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Health Communication
ISSN: 1041-0236 (Print) 1532-7027 (Online) Journal homepage: http://www.tandfonline.com/loi/hhth20
#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 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.
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-
cer”hashtag 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 others’content (i.e.,
retweets), or to favorite specific Twitter messages so that
these messages are broadcast across a user’sentirenetwork
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 individual’s
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 support—emotional sup-
port—also has a strong connection to the cancer experience.
Emotional support is the act of acknowledging or validating
another person’s 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 space—open 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 user’s 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 defined—we 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, Nabi’s(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
user’s emotional state. Nabi’s 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 Cancer’s 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).
4J. G. MYRICK ET AL.
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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 organization’s 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 NodeXL’sTwitter Search importer
(Hansen, Shneiderman, & Smith, 2011), which was set to
identify the most recent 1,000 Twitter users who included
the hashtag “#stupidcancer”in 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 “#stupidcancer”hashtag. 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
Krippendorf’s 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 you’re 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., “I’m grateful for the nurse’shelp
today”) also served as a form of social support because it
can help users recognize their own gratitude (ICR = .90).
Religious support (e.g., “I’m praying for you”)wasanother
form of social support (ICR = .94). Empathetic messages
(e.g., “Ifeelyourpain,”or “I’m 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 11–23 200
70 7–10 200
60 5–6 200
40–50 4 400
10–30 3 600
0≤2 200
HEALTH COMMUNICATION 5
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about cancer as well as the use of phrases like “lol”or “haha.”
Expressions of anger (ICR = .88) included messages stating
frustration (e.g., “I hate cancer,”“f.u. cancer,”“I’m 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 = 1–533, 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. Mann–Whitney 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 hoc”publics—spaces 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
are—a 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 individuals—of 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 Utah’s Networked Health Lab for their assistance with
coding. We also thank the peer reviewers for their thoughtful comments.
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