, their presence in cancer stories should decrease the intensity of vicarious experience and thus not affect user engagement (Kim et al., 2016;Myrick, Holton, Himelboim, & Love, 2016;Wang et al., 2019). Myrick et al. (2016) even found that cancer-related tweets with sadness were less likely to be shared. ...
... First, as hope and sadness are characterized by low arousal, their presence in cancer stories should decrease the intensity of vicarious experience and thus not affect user engagement (Kim et al., 2016;Myrick, Holton, Himelboim, & Love, 2016;Wang et al., 2019). Myrick et al. (2016) even found that cancer-related tweets with sadness were less likely to be shared. This finding is consistent with the appraisal theory: The behavioral tendency of sadness is inaction and withdrawal (Lazarus, 1991;Nabi, 1999). ...
... This is inconsistent with the literature on the positive effect of emotional arousal on engagement (Berger & Milkman, 2012). Although previous research supports the positive effect of joy on retweets (Kim et al., 2016;Wang & Wei, 2020), breast cancer support communities on Twitter primarily feature informational content related to cancer coping and are typically created by individual users (Myrick et al., 2016). As such, joy may be useful for increasing a survivor's efficacy to navigate their cancer journey. ...
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Amid emerging information and communication technologies with unique affordances for storytelling and story sharing, most studies in narrative communication still focus on narratives delivered through traditional mediums. There has been little research on how emotionally charged stories can be used to engage audiences on social media. This study examined the roles of emotions and emotional shifts on user engagement behaviors on Facebook. Analyzing Facebook narratives by multiple breast cancer organizations (N = 403), we found a primacy effect of emotions in social stories, as negative emotions in the initial segment of a story increased user engagement behaviors. Emotional shift patterns were associated with user engagement behaviors, with the shift from positive to positive being the least engaging. Our findings advance narrative communication science in the social media context and offer important implications on how organizations can use social media to tell emotionally engaging stories.
... Within health-related topics, specifically communication among breast cancer survivors as there is little current work on previvors, social media is used to gain information, connect with loved ones, find support, and communicate with others facing similar experiences (Koskan et al., 2014). Social media sites commonly function as spaces of support for patients, especially those with cancer (Han et al., 2011;Myrick et al., 2016). Recent scholarship emphasized the impact and potential utility of social media use for breast cancer survivors (Foley et al., 2015;Myrick et al., 2016). ...
... Social media sites commonly function as spaces of support for patients, especially those with cancer (Han et al., 2011;Myrick et al., 2016). Recent scholarship emphasized the impact and potential utility of social media use for breast cancer survivors (Foley et al., 2015;Myrick et al., 2016). A study by Foley et al. (2015) explained how breast cancer narratives are shared on YouTube video blogs. ...
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Research on previvors, individuals with a genetic predisposition to develop hereditary breast and ovarian cancer but who have not yet been diagnosed with breast or other cancers, examines online information gathering and community support to alleviate uncertainty. However, research exploring online content published by previvors themselves is limited. We examined content published to Instagram and TikTok to explore how breast cancer previvors discussed their lived experience which included, but was not limited to, genetic testing, diagnosis with a BRCA1/2 pathogenic (i.e. risk-increasing) variant, the decision to undergo preventative measures like surgery and/or reconstruction, and how they cope after diagnosis and surgical procedures. In the findings, we explicate how many previvors feel a responsibility to share their authentic experience on social media in order to help others and mitigate their own feelings of uncertainty. This study offers a snapshot of how women are sharing breast cancer previvorship and building social connections with each other online.
... Sebagai contoh, pada isu-isu yang menggunakan tagar #StupidCancer, terdapat ekspresi emosional dari tweet dengan tagar tersebut. Mereka memberikan dukungan sosial melalui online dan menyalurkan rasa empati mereka dengan kata-kata dan foto yang disertai tagar #StupidCancer (Myrick et al., 2016). Hal ini menunjukkan bahwa tagar begitu penting dalam meningkatkan emosi para komunikator ketika menerima informasi tersebut. ...
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Penelitian ini bertujuan untuk melakukan studi eksperimen pada mahasiswa di Jurusan Komunikasi Universitas Negeri Gorontalo terkait informasi yang berkaitan dengan polisi. Polisi memiliki citra negatif pada masyarakat Indonesia, khususnya generasi Z. Warganet menjadikan media sosial Twitter sebagai salah satu wadah untuk mengkritik polisi dengan tagar #1hari1oknum. Tagar tersebut menunjukkan tentang berita-berita mengenai perilaku beberapa anggota polisi di Indonesia. Selain itu, tagar tersebut juga merupakan upaya dari masyarakat untuk mengkritik polisi. Di sisi lain, polisi juga membuat akun Twitter @divhumas_polri untuk meningkatkan citranya. Penelitian ini menganalisis tentang pengelolaan informasi yang dilakukan oleh mahasiswa ketika diberikan informasi-informasi dari akun @divhumas_polri dan informasi berita dengan tagar #1hari1oknum. Penelitian ini menggunakan paradigma positivistik, pendekatan kuantitatif, dan teknik penyebaran kuesioner. Jumlah populasinya sebesar 120 mahasiswa yang dibagi menjadi dua kelompok, yaitu kelompok eksperimen dan kontrol. Selanjutnya, 60 mahasiswa tersebut dibagi lagi menjadi tiga kelompok berdasarkan tahun ajaran 2021, 2020, dan 2019. Hasil dari penelitian ini menunjukkan bahwa mahasiswa tidak memiliki rasa percaya yang tinggi terhadap polisi, terutama mahasiswa dari kelompok eksperimen yang menerima treatment berupa berita #1hari1oknum. Temuan pada penelitian ini juga menunjukkan bahwa mahasiswa mengelola informasi tersebut dengan jalur sentral. Sebagai generasi Z yang bergantung pada gadget, mahasiswa tidak langsung percaya pada informasi yang diterima dari @divhumas_polri dan berita #1hari1oknum. Mahasiswa mencari informasi pendukung dari sumber lain, melihat pemilik akun yang menyebarkan informasi, dan memperhatikan tagar yang digunakan. Penelitian ini diharapkan bermanfaat untuk akademisi di Komunikasi, polisi, dan masyarakat.
... While these categories of social support may not necessarily be mutually exclusive in their dispensation, their differential impact is worth noting. In multiple studies conducted in various settings [27][28][29], for example, emotional support was found to be the most useful of the different categories in enhancing mental health. While none of these studies was conducted in a sub-Saharan African setting, observations from our current and earlier studies undertaken in the Ugandan setting point to the critical role of especially emotional support in improving health out-comes for persons living with HIV/AIDS and depression comorbidity. ...
Article
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Introduction: Depression is the fourth leading cause of the global disease burden and worsens the outcome of comorbidities including HIV/AIDS. Depression is particularly problematic among persons living with HIV in sub-Saharan Africa where scarcity of cost-effective interventions is compounded by inadequate understanding of the disease. We examine risk factors for depression among persons living with HIV undergoing antiretroviral treatment in Uganda and discuss policy implications. Methods: A qualitative study using a narrative approach was conducted, the formative phase of a large study to develop a model for integrating depression management into routine HIV care in Uganda. Participants were purposively sampled at four public health facilities in Mpigi District. In-depth interviews were conducted with four clinicians, three supervisors, and 11 persons living with HIV and suffering from depression, as were three focus group discussions with lay health workers. Exit interviews were conducted with 17 persons living with HIV who completed/interrupted depression treatment but had not been interviewed. Only data collected from persons living with HIV and lay health workers were analysed for the purpose of this paper. A narrative thematic approach was used in data analysis. Findings. There were several pathways through which lack of family social support reportedly led to depression: worries about disclosure in discordant relationships, false perceptions of social support, stigmatisation and discrimination, and domestic violence. Economic/poverty and other causes were identified, but their role was less significant or moderated by family social support. Conclusion: Family social support plays a dominant role-both directly and indirectly-in influencing depression risk. We propose the mainstreaming of formal psychosocial support and a shift from individual to family-focused counselling that targets both persons living with HIV and their family.
... As noted earlier, an additional 22 at-risk patients expressed interest in the study during active recruitment; however, we had already reached saturation at nine couples and still enrolled six more couples to confirm saturation. Online forums and support groups are also important places for patients and partners to find support and seek information about ICR [53][54][55]. Nevertheless, important group differences occur between social media and clinic recruited samples (e.g., race, treatment, mental health) [52]. ...
Article
Abstract Objective: This study reports the feasibility, acceptability, and outcomes of a longitudinal, communication pilot intervention for patients with inherited cancer risk and their partners. Methods: Couples were recruited through social media and snowball sampling. At Time 1 and 2, 15 couples completed a structured discussion task about family building concerns and decisions, followed by an online post-discussion questionnaire and dyadic interview to provide feedback about the experience. Interview data were analyzed to assess outcomes using applied thematic analysis. Results: Participants reported the intervention created an opportunity for honest disclosure of family building goals and concerns. Participants also stated the structured nature of the discussion task was useful and did not cause additional stress. The intervention ultimately aided at-risk patients and their partners to realize their concordant concerns, discover/confront discordant concerns, and mutually agree upon next steps. Conclusions: This pilot intervention is feasible and acceptable. Furthermore, it offers a framework to facilitate effective communication about family building between patients with inherited cancer risk and their partners. Innovation: This intervention is the first conversational tool designed for at-risk patients and their partners.
... grupos de portadores de diabetes e de distúrbios mentais, sujeitos do estudo desses autores). Ao mesmo tempo, essas novas mídias têm uma grande amplitude de circulação, como sugerem Myrick et al. (2016), que examinaram a relação  377 entre expressões emocionais presentes no Twitter e suporte social on-line, salientando a capacidade de comunicação para uma ampla comunidade, tanto a partir de postagens originais, como de "repostagens" e de mensagens "favoritadas". ...
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... grupos de portadores de diabetes e de distúrbios mentais, sujeitos do estudo desses autores). Ao mesmo tempo, essas novas mídias têm uma grande amplitude de circulação, como sugerem Myrick et al. (2016), que examinaram a relação  377 entre expressões emocionais presentes no Twitter e suporte social on-line, salientando a capacidade de comunicação para uma ampla comunidade, tanto a partir de postagens originais, como de "repostagens" e de mensagens "favoritadas". ...
... The final codebook was then applied to a random selection of 10% of the tweets from the study sample (n = 136), a proportion that is considered appropriate for establishing intercoder reliability (Lombard et al. 2002). The three coders read each tweet in its entirety and coded for the most dominant sentiment conveyed in the tweet (Myrick et al. 2016). Tweets that did not relate to the commercial, the brand, or the COVID-19 pandemic were coded as "Other". ...
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Typically, consumer advertising is designed to promote or build brand identity for goods or services. Yet when a major crisis disrupts the everyday flow of life, advertisers often pivot from directly pitching their brands to conveying messages that somewhat reflect the tone of public service announcements. After examining the nature of much of the television advertising produced shortly after the United States was placed on lockdown following the announcement of the COVID-19 pandemic, this exploratory study investigates posts to Twitter to begin to address the question: To what extent did viewers’ interpretations of pandemic-themed commercials either accord with or challenge the advertisers’ intended messages of hope? The results show that targeted consumers demonstrated a greater tendency to contest advertisers’ inspirational themes than to passively accept them. These findings are discussed within the context of advertising’s ideological function as propaganda aimed toward especially active audiences in the age of social media.
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Patient advocates and activists are increasingly relying on online health information that can assist them to manage their health condition. Yet once online, they will confront diverse information whose veracity and utility are difficult to determine. This article offers a sociological analysis of the practical methods, or heuristics, that patient advocates and activists use when making judgements about the credibility and utility of online information. Drawing on the findings from interviews with fifty Australian patient advocates and activists, it is argued that these individuals' use of these heuristics reflects their hopes that information can help them manage their condition which may, in some cases, override fears and uncertainties that arise during searches. The article identifies the common ‘rules-of-thumb’—or what we call the ‘heuristics of hope’—that patient advocates/activists may use to make judgements and highlights the dangers of over-reliance on them, especially regarding clinically unproven, potentially unsafe treatments. Analyses of the heuristics of hope, we conclude, can assist in understanding the dynamics of decision-making and the role that affect plays in online patient communities which is crucial in an age characterised by the rapid circulation of emotionally charged messages, often based on hope.
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Hyperlinks are connective devices that allow users to direct each other in digital spaces while also demonstrating their own interests in specific types of content. Communication scholars have analyzed motivations for the use of social network sites (SNSs) at a broad level, opening up questions about the impetus for sharing hyperlinks in these spaces. In particular, scholars have focused on Twitter as an important platform for news and infor- mation sharing and community building, exploring a variety of motivations for its use. This study expands upon recent research by analyzing user motivations for posting hyper- links on Twitter. Through a survey of Twitter users, this study revealed a central social role for hyperlinks, indicating their use to seek information by soliciting reciprocal linking from other users. The findings provide new insights for researchers and practitioners into an increasingly important part of users’ engagement and information flows on Twitter. Broader implications for media scholars and practitioners are discussed.
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Background: Emotional support has traditionally been conceived as something a breast cancer patient receives. However, this framework may obscure a more complex process, facilitated by the emerging social media environment, which includes the effects of composing and sending messages to others. Accordingly, this study explores the effects of expression and reception of emotional support messages in online groups and the importance of bonding as a mediator influencing the coping strategies of breast cancer patients. Methods: Data were collected as part of two National Cancer Institute-funded randomized clinical trials. Eligible subjects were within 2 months of diagnosis of primary breast cancer or recurrence. Expression and reception of emotionally supportive messages were tracked and coded for 237 breast cancer patients. Analysis resulted from merging 1) computer-aided content analysis of discussion posts, 2) action log analysis of system use, and 3) longitudinal survey data. Results: As expected, perceived bonding was positively related to all four coping strategies (active coping: β = 0.251, P = .000; positive reframing: β = 0.288, P = .000; planning: β = 0.213, P = .006; humor: β = 0.159, P = .009). More importantly, expression (γ = 0.138, P = .027), but not reception (γ = -0.018, P = .741), of emotional support increases perceived bonding, which in turn mediates the effects on patients' positive coping strategies. Conclusions: There is increasing importance for scholars to distinguish the effects of expression from reception to understand the processes involved in producing psychosocial benefits. This study shows that emotional support is more than something cancer patients receive; it is part of an active, complex process that can be facilitated by social media.
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Is social media a valid indicator of political behavior? There is considerable debate about the validity of data extracted from social media for studying offline behavior. To address this issue, we show that there is a statistically significant association between tweets that mention a candidate for the U.S. House of Representatives and his or her subsequent electoral performance. We demonstrate this result with an analysis of 542,969 tweets mentioning candidates selected from a random sample of 3,570,054,618, as well as Federal Election Commission data from 795 competitive races in the 2010 and 2012 U.S. congressional elections. This finding persists even when controlling for incumbency, district partisanship, media coverage of the race, time, and demographic variables such as the district's racial and gender composition. Our findings show that reliable data about political behavior can be extracted from social media.
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This study suggests taking a social networks theoretical approach to predict and explain patterns of information exchange among Twitter prostate and breast cancer communities. The authors collected profiles and following relationship data about users who posted messages about either cancer over 1 composite week. Using social network analysis, the authors identified the main clusters of interconnected users and their most followed hubs (i.e., information sources sought). Findings suggest that users who populated the persistent-across-time core cancer communities created dense clusters, an indication of taking advantage of the technology to form relationships with one another in ways that traditional one-to-many communication technologies cannot support. The major information sources sought were very specific to the community health interest and were grassroots oriented (e.g., a blog about prostate cancer treatments). Accounts associated with health organizations and news media, despite their focus on health, did not play a role in these core health communities. Methodological and practical implications for researchers and health campaigners are discussed.
Book
Businesses, entrepreneurs, individuals, and government agencies alike are looking to social network analysis (SNA) tools for insight into trends, connections, and fluctuations in social media. Microsoft's NodeXL is a free, open-source SNA plug-in for use with Excel. It provides instant graphical representation of relationships of complex networked data. But it goes further than other SNA tools -- NodeXL was developed by a multidisciplinary team of experts that bring together information studies, computer science, sociology, human-computer interaction, and over 20 years of visual analytic theory and information visualization into a simple tool anyone can use. This makes NodeXL of interest not only to end-users but also to researchers and students studying visual and network analytics and their application in the real world. In Analyzing Social Media Networks with NodeXL, members of the NodeXL development team up to provide readers with a thorough and practical guide for using the tool while also explaining the development behind each feature. Blending the theoretical with the practical, this book applies specific SNA instructions directly to NodeXL, but the theory behind the implementation can be applied to any SNA. To learn more about Analyzing Social Media Networks and NodeXL, visit the companion site at www.mkp.com/nodexl Walks readers through using NodeXL while explaining the theory and development behind each step, providing takeaways that can apply any SNA Demonstrates how visual analytics research can be applied to SNA tools for the mass market Presents readers with case studies using NodeXL on popular networks like email, Facebook, Twitter, and wikis.
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Objective To compile and evaluate the evidence on the effects on health and social outcomes of computer based peer to peer communities and electronic self support groups, used by people to discuss health related issues remotely. Design and data sources Analysis of studies identified from Medline, Embase, CINAHL, PsycINFO, Evidence Based Medicine Reviews, Electronics and Communications Abstracts, Computer and Information Systems Abstracts, ERIC, LISA, ProQuest Digital Dissertations, Web of Science. Selection of studies We searched for before and after studies, interrupted time series, cohort studies, or studies with control groups; evaluating health or social outcomes of virtual peer to peer communities, either as stand alone interventions or in the context of more complex systems with peer to peer components. Main outcome measures Peer to peer interventions and co-interventions studied, general characteristics of studies, outcome measures used, and study results. Results 45 publications describing 38 distinct studies met our inclusion criteria: 20 randomised trials, three meta-analyses of n of 1 trials, three non-randomised controlled trials, one cohort study, and 11 before and after studies. Only six of these evaluated “pure” peer to peer communities, and one had a factorial design with a “peer to peer only” arm, whereas 31 studies evaluated complex interventions, which often included psychoeducational programmes or one to one communication with healthcare professionals, making it impossible to attribute intervention effects to the peer to peer community component. The outcomes measured most often were depression and social support measures; most studies did not show an effect. We found no evidence to support concerns over virtual communities harming people. Conclusions No robust evidence exists of consumer led peer to peer communities, partly because most peer to peer communities have been evaluated only in conjunction with more complex interventions or involvement with health professionals. Given the abundance of unmoderated peer to peer groups on the internet, research is required to evaluate under which conditions and for whom electronic support groups are effective and how effectiveness in delivering social support electronically can be maximised.
Conference Paper
Twitter, the most famous micro-blogging service and online social network, collects millions of tweets every day. Due to the length limitation, users usually need to explore other ways to enrich the content of their tweets. Some studies have provided findings to suggest that users can benefit from added hyperlinks in tweets. In this paper, we focus on the hyperlinks in Twitter and propose a new application, called hyperlink recommendation in Twitter. We expect that the recommended hyperlinks can be used to enrich the information of user tweets. A three-way tensor is used to model the user-tweet-hyperlink collaborative relations. Two tensor-based clustering approaches, tensor decomposition-based clustering (TDC) and tensor approximation-based clustering (TAC) are developed to group the users, tweets and hyperlinks with similar interests, or similar contexts. Recommendation is then made based on the reconstructed tensor using cluster information. The evaluation results in terms of Mean Absolute Error (MAE) shows the advantages of both the TDC and TAC approaches over a baseline recommendation approach, i.e., memory-based collaborative filtering. Comparatively, the TAC approach achieves better performance than the TDC approach.
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This article illustrates the structural changes in hyperlink networks from Web 1.0 to Web 2.0 and describes Web 1.0 using hyperlink data obtained from websites of South Korean National Assembly members between 2000 and 2001. The websites were sparsely knitted and formed a hub-spike network. Hyperlinks were created to enhance the interface and navigation ability of websites. The article also examines how hyperlink patterns began to change in 2005 and 2006 when Web 2.0 (blogs) was introduced. A key difference between Web 1.0 and Web 2.0 was that the Assembly members were relatively well connected in the blogosphere. Furthermore, prominent Web 1.0 hubs with many links tended to disappear, but butterfly networks based on political homophily emerged. Finally, the hyperlink network of Twitter, a recent Web 2.0 application, is examined. Twitter’s network diagram shows that online social ties between politicians are becoming denser.