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


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Study of the structural implications of micronetworks (i.e. Twitter) on user health

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

  1. 1. UNIVERSITY OF OXFORD Where are my friends? The effects of real and imagined online communities on user wellbeing and happiness St. Cross College Oxford Internet Institute, August 2011
  2. 2. Abstract Everyday social relationships are one of the most well documented factors influencing physical health, wellbeing, and happiness. Benefits include decreased mortality, lowered levels of distress, increased feelings of security and self worth, an increased sense of belonging, a source of regulation for maintaining healthy behaviors, as well as enhanced social participation and commitment to community. Over one billion people are a member of at least one online social network, which are designed to forge and maintain social ties. I set out to explore how online interactions impact the health and wellbeing of average users. This research puts forth a new theory, known as the theory of perceived companionship, which postulates that online environments present unique affordances that facilitate the development of companionate relationships known to improve health. Hypotheses were tested through an online survey of 61 Twitter users, which evaluated network structure, expectations for interactions, and explicit reactions following online correspondences along a number of metrics known to boost well-being. This data was analyzed to better understand the associations between community membership, direct interactions, and subsequent user reactions. By identifying instances known to increase happiness, researchers and developers may incorporate the results to inform the creation of digital tools that consider the psychological impact of online use and advance its known benefits.
  3. 3. Acknowledgements I wish to thank my advisor, Dr. Eric Meyer, for his unwavering support and guidance. I am also grateful to Dr. John Powell, Fadhila Mazanderai, Dr. Louise Locock and everyone at the Health Experiences Research Group for taking interest in my project and providing expert advice. To Dr. Theodore Zeldin: Thank you for emphasizing the importance of human connectedness and the specialness of human-to-human interactions. These ideas served as a primary inspiration for my research and I hope that my results stimulate the development of digital tools that connect individuals in a meaningful way. Thank you to my colleagues for disseminating the online survey, and to Dr. Kevin Wright and Dr. Kathleen Griffiths, who were gracious enough to provide research materials and recommendations. "The happiest people are those who think the most interesting thoughts.” – William Lyon Phelps
  4. 4. Table of Contents Chapter 1 Introduction p. 1 1.1 The Internet, social support, and positive health outcomes p. 2 1.2 Research questions p. 4 Chapter 2 Literature p. 7 2.1 Social support p. 7 2.2 Companionship p. 9 2.3 Perceived social support, companionship, and social capital p. 11 2.4 The Internet’s ability to foster perceived social support and companionship p. 13 2.5 Fostering perceived social support online: design challenges for digital tools p. 14 2.6 Summary p. 15 Chapter 3 Method p. 17 3.1 Theoretical framework p. 17 3.2 Methodological approach p. 18 3.3 Twitter survey p. 19 3.3.1 Background p. 19 Chapter 4 Results p. 25 Chapter 5 Discussion p. 50 Appendix p. 55 References p. 67
  5. 5. 1 Chapter One Introduction In 2010, The Lancet published a study (Lester et al., 2010) that examined whether mobile phone communication between health care providers and HIV patients in Kenya would improve adherence to drug regimens and result in suppressed viral loads. 581 participants were randomized to either an experimental or control group. Every Monday morning, the intervention group received a text message from a health worker that simply asked, “How are you?” Patients were instructed to reply that they were either doing well, “Sawa” in Kiswahili, or “Shida,” that they had a problem. If a subject responded with the latter, a nurse would contact that individual during clinical hours. Out of 11,983 responses to SMS inquiries, 7,812 stated that they were well while only 391 reported a problem. Although the majority of subjects did not interact with a health worker except to convey that they were doing well, the intervention group displayed higher adherence to the drug regimen and increased viral suppression compared with the control, which received traditional health services. When subjects were asked what they thought about the program, many expressed that they felt “like someone cares.” Even though the patients did not know the health provider who had reached out to them, these short, impersonal messages fostered a clear sense of comfort, resulting in positive health outcomes. This thesis explores how
  6. 6. 2 supportive mechanisms function in mediated interpersonal communication1 , with a specific focus on the Internet, to benefit both those who may be ill as well as individuals who are healthy. 1.1 The Internet, social support, and positive health outcomes The link between social ties and well-being is firmly established (Kawarchi and Berkman, 2001). Social support is one of the most well documented psychosocial factors influencing physical and mental health (McCormick, 1999; Rook, 1987). Benefits include decreased mortality, lowered levels of distress, increased feelings of security and self worth, a heightened sense of belonging, a source of regulation for maintaining healthy behaviours, as well as enhanced social participation and commitment to community (Shklovski, Kraut, and Cummings, 2006). However, the precise definition of social support is unclear. Overall, it is a type of relationship transaction between individuals (Zimet et al., 1988). Shumaker and Brownwell characterized social support as the interchange of resources between actors that results in enhanced well-being for one or more of the individuals (Zimet et al., 1988). Although the intended outcome is apparent, the kinds of resources enacted to achieve this end are unspecified. Furthermore, research has demonstrated that the provision of tangible resources may not directly lead to stated health benefits. Rather, positive health outcomes are the result of how the activity is perceived and interpreted by an individual, a concept known as perceived social support. (Shklovski, Kraut, and Cummings, 2007.) 1 Mediated communication is defined as a process by which a message is transmitted via some intermediary, such as a networked computer or mobile phone (Pavlik and McIntosh, 2003).
  7. 7. 3 The Internet has been lauded for its ability to foster feelings of perceived social support across a multitude of online environments (Caplan and Turner, 2007; Wright 2000; Morahan-Martin and Schumacher 2003; White and Dorman 2001; Barrera et al., 2002; Yoder and Stutzman, 2011). For instance, a recent study by the Pew Research Center (Hampton, Goulet, Rainie, Purcell, 2011) concluded that Facebook users possess higher levels of perceived social support than the average American. To quantify this outcome, an individual who uses Facebook multiple times per day accrues about half of the boost in total support as someone gains from being married or living with a partner. As the Internet becomes increasingly embedded in our lives, it is imperative that software developers and researchers establish a detailed understanding of how online use affects health in order to strategically advance its known benefits. The broad goal of this study is to inform theories regarding how the Internet can be employed to improve and maintain health through cultivating feelings of support among users. The World Health Organization defines health as “a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity (2011). My primary area of interest is mental health, which is known to impact physical health, and defined as a “state of well-being in which every individual realizes his or her own potential, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to her or his community.” (World Health Organization, 2011). As such, mental health refers to an optimum level of well-being rather than a psychological impairment. The implications of this study are directed at those who are traditionally considered healthy, or without any identifiable illness. The research explores ways in which the Internet may be used to elevate feelings that promote well-being, such as connectedness, self-esteem and self-worth, among
  8. 8. 4 others (Cheng and Furnham 2002; Wright 2000). Findings may be appropriated to inform the creation of digital tools that consider the psychological impact of mediated interpersonal engagement and aim to enhance the lives of objectively healthy individuals. 1.2 Research Questions This study seeks to identify the specific mechanisms that foster perceived social support online. Through evaluating literature from the areas of eHealth, psychology, and social dynamics of the Internet, I developed a distinct underlying mechanism called perceived companionship that, when enacted in the prescribed context, has the potential to improve user well-being through online interactions. I tested this theoretical framework through an online survey of Twitter users. Twitter is an online social networking service that allows subscribers to send and read messages called tweets (Twitter, 2011). Tweets are text-based posts of up to 140 characters that are published on a user’s profile page in the sequential order in which they were received and are publicly visible by default. Twitter users can subscribe to receive other users’ tweets, a process known as “following,” while those who follow are “followees.” Subscribers are called “followers” and can reply to tweets posted by those they follow. In addition, they can re-post messages published by other users on their own Twitter account, which publicly assigns credit to the originator of the message. This action is known as a “retweet.” Twitter was created in 2006 and currently has over 200 millions users who generate more than 350 million tweets per day (Shonfeld, 2011). The Twitter survey was designed to test the following hypotheses: H1: A reciprocal online interaction has the potential to improve user well- being
  9. 9. 5 H2: A potentially reciprocal online interaction has the ability to improve user well-being A reciprocal interaction is defined as one in which, for any action undertaken by an individual, there is the opportunity for others to do the same in return (Byman, Jarvela, and Hakkinen, 2005). I selected Twitter to test these hypotheses because any action by a Twitter user has the potential to be reciprocated by another individual. Specifically, reciprocal interactions were delimited as those where the subject tweets and another user acknowledges that subjects’ contribution by electing to follow that user, directly reply to their post, or retweet.2 As previously stated, social support is the objective transaction of resources while perceived social support is an individual’s subjective notion that resources are conceivably available for use (Shklovski, Kraut, and Cummings, 2006.) In this study, the concept of perceived companionship extends the traditional theory of perceived social support. Furthermore, as with perceived social support, the beneficial outcomes associated with perceived companionship remain contingent on an explicit exchange between individuals, or the potential for an exchange to occur in the future. In H1, the term “potential” is synonymous with capacity; a reciprocal online interaction has the capacity to improve user well-being. For H2, potential refers to an exchange that may occur in the future and is aligned with the notion of perception as it relates to perceived social support. In other words, the mere knowledge that a reciprocal online interaction could develop is predicted to improve user well-being. H2 focuses on individuals who have previously engaged in a reciprocal online exchange and accrued subsequent positive affect, consisting 2 For additional information, see Appendix: Section 1
  10. 10. 6 of positive emotions such as happiness and optimism. Consequently, these users would develop an expectation that if a reciprocal connection were to occur, they would experience a boost in well-being. This anticipation is consistent with the notion of perceived social support, whereby health benefits are derived from the understanding that support could and would be enacted if the user so desired, rather than through the direct transaction of supportive resources. Figure 1.1 The role of positive reinforcement in fostering perceived social support on Twitter Survey items were designed to measure (1) how users perceive their Twitter community (Who are the individuals that comprise a subject’s Twitter network and what is their relationship to the subject?), (2) expectations and hopes for interactions on Twitter, and (3) general and specific reactions to, and perceptions of, Twitter interactions (including but not limited to gaining a follower, receiving a reply, and having a post retweeted.) In order to quantify the adequacy of boosts in well-being and directly test the hypotheses, actions were deemed particularly notable if over 50% of subjects exhibited the measured effect. However, responses were also discussed from a qualitative perspective in order to more deeply explore the implications of reported data. Subject(tweets( Another(user( acknowledges( tweet( Subject( experiences(a( boost(in(well8 being( Cultivates feelings of perceived support
  11. 11. 7 Chapter 2 Literature The literature explores social support, perceived social support, and their associated health outcomes. In addition, it discusses the role of companionship as a related mechanism that, along with perceived social support, is part of a single global construct known to improve well-being (Newcomb,1990). Lastly, the review identifies the Internet’s specific affordances that make it an advantageous environment for fostering perceived social support and companionship among users (Walther, 2007). This study adheres to the assertion that social support, companionship, and other descriptions of human connectedness, including loneliness3 , all reflect certain shared processes (Newcomb, 1990.) Although these mechanisms have primarily been studied independently, they are part of a high-order construct of general social attachment (Newcomb, 1990). Correspondingly, metrics used to evaluate companionship and social support must address a more basic need for individuals to connect with a personal social network (Newcomb and Bentler, 1986). This study considers social support, companionship, and related mechanisms associated with well-being from a holistic perspective, as different sides of the same concept. 2.1 Perceived Social Support There is not a singular theory that wholly encapsulates researchers’ understanding of social support (Lewkowicz, et al., 2008). Although most agree 3 For additional information, see Appendix: Section 2
  12. 12. 8 that it involves some kind of relationship transaction between individuals, the nature of the transaction has been particularized in a variety of ways (Zimet et al.,1988). For example, Barnes and Duck assert that social support is an exchange of verbal and nonverbal messages that transmit emotion or information in order to reduce ambiguity or stress (1994). Others delineate the theory by the type of support provided, such as emotional support (comfort, friendship, love), informational support, esteem support, tangible aid (instrumental or material help), or social network support (McCormick, 1999). More generally, it may be the feeling of being loved, cared for, or esteemed by others. Evidently these definitions range in specificity and incorporate a number of characteristics that are known to enhance well-being. Consequently, Vaux (1988) concluded that any single definition of social support will prove inadequate in encompassing its many facets. Health Benefits: Despite these varied definitions, the positive health benefits associated with perceived social support are incontrovertible. Support is known to positively influence both physical and mental health through, for example, decreased morbidity and mortality and improved psychological well-being (Zimet et al., 1988). Importance of individual perception: According to Heller et al (1986), the objective support activity is not as important as how it is interpreted, which directly influences the outcome and overall satisfaction with the support provided. To underscore the value of perception, Hawkley and Cacioppo state (2010, p. 224), “Humans are such meaning-making creatures that we perceive social relationships where no objectifiable relationship exists (e.g., between author and reader, between an individual and God) or where no reciprocity is
  13. 13. 9 possible (e.g., in parasocial relationships with television characters). Conversely, we perceive social isolation when social opportunities and relationships do exist but we lack the capacity to harness the power of social connectedness in everyday life.” Because the definition of social support is nebulous and highly dependent on an individual’s subjective interpretation of an exchange or relationship, a “systematic exploration into the perceived attraction of online social support is especially warranted.” (Walther and Boyd, 2002; pp. 153) 2.2. Companionship Companionship has been characterized as part of the inclusive concept of perceived social support and, alternatively, as a distinct independent construct (Diener, 1994). Notwithstanding the lack of consensus around its definition, social support is consistently contextualized as a mechanism employed to alleviate personal problems and emotional distress (Caplan and Turner, 2007). Appropriately, much of the related research focuses on individuals who are in obvious need of support, such as the ill, the elderly, and those experiencing major life upheaval (Wright, 2000). Companionship, on the other hand, is motivated by the wish to experience rewards, such as positive affect and stimulation, through interpersonal exchange (Thoits, 1982). In addition, it is the primary way in which people bring pleasure and excitement into their lives (Rook, 1987). Rook (1987) posits that companionship is a relationship formed around shared leisure activities that are undertaken for the intrinsic goal of enjoyment. Activities are described as those that provide benefits, such as humor, recreation, and affection, and occur on a continuous basis as an integral part of our lives (Rook, 1987).
  14. 14. 10 Based on this analysis, the primary differences between social support and companionship are the intent behind the interactions, namely to ameliorate a problem versus enjoyment, and the ways in which they improve well-being (Rook, 1987; Wright 2000). To illustrate this concept, if well-being is conceptualized as a spectrum with zero as an individual’s equilibrium level of contentment, social support is employed to bring people from a negative level of well-being to equilibrium, or their usual level of contentment. Conversely, companionship boosts an individual beyond their balanced level, resulting in relatively higher levels of positive affect (Thoits, 1982). Figure 2.1. The role of social support and companionship; conceptualizing well-being as a spectrum Health Benefits: Companionate interactions produce many of the same health benefits as those accrued through social support in times of distress (Wright, 2000), including reduced stress, lower levels of morbidity and mortality, and improved psychological wellbeing (Wright, 2000). Despite the fundamental difference between the two concepts, the analogous health effects produced by these mechanisms are consistent with the idea that
  15. 15. 11 social support and companionship are part of the same construct (Newcomb, 1990). According to Wright (2000), it would be difficult to find a solely supportive or a solely companionate exchange. Some researchers consider companion relationships to be more beneficial than those deemed socially supportive because they are proactive rather than reactive. In this way, actors must have mutual respect for one another since they elect to foster a connection for the sake of being in touch, rather than to elicit a specific kind of support, such as tangible or emotional aid, and fill an unmet need (Wright, 2000). Companionship was found to be the strongest predictor of relationship satisfaction and loneliness. It may also function as a maintenance mechanism; although social support might restore self-esteem on a specific occasion, companionship could preserve these feelings for an extended duration. Overall, companionship fosters positive affect, which contributes to greater resilience and optimism, thus shaping how individuals perceive the world (Cohen and Pressman, 2005). Low positive affect and a lack of pleasurable activities have been implicated in the inception and maintenance of some psychological disorders, such as depression (Rook, 2011). In accordance with perceived social support, the health sustaining value of companionship may emanate from the perception that these connections exist, which is separate from the direct benefits derived from interpersonal interactions (McCormick, 1999). 2.3 Perceived social support, companionship, and social capital Social capital includes the notions of perceived social support and companionship and is defined as the actual or virtual resources amassed through relationships between people (Steinfeld, Ellison, and Lampe, 2008), Social capital results in a number of personal advantages, including improved health. For example, access to individuals outside of one’s close social circle provides exposure to non-redundant
  16. 16. 12 information (MacKenzie and Harpham, 2006), which is directly related to measures of psychological well-being such as self esteem and satisfaction with life (Steinfeld, Ellison, and Lampe, 2008). This connection to a wider community is the result of weak ties, or contacts that convey useful information or new perspectives but do not provide emotional support (Granovetter, 1973). Weak ties foster a specific type of social capital known as bridging social capital and members of weak tie networks are thought to be outward looking since their social networks include people from a range of backgrounds (Steinfeld, Ellison, and Lampe, 2008). The Internet has been shown to cultivate bridging social capital because it allows users to maintain relatively large networks from which they can draw resources. 2.4 The Internet’s ability to foster perceived social support and companionship As previously stated, perceived social support and companionship are based on subjective interpretations of a relationship. Online, the layout of websites can be manipulated to alter user perception (Lewkowicz et al., 2008). For example, everything from the placement of images, to the color, shape and typography can affect users’ psychological responses to these features (Lee et al., 2004). Because of the malleability of online environments and their direct effect on subjective interpretation, I assert that the Internet is a favorable domain for fostering perceived responses, such as social support and companionship. For that reason, websites may be specifically designed to cultivate positive feelings, thus leading to related health benefits. I applied Walther’s hyperpersonal communication model to address why online spaces are advantageous for producing perceived responses (Walther, 2007). The Internet affords users a distinct environment that cannot be duplicated in the
  17. 17. 13 face-to-face context through attributes such as distance, anonymity, interaction management, and access. Dynamics between the sender, receiver, channel, and feedback systems are directly impacted by attributes associated with computer- mediated communication. For example, online contact can promote the development of relationships and result in exaggerated impressions of conversation partners. Specifically, interlocutors will note message elements that indicate minimum levels of similarity or desirability and idealize partners based on these limited cues (Walther, 2007). In both face-to-face and online dialogues, actors are motivated to mitigate ambiguity, create impressions, and develop positive views of one another (Walther and Boyd, 2002). However, online interactions may be exceptionally information poor and subjects might lack reliable verbal and physical cues to formulate these conceptions. As a result, users will assess the limited signals available and extrapolate to fill any information deficits in order to construct a coherent, subjective perception of others and enhance relational outcomes. Perception development may also be particularly straightforward online because relationships are often less complex than in the offline environment (Walther and Boyd, 2002). For example, in an online support group, the only feature uniting interactants may be their source of personal discomfort. In this way, the connection is uniplex rather than multiplex; this simplicity, combined with a lack of cues and desire to develop favorable views of one another, may enable users to readily form positive perceptions of conversation partners. Consequently, individuals might be more likely to perceive online dialogues as supportive or companionate than those with added contextual cues. Numerous studies have explored the effect of Internet use on perceived social support and companionship (Wright, 2000; Barrera et al., 2002; Drentea and
  18. 18. 14 Moren-Cross, 2005). Steinfield, Ellison, and Lampe (2008) tested the impact of online social network use on social capital and self-esteem. The study concluded that online activity is positively associated with a person’s sense of self worth and measures of psychosocial development. Despite these positive findings, conflicting evidence has indicated that Internet use may increase social isolation and inhibit the development of meaningful relationships in real life (McCormick, 1999). 2.5 Fostering perceived social support online: design challenges for digital tools The design and evaluation of health technologies are interdisciplinary processes (Ahern, Patrick, Phalen, and Neiley, 2006). Scientific, policy, and commercial communities play an integral role in various phases of development and each stakeholder contributes a different perspective to the planning, understanding, and evaluating of new health tools. Academia and industry have each worked to develop effective digital products that improve health. However, software development and health services research, comprised of industry and academia respectively, are distinct fields; software developers are traditionally involved with creating tangible digital tools, while researchers focus on evaluating interventions. However, as technology has advanced, these roles have become increasingly heterogeneous and less strictly defined. As a result, academics and developers have taken on many of the same responsibilities. For example, researchers have employed methodologies that require constant collaboration with key stakeholders in order to actively promote health and implement incremental improvements to suit the evolving needs of a community (Pagliari, 2007). Analogously, software developers may continually evaluate user needs through a similarly iterative process in order to create health applications.
  19. 19. 15 Despite these overlapping methodologies, developers and researchers have tended to work in parallel rather than as a cohesive unit. Consequently, the field of health technology has been criticized for its lack of user involvement in the design of eHealth applications, dearth of evidence demonstrating impact, and difficulties in bringing new technology to adoption. Because of this disconnect between fields, it is difficult to translate research into practice. While technology holds enormous potential to improve well-being, creating effective products requires joint thinking between the two groups to ensure that they are high quality and user-directed. My research aims to deconstruct the concept of perceived social support online in order to create a coherent theory of practice. By examining the present state of research and testing a new theory through an online survey, I intend to develop a normative standard that can be easily appropriated by both researchers and software developers in order to inform the creation of efficacious digital tools that enhance health and well-being. 2.6 Summary This study is about social support, tested through Twitter. Previous literature has evaluated Twitter as a communication tool (Pear Analytics, 2009), such as in crisis situations (Huberman and Romero, 2009), as an alternative news source (Kwak, Lee, Park, and Moon, 2010), a backchannel communication tool (Nely, 2009), and for its ability to track real-time trends (Sakaki, Okazaki, Matsuo, 2010). Twitter has only recently been explored as a potential means for increasing happiness (Goncalves, Ruan, and Mao, 2011; Dodds, et al., 2011). Existing evidence focuses on how mood states flow through a network. In addition, Dodds et al. (2011) correlated 50 million messages with nation-wide happiness polls in order to ascertain user reactions to tweets. My study will extend this burgeoning
  20. 20. 16 field of research, comprised of just a handful of studies, by identifying specific interactions that elicit positive emotions through self-reported responses. This research reframes how perceived social support is conceptualized and operationalized in the online space and assesses this idea in a largely unexamined environment. The resulting conclusions represent a progression in the field of Internet psychology and exposes opportunities for a future stream of research dedicated to more deeply understanding how and why we interact online, specifically in information-poor environments, and the ways in which this knowledge can be employed to advance health and happiness.
  21. 21. 17 Chapter 3 Method 3.1 Theoretical framework: perceived companionship As defined in this study, the notion of perceived companionship is unique to interactions conducted through mediated communication. In the offline environment, companions may be synonymous with friends or leisure activity partners (McCormick, 1999). Correspondingly, perceived companions also engage, or have the potential to engage, in direct interactions. However, the traditional notion of a friend is missing from the exchange. Applying Rook’s definition of companionship (1987), rather than participating in shared activities, a user may connect with a perceived companion through a one-off interaction, or a series of short, information poor interactions. I posit that these correspondences may result in the same health benefits associated with traditional companionate relationships. In this way, the companionship aspect of perceived companionship refers primarily to the purpose or intent of the interaction, namely a leisure activity undertaken for the sake of enjoyment, and the resultant positive outcomes. The perceived component of the mediated interaction is twofold: (1) Like perceived social support, it can be the potential for future interactions or the perception that social support, in this case companionship, is available. (2) However, perception also refers to the essence of the exchange. In order for an individual to accrue benefits through interactions on Twitter, such as boosts in self-esteem or self-worth, the recipient must have some baseline level of respect
  22. 22. 18 for the other actor involved (Rook, August, Sorkin, 2009). For example, if a user retweets my post but I know that the post was never read, I might not experience a boost in well-being; the health benefit emanates from the fact that I believe this actor noticed and interpreted my tweet, and felt it was significant enough to retweet and share with their followers. This process is largely perceived in the mind of the user. Twitter is an information poor environment; communications and profiles consist of 140 characters or less, which makes it difficult for users to possess a deep understanding of others, (unless they obtain additional information through outside sources), and fairly impossible to share detailed messages through tweets. Regardless of whether the interaction is with a friend, stranger, or otherwise, the hypothesized health benefits resulting from interactions with, and perceptions of, one’s Twitter network are based off of a cognitive process whereby the subject believes that another user has deciphered the published information and deemed it relevant, which compels them to initiate action directed at the subject. This series is facilitated by the nature of mediated contexts; computer mediated communication leads to more extreme impressions than in the face-to-face environment and more positive relations over time (Walther, 2007). Consequently, the subject may be more inclined to perceive activities as supportive or companionate even if they were not intended as such. 3.2 Methodological approach According to Lewkowicz et al. (2008), there is a lack of conceptual understanding of social support and a poor level of innovation when designing computer-based programs to cultivate feelings of perceived social support online. Because software developers and researchers are members of distinct communities (Pagliari, 2007), I believe that to bridge this gap, it is most beneficial to understand the
  23. 23. 19 underlying mechanisms that manifest themselves in online interventions and are known to improve health, such as perceived social support and companionship, so that this information may be easily appropriated by all relevant stakeholders to develop digital tools that effectively enhance well-being.4 As such, this represents an innovative approach to eHealth research. 3.3. Twitter survey 3.3.1 Background I selected Twitter as a platform to test the theory of perceived companionship and the notion that a (potentially) reciprocal interaction has the capacity to improve well-being. Lewkowicz et al. (2008) found that there must be an opportunity for a reciprocal interaction to occur in order for a relationship to feel and/or be supportive. Furthermore, individuals must be aware that they are relation to one another for a social or exchange opportunity to exist (Lewkowicz et al., 2008). On Twitter, members of the same network have the ability to engage in these types of reciprocal exchanges with their contacts. In addition, the process of following and gaining followers produces a built-in community where the direct connection between the subject and his or her contacts is evident to the user and the Twitter network. In addition, the Twitter feed, which contains updated tweets from followees, continually reminds users which Twitter subscribers are a part of their personal network. 4 For a systematic review of related literature, see Appendix: Section 3
  24. 24. 20 Figure 3.1. Example of a Twitter feed Survey construction and implementation The online survey was hosted on Survey Gizmo ( for a 2- week period in July 2011 and consisted of 42 items5 . 61 subjects were recruited through requests for participation on Facebook and Twitter. Key variables The survey assessed specific feelings using a 5-item Likert scale.6 5 For a full list of survey items, see Appendix: Section 4 Continual updates from followees in subject’s Twitter network
  25. 25. 21 H1: An online interaction has the potential to improve well-being Independent variables: Survey items assessed baseline demographic characteristics, including educational background, gender, as well as country of residence and origin. In order to gain an understanding of individuals’ Twitter use and the structural configurations of their personal networks, questions measured number of followers and followees, time since account activation, how often the user tweets and “checks” their Twitter account, (defined as reviewing the Twitter feed but not directly interacting with other users), and the percent of Twitter interactions that occur with individuals who the subject considers to be a friend and/or someone they interact with via other modes of communication. Dependent variables: Dependent variables were selected to provide a qualitative understanding of: (1) User perception of their Twitter community (What is the relationship between the subject and contacts who comprise their personal Twitter network?): Part 1 assesses the objective and perceived structure of, and relationship to, one’s Twitter network. (2) Expectations and hopes for interactions on Twitter: Part 2 identifies the type of support provided by the network. Items were designed to assess user reactions to specific Twitter interactions and the Twitter community more generally, with metrics adapted from surveys included in traditional support and companionship literature. These questionnaires are widely used in related research and include the Social Support Questionnaire (Sarason, Sarason, Shearin & Pierce 1987), 6 Survey items were both positively keyed (“If I want to have lunch with someone, I can easily find someone to join me”) and negatively keyed (“I don’t often get invited to do things with others”)
  26. 26. 22 companionship satisfaction survey (Wright, 2000), UCLA Loneliness Scale (Russell, Peplau, and Cutrona, 2011), and network satisfaction scale (Wright, 2000). (3) Part 3 records user reactions to specific activities on Twitter as well as general feelings about their Twitter networks. User reactions were aggregated along five dimensions known to reflect well-being for three specific types of Twitter interactions, including gaining a follower, having a post retweeted, or receiving a reply. In addition, responses were ascertained for situations when a user contributes to the Twitter community through a tweet, but is not acknowledged by another user. According to Rook, August, and Sorkin, (2009) companionate interactions may be detrimental if an individual faces rejection or exclusion by others. The latter scenario is designed to measure if a lack of acknowledgement would negatively affect well-being. Key words were selected to assess perceived social support, companionship, and loneliness. Metrics included recognized and appreciated, related to the concept of self-worth and self esteem; connected, which is part of bridging social capital; loneliness or feeling embedded within a community; capable, reflecting feelings of empowerment and self-esteem; as well as annoyed, which is a stressor and known detriment to health (Thoits, 1982).
  27. 27. 23 Figure 3.2. Categorization of metrics: user reactions to Twitter interactions H2: A potentially reciprocal online interaction has the ability to improve user well-being In order to operationalize H2, I stratified the sample into Frequent and Less Frequent user groups and compared results across survey items. Frequent users were characterized as those who reportedly check their Twitter account once per day or more often, and tweet once per day or more often. Less Frequent users were defined as individuals who check their account at most once per week and tweet at most once per week. In addition, I included subjects who check their multiple times per day, but tweet once per week or less, in the Less Frequent user group; although these subjects check their account often, they likely engage in fewer reciprocal interactions because they rarely contribute directly to the community through tweeting. Like H1, I assessed (1) how users perceive their
  28. 28. 24 Twitter community, (2) expectations and hopes for interactions on Twitter, and (3) reactions to, and perceptions of, Twitter interactions.
  29. 29. 25 Chapter 4 Results This study was deployed as a pilot. Given the relatively small sample size (n=61), dependent categorical variables were compared using response modes in order to develop a qualitative portrayal of participants’ Twitter use. This information was evaluated in order to postulate the effects of subjects’ perception of, and engagement with, their Twitter community on observed results. Although control variables and demographics were ascertained, the sample was too small to statistically compare outcomes. Because a quantitative analysis could not be performed, these variables merely provided added depth to the cases. To simplify comparisons between response categories, 5-item Likert scale items were condensed into three classifications, including agree, neutral, and disagree. In order to compare the adequacy of boosts, a 50% threshold was applied to reported responses, whereby any category with a response rate of 50% or greater was deemed particularly notable. 4.1 Sample characteristics (independent variables) 61 participants were split evenly between males and females (51% and 49% respectively). Most were between the ages of 25 and 34 years (57%), followed by 35 to 54 years (25%), and 18 and 24 years (18%). The sample was highly educated (69% had obtained a postgraduate degree) while the majority of subjects resided in the UK (44%) and the US (35%), with the remainder located throughout the world. The survey specifically targeted individuals who utilize Twitter; 44% of subjects had established an account over 2 years ago, 26%
  30. 30. 26 between 1 and 2 years, 18% 6 months to one year ago, and the remaining 11% had active for less than 6 months. 36% of the sample tweeted multiple times per day, 21% once each day, 21% once each week, 8% once per month, and 13% rarely. 62% of users checked their accounts multiple times per day, 31% once each day or week, and 6% once per month, rarely, or never. Overall, most respondents tweeted often, although less frequently than they checked their accounts. The majority of subjects said they consider fewer than 25% of Twitter contacts with whom they interact with through the service to be friends (54.1%), while 25% of subjects stated that 26% to 50% of interactions take place with friends. Furthermore, 67% of respondents concluded that they communicate with fewer than 25% of the users they follow on Twitter through additional modes of contact (including email, Facebook, and face to face). Figure 4.1. Frequency of tweets and checking accounts 62% 15% 16% 2% 5% How often do you check your account? n = 61 Multiple times per day Once each day Once each week Once each month Rarely
  31. 31. 27 Figure 4.2. Characteristics of Twitter contacts 36% 22% 21% 8% 13% How often do you tweet? n = 61 Multiple times per day Once each day Once each week Once each month Rarely 3% 54%25% 5% 13% Out of the contacts you interact with most frequently on Twitter, what percent do you consider friends? n = 61 None Less than 25% 26%-50% 51%-75% 76%-100%
  32. 32. 28 I. User perception of their Twitter community 40% of respondents claimed that they do not interact on Twitter and only monitor their contacts’ activity. As previously stated, most interactions occur with individuals who are not considered friends and users connect with fewer than 25% of followers through additional modes of communication. In addition, about half of respondents considered their interactions to be with members of their professional network and 67% state that contacts are not part of their close personal community. Only 23% of subjects found Twitter interactions to be personal, 54% felt they were not personal, and 23% were neutral. Based on this data, the objective relationship between users and their Twitter contacts is unclear. However, it seems that the majority of contacts are not friends in the traditional sense, because most do not communicate through additional channels (such as email, Facebook, face to face, etc.), the interactions are perceived to be impersonal, and Twitter contacts are not considered part of the subjects’ close personal community. To capture participants’ latent perceptions surrounding the concept of friendship and closeness in a Twitter connection, I applied metrics that evaluate 1% 67% 20% 10% 2% Out of your followers, what percent do you communicate with via other modes of contact (email, face-to-face, Facebook, etc)? n = 61 None Less than 25% 26%-50% 51%-75% 76%-100%
  33. 33. 29 responsiveness of the community, individual expectations for the interactions, and general feelings toward the Twitter network. The abovementioned responses indicate that a subjects’ community is likely to be constructed of weak ties. However, 73.3% of respondents claimed that they share many interests with their Twitter contacts, while only 8.3% disagreed with this notion. Although the majority of the community might not be personally connected to the subject as a friend or through other modes of communication, the user perceives Twitter contacts to be highly similar to themselves. This is consistent with Walther’s hyperpersonal communication model, which states that online interlocutors will develop exaggerated positive perceptions of others based on cues that indicate minimum levels of similarity or desirability. (However, these similarities could be attributed to a connection with the professional network.) 65% of subjects agreed that if someone tweeted that they were having a problem, they would feel concern for that person (27% were neutral and 8% disagreed) and 47% said they would contact that user through Twitter (23.3% were neutral and 30% disagreed.) Only 40% of participants agreed that if they posed a problem on Twitter they were confident someone would respond (32% disagreed and 28.4% were neutral.) Although this data is not statistically significant, it demonstrates that Twitter networks are comprised of weak ties yet users feel concern for individuals within their Twitter community. In addition, contacts are not perceived to be responsive in addressing subjects’ problems through Twitter.
  34. 34. 30 Figure 4.3. User perception of Twitter community II. Expectations and hopes for Twitter interactions The majority of users reported that Twitter does not provide them with emotional support (70.1%) and if they are down in the dumps, they cannot count on interactions with their Twitter network to make them feel better (70.5%). This is consistent with the theory of companionship, which includes activities designed for leisure rather than the provision of emotional aid. In terms of expectations, only 13% of participants expect their tweets to be acknowledged by another user and 55% do not care if a tweet is recognized (20% neutral and 25% disagreed.) Additionally, most users reported neutral feelings (~60% to 70%) when asked how they would react if they tweeted and did not receive a response. Despite the lack of expectation or apparent care, 50% of subjects claimed that they hoped other users would acknowledge their tweets. This discrepancy might suggest that 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 100.00% Agree Neutral Disagree PercentofRespondents Structure and Perception of Twitter Community Professional network Close personal community Interactions are personal I would feel concern if someone tweeted a problem If someone posted problem I would contact them Share many interests with contacts
  35. 35. 31 although users do not expect their tweets to be acknowledged and do not necessarily care if they are recognized, they are still hopeful that someone will elect to reply, retweet, or follow their account. Furthermore, 75% of subjects were comforted by the fact that someone might acknowledge their contribution (3% disagree and 22% neutral.) If tweets are not acknowledged, it is unlikely that the user will experience any negative outcomes, evidenced by data demonstrating that most users felt neutral if they did not receive a response following a tweet and the majority do not care if a tweet is acknowledged. If a user tweets that they are having a problem, 40% believed that someone on Twitter would respond, while the remaining 59% were neutral or disagreed. Additionally, 41.6% stated that if they pose a question on Twitter, they are confident someone would respond (35% disagree.) All in all, Twitter users are not confident that someone will respond to their tweets but 75% of subjects are comforted by the fact that someone might acknowledge their contribution to the Twitter community. Although the specific impact of this comforting presence cannot be quantified through the Twitter survey, it is consistent with the notion of perceived companionship, whereby the potential for a companionate interaction increases well-being.
  36. 36. 32 Figure 4.4. Expectations and hopes for Twitter interactions III. Explicit reactions to, and perceptions of, Twitter interactions: Explicit reactions: The data illustrates that when a user gains a follower, is retweeted, or replied to, there is a boost in well-being that varies between the specified metrics. If a user is notified about gaining a follower, 62% felt recognized, 46% felt connected, 61% felt appreciated, 79% did not feel annoyed, 21% felt the same as before receiving the notification, and 24% felt capable. When someone replies to a subject’s tweet, 88% felt recognized, 88% felt connected, 77% felt appreciated, 80% did not feel annoyed, 8% felt the same as before the notification (44% were neutral and 30% disagreed), and 41% felt more capable (51% neutral.) If a post is retweeted, 81% felt recognized, 78% felt connected, 82% felt appreciated, 87% would not feel annoyed, 10% would feel the same (50% neutral), and 46% felt more capable (47% neutral). If a tweet is not acknowledged, the majority of participants were neutral when asked if they felt 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% Agree Neutral Disagree PercentofRespondents Expectations and Hopes for Twitter Interactions Comforting that someone might acknowledge my contribution Hope for acknowledgement from another user If I am having a problem, someone will respond on Twitter Do not care if tweet is acknowledged Pose a question, confident someone will respond
  37. 37. 33 recognized, capable, connected, or appreciated (~60% to ~70%). However, 40% of subjects agreed that they would feel annoyed (40% neutral and 20% disagreed). Overall, these statistics imply that users’ feelings are affected by notifications about gaining a follower, being retweeted and receiving a reply along the dimensions known to improve well-being. However, user well-being is relatively unharmed if a response is not garnered. In terms of explicit reactions, feeling recognized, appreciated, and connected garnered the strongest positive responses, as well as the most polarized. Based on comparisons to frequencies of Neutral and Disagree, replying to a tweet resulted in the most pronounced objective increase along these dimensions (with 54, 54, and 47 users in agreement respectively) and retweeting followed with 50, 42, and 50 subjects in support of feeling recognized, connected, and appreciated. Furthermore, the responses for neutral were relatively low and few or zero users disagreed with these assertions. Gaining a follower yields increases in feeling recognized, connected, and appreciated, although they are not as high as for the replying and retweet categories. In addition, most subjects were indifferent (neutral) or disagreed with the metrics, stating that they would feel the same as before receiving the notification across all three categories. This is in direct support of H1, which posits that reciprocal online interactions have the potential to improve well-being. For retweeting, about half of users felt capable while the other half were neutral, while replying yielded 42% as more capable and 42% neutral. Overall, the majority of subjects did not find notifications to be annoying, or submitted a neutral response. In order to better understand this data, I compared ratios of the frequency of users who agreed and those who were neutral along the specified dimensions. (I did not include those who disagreed because there were relatively few responses
  38. 38. 34 for this category.) For feeling recognized, replies garnered the highest agree/neutral ratio (7.714), followed by retweets (4.55) and follows (3.31). Replies increased feelings of connectedness by the largest amount, followed by retweets and follows. Retweets made users feel most appreciated, followed closely by replies and then follows. The majority of users reported feeling neutral about a heightened sense of capability, but retweets provided a ratio of 1, .8 for replies and .44 for gaining a follower. Overall, following resulted in the highest feelings of recognition, replying resulted in highest connectedness and recognition, and retweets resulted in highest levels of appreciation and recognition.
  39. 39. 35 4.5. Explicit reactions to Twitter interactions 0 20 40 60 80 100 Agree Neutral Disagree PercentofRespondents Gains a Follower Recognized Connected Appreciated Capable 0 20 40 60 80 100 Agree Neutral Disagree PercentofRespondents Receives Reply Recognized Connected Appreciated Capable 0 20 40 60 80 100 Agree Neutral Disagree PercentofRespondents Retweeted Recognized Connected Appreciated Capable
  40. 40. 36 General perception of Twitter interactions: Users reported higher feelings of connectedness through engaging with a Twitter network (61.7% agreed), it is considered an outlet to express ideas (68.3% agreed), and interactions are viewed as a source of stimulation (68%) and rewarding (67%). Although stimulation, connectedness, and feeling rewarded are associated with well-being, only 23% reported a boost in well-being when they tweet, and 46% outright disagreed with this notion. However, when another user acknowledges the tweet, 63.9% of subjects showed a boost in well-being (26% neutral and 9.8% disagree). This finding may result from the fact that following is a one-way interaction, since, most often, user’s may join a subject’s Twitter network automatically without any action by the subject or need for approval. However, replying or retweeting requires that another user directly respond to a post that the participant has published. Consequently, these may be considered more interactive categories and subsequently resulted in greater increases across all metrics except, “I would feel the same as before receiving the notification.” Out of the five active feelings measured through the survey, capable consistently garnered the lowest level of agreement and most respondent’s reported feeling neutral.
  41. 41. 37 Figure 4.6. General perception of Twitter interactions H2: A potentially reciprocal online interaction has the ability to improve user wellbeing This hypothesis was tested using a stratified sample consisting of Less Frequent and Frequent Twitter users. I deconstructed the evaluation categories in order to effectively compare results between the two groups: 1. How users perceive their Twitter community: Identified structural and perceived differences between Frequent and Less Frequent users 2. Expectations and hopes for Twitter interactions: Compared feelings of support emanating from Twitter network and interactions 3. Reactions to, and perceptions of Twitter interactions: Compared the perceived ability of the Twitter network to result in specified outcomes Sample characteristics (independent variables): 35 subjects were characterized as Frequent users (check their account at least once per day and tweet at least once 0.00% 20.00% 40.00% 60.00% 80.00% 100.00% Agree Neutral Disagree Boosts in Wellbeing for Tweeting and When Tweet is Acknowledged Boost in wellbeing when tweet Boost in wellbeing when tweet is acknowledged
  42. 42. 38 per day) while 26 subjects were deemed Less Frequent users (check their account at most multiple times per day and tweet at most once per week). Users who check their account multiple times per day but tweet at most once per week were categorized as Less Frequent users because they do not attempt to directly engage with their Twitter network on a daily basis, but rather monitor their accounts through reading Twitter feeds (defined as “checking the account.”) On average, Frequent users had more followers (613) than followees (499) while Less Frequent users had fewer followers (103) than followees (139). Figure 4.7. Structural configuration of Twitter networks Frequent and Less Frequent users were split fairly equally along the dimensions that evaluated friendship on Twitter; the majority of the stratified sample agreed that they consider less than 25% of contacts with whom they interact with on Twitter to be friends, and most communicate with less than 25% of contacts via additional channels. -2000 0 2000 4000 6000 8000 10000 NumberofContacts Frequent Users Less Frequent Users Number of Followers and Followees: Less Frequent and Frequent Users Followers Followees
  43. 43. 39 Figure 4.8. Perception of friendship on Twitter (Less Frequent and Frequent users) 1. How users perceive their Twitter community: 65% of Less Frequent users monitor other contacts’ activity, but do not interact directly (only 23% of Frequent users agreed with this statement.) Both groups 0% 60%20% 6% 14% 0% 69% 20% 11% 0% Frequent Users None Less than 25% 26%-50% 51%-75% 76%-100% Outside circle: Out of followers, what percent do you communicate with via other channels? Inside circle: What percent of contacts with whom you interact on Twitter are considered friends? 8% 46%31% 4% 11% 4% 65% 19% 8% 4% Less Frequent Users None Less than 25% 26%-50% 51%-75% 76%-100% Outside circle: Out of followers, what percent do you communicate with via other channels? Inside circle: What percent of contacts with whom you interact on Twitter are considered friends?
  44. 44. 40 reported high rates of similarity with their Twitter community (65% Less Frequent; 77% Frequent) and the groups had analogous rates for those contacts they consider to be part of their professional network (46.15% Less Frequent; 48.57% Frequent.) Regarding the closeness of Twitter connections, 8% of Less Frequent users stated that contacts are part of their close personal community and interactions are personal, while Frequent users noted that 23% were part of a close community and 34% engage in personal interactions. Both scored low on the provision of emotional support (16% Less Frequent; 23% Frequent). If someone tweeted a problem, 50% of Less Frequent users would feel concern and 35% would contact that user, while 74% of Frequent users would feel concern and 54% would contact the user. Overall, Less Frequent users were not as likely to feel concern for another contact or reach out if that contact was having a problem. Based on this data, the only structural differences in the network configurations were the average number of followers and followees for the two groups, the fact that most Less Frequent users monitor activity and do not directly interact on Twitter (65% compared to 23% for Less Frequent), and average Twitter use, namely how often subjects tweet and/or check their account.
  45. 45. 41 Figure 4.9. Structural and perceived differences in Twitter network (Less Frequent and Frequent users) 2. Expectations and hopes for Twitter interactions The groups reported similar findings for “hope for acknowledgment,” (48% Less Frequent; 51% Frequent.) However, only 54% of Less Frequent users are comforted by the fact that someone might acknowledge their contribution, compared with 89% for Frequent users. In addition the majority of Less Frequent users do not care if the tweet is acknowledged (69% Less Frequent; 43% Frequent) and fewer expect a response (4% Less Frequent; 20% Frequent). Regarding network responsiveness and perceived support, 15% of Less Frequent users believe someone would respond if they tweet a problem compared with 57% of Frequent 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 100.00% Professionalnetwork Closepersonal community Interactionsarepersonal Iwouldfeelconcernif someonetweeteda problem Ifsomeoneposteda problem,Iwouldcontact them Sharemanyinterestswith contacts Monitorbutdonot interact Networkprovides emotionalsupport Percent of Users Who Agree with Stated Metrics: Frequent and Less Frequent Groups Frequent Less Frequent
  46. 46. 42 users. In addition, only 22% of Less Frequent subjects believe that someone will respond to their question, compared with 53% of Frequent users. In addition, none of the Less Frequent users reported that Twitter could help them feel better if they were down in the dumps, compared with of 17% Frequent users. It is clear that both groups do not rely on their Twitter network for social support and, for the most part, do not care or expect their tweets to be acknowledged by their contacts (although Frequent users care 30% more than Less Frequent users). Both groups hoped for acknowledgment from other users and are comforted by the fact that someone may recognize their contribution to the Twitter community (although Frequent users are more comforted than Less Frequent). In addition, Frequent users are more inclined to believe that their Twitter community will respond if they are having a problem or pose a question. This discrepancy may be due to the fact that 77% of Frequent users interact on Twitter and do not just monitor other contacts’ activity, while 65% of Less Frequent users monitor and do not interact. Consequently, engaging with Twitter contacts may enact the reinforcing mechanisms (exhibited in Figure 1.1) and compel Frequent users to continue interacting with their network.
  47. 47. 43 Figure 4.10. Expectations and hopes for Twitter interactions (Frequent and Less Frequent users) III. Reaction to, and perceptions of, Twitter interactions: Regarding specific reactions to gaining a follower, receiving a reply, and being retweeted, Frequent users reported higher levels of agreement along all metrics except “I would feel the same” (Less Frequent users were more likely to agree that following, replying, retweeting, or not receiving a response from another user, would result in them feeling the same as before receiving the notification.) Overall, Frequent users showed significantly higher levels of agreement for feeling recognized when they gain a follower than Less Frequent users (83% for Frequent; 58% for Less Frequent). However, when the interaction consisted of a reply or a retweet, both groups reported relatively high levels of agreement for recognized, connected, and appreciated, and Less Frequent users exhibited significant increases in agreement for a retweet or reply. According to Figure 4.11, which 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 100.00% Rewarding Outlettoexpress ideas Sourceofstimulation WhenItweet,feel boostinwellbeing Whentweetis acknowledged,feel boostinwellbeing Networkissourceof encouragement Feelconnectedto largercommunity Percent of Users Who Agree with Stated Metrics: Frequent and Less Frequent Groups Frequent Less Frequent
  48. 48. 44 compares reactions between the stratified sample, the increase is represented by the change from blue responses to green and orange responses. The graph visualizes the difference in rates of agreement for Frequent and Less Frequent Users across three scenarios (gaining a follower, receiving a reply, and being retweeted) for recognized, connected, and appreciated (which were shown to garner the highest response rates for metrics associated with positive affect when testing H1). The effect is especially pronounced for Less Frequent users in reported feelings of connectedness, followed by the second largest increase in feelings of recognition. Figure 4.11. Reactions to gaining a follower, receiving a reply, and being retweeted (Frequent and Less Frequent users) In terms of capability, the groups both showed relatively low levels of agreement compared with other metrics. However, receiving a reply and being retweeted resulted in higher feelings of capability for both user groups. 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 100.00% Recognized Connected Appreciated Percent of Users Who Agree with Stated Metrics: Frequent and Less Frequent Groups Follow: Frequent Follow: Less Frequent Reply: Frequent Reply: Less Frequent Retweet: Frequent Retweet: Less Frequent
  49. 49. 45 Figure 4.12. Reaction to feeling capable (Less Frequent and Frequent users) Data regarding whether gaining a follower, receiving a reply, or being retweeted did not affect the user (“I feel the same as before receiving the notification”) demonstrated that Less Frequent users are more likely to disagree with this statement (Figure 4.13). In other words, Less Frequent users are more likely to acknowledge that these online interactions produce some effect. These reports are particularly distinct for receiving a reply and being retweeted. Overall, gaining a follower is least likely to affect users. 0.00% 20.00% 40.00% 60.00% 80.00% 100.00% Gain follower Receive reply Retweeted Percent of Users Who Feel More Capable: Frequent and Less Frequent Groups Frequent Less Frequent
  50. 50. 46 Figure 4.13. Users who feel the same after receiving a notification (Less Frequent and Frequent users) General reactions to Twitter community: Frequent users experienced higher levels of agreement along all metrics, including feeling rewarded, stimulated, and connected to a larger community. Using the 50% threshold for significance, both groups believe utilizing Twitter is stimulating, while Less Frequent users had fewer than 50% in agreement across the remaining dimensions. In addition, more Frequent users felt Twitter is an outlet to express ideas compared with Less Frequent users. Both groups reported relatively low levels of agreement for the item measuring whether subjects experienced boosts in wellbeing when they tweet. However, when the tweet is acknowledged, both agree that they feel a boost in wellbeing. In order to more precisely compare responses between Frequent and Less Frequent users, I categorized reactions based on whether subjects agreed, were neutral, or disagreed. The dimensions that displayed the largest discrepancy between responses, including feeling rewarded, connected to a larger community, and that Twitter is an outlet to express ideas, may be the result of reciprocal 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% Gain follower Receive reply Retweeted Percent of Users Who Feel the Same When Receive Notification: Frequent and Less Frequent Groups Agree: Frequent Disagree: Frequent Agree: Less Frequent Disagree: Less Frequent
  51. 51. 47 interactions; since the Less Frequent group does not usually interact through Twitter, they likely experience these interpersonal benefits less often than the Frequent users, or not at all. However, both groups scored low on “When I tweet, I feel a boost in wellbeing,” but relatively similarly on “When my tweet is acknowledged, I feel a boost in wellbeing.” This supports the notion that actual or perceived benefits are related to reciprocity rather than the one-way projection of information. 4.14. General reaction to Twitter network (Frequent and Less Frequent users) 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 100.00% Rewarding Outlettoexpressideas Sourceofstimulation WhenItweet,feelboostin wellbeing Whentweetis acknowledged,feelboostin wellbeing Networkissourceof encouragement Feelconnectedtolarger community UseTwittertowastetime Sociallyisolated Percent of Users Who Agree with Stated Metrics: Frequent and Less Frequent Groups Frequent Less Frequent
  52. 52. 48 Less Frequent users were more likely to disagree with all metrics except “I use Twitter as a way to waste time” and using Twitter leads to social isolation. The level of disagreement among this group significantly decreased between “When I tweet I feel a boost in wellbeing” and “When a user acknowledges my tweet I feel a boost in wellbeing.” Less Frequent users showed higher levels of disagreement for feeling connected to a larger community and that engaging with their Twitter network is rewarding. Response rates were most similar for “When I tweet I feel a boost in wellbeing,” “Twitter is a way to waste time,” and Twitter makes me feel “socially isolated.” 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 100.00% Rewarding Outlettoexpressideas Sourceofstimulation WhenItweet,feelboostin wellbeing Whentweetisacknowledged, feelboostinwellbeing Networkissourceof encouragement Feelconnectedtolarger community UseTwittertowastetime Sociallyisolated Percent of Users Who Feel Neutral About Stated Metrics: Frequent and Less Frequent Groups Frequent Less Frequent
  53. 53. 49 Figure 4.15. Users who disagree with metrics (Frequent and Less Frequent) 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 100.00% Rewarding Outlettoexpressideas Sourceofstimulation WhenItweet,feelboostin wellbeing Whentweetis acknowledged,feelboostin wellbeing Networkissourceof encouragement Feelconnectedtolarger community UseTwittertowastetime Sociallyisolated Percent of Users Who Disagree with Stated Metrics: Frequent and Less Frequent Groups Frequent Less Frequent
  54. 54. 50 Chapter 5 Discussion This study confirmed the proposed hypotheses. Reciprocal online interactions enhanced well-being for users, most notably through a perceived boost when a tweet is acknowledged as well as based on feelings of heightened recognition, connectedness, and appreciation, especially in the retweet and reply scenarios. In support of the second hypothesis, Frequent users expected their Twitter networks to be more responsive than Less Frequent counterparts, and demonstrated higher levels of positive sentiments across all metrics. However, as a pilot study with only 61 subjects, the results are promising but cannot be generalized. For example, gender (Haines, Begges, and Hurlbert, 2008), age, baseline levels of social support (Kaul and Lakey, 2003), measures of introversion and extroversion, as well as general disposition, are known to affect feelings of support and modify the influence of social contexts (Steinfield, Ellison, and Lampe, 2008). In addition, certain types of individuals might gravitate toward Twitter, thereby skewing results. Without a detailed assessment of subjects’ underlying characteristics, it is difficult to identify the specific processes that led to observed outcomes. Nevertheless, noted boosts suggest the possibility for more in-depth work dedicated to understanding the associations between network configurations, direct interactions, and subsequent user reactions. The data is largely consistent with the theory of perceived companionship. First, it is likely that Twitter users do not know their contacts on a personal level. Therefore, the friendship bond that traditionally underpins companionate
  55. 55. 51 interactions is, in fact, nonexistent (Rook, 1987). In addition, connections are not pursued for social support and subjects feel affinity toward their network based on shared interests. However, the data does not indicate whether these positive outcomes are in response to the content of the tweets or, alternatively, a reaction to the perceived intent of the user, as postulated by the theory of perceived companionship.7 The diminished importance of message content is an integral component of this construct because it applies heightened significance to the role of subjective perception in online communication. Moving forward, research should evaluate why users exhibit certain responses to specific types of interactions in order to understand the affordances of various forms of exchange. Companionship is generally measured by whether subjects believe they interact with people who share their interests, have individuals they can get together with and have fun, and overall satisfaction with these relationships (Wright, 2000). It has also been discussed in the context of parasocial interactions, whereby relations between humans and social robots are evaluated for their ability to provide comfort or stimulation (Leite et al., 2010). The theory of perceived companionship explores the possibility of a type of relationship that falls somewhere between a parasocial and companionate interaction. As previously stated, researchers have just begun to investigate Twitter’s capability as a powerful medium for fostering feelings of perceived social support (Dodds et al., 2011). The data indicates that, although users might not be directly connected through additional channels, subjects may nonetheless accrue some personal benefit through interactions with these weak ties, especially during interactive exchanges such as retweets and replies. Wellman, Gruzd, and Takhteyev (2011) evaluated real and “imagined” communities on Twitter and found that both 7 Anecdotal accounts were obtained for select survey respondents that described a tweet or exchange that they found particularly meaningful. (Appendx: Section 7).
  56. 56. 52 friends and non-friends are equally connected to the subjects’ network and display varying degrees of interpersonal commitment. In this study, the inference that interactions between purported weak ties produces a boost in well-being merits further exploration into how network membership affects the perception of interactions and subsequent health outcomes. As mere snapshots of subject perception, it is difficult to understand if and how elevated levels of well-being may impact users beyond isolated interactions. Despite this dearth of information, the fact that more than 50% of Less Frequent and Frequent users exhibited boosts along a number of dimensions suggests that the potential for positive feelings to be channeled toward beneficial activities. Consider the following quotations: “Transient happy moods lead people to seek out others and engage with the environment, to be more venturesome, more open, and more sensitive to their individuals” (Lyubomirsky, King, and Diener, 2005; pp. 836) “We perceive social isolation when social opportunities and relationships do exist, but we lack the capacity to harness the power of social connectedness in everyday life (Hawkley and Cacioppo, 2010; pp. 224)” “Our biggest problems have no technological solution. We have come through the industrial age, the information age. Now we need to prepare ourselves for what I call the human engineering age and address the relationships which enable societies to work” (Herbert, 2011). According to Heinz Wolff, we cannot employ technology to “address the relationships that enable societies to work” (Herbert, 2011). However, instead of serving as a solution, mediated communication may be used as a tool to inspire feelings that enable individuals to lead more productive lives. Emboldened by
  57. 57. 53 even slight boosts in optimism, energy, belongingness, and self-efficacy, people may be compelled to take advantage of opportunities to further enhance their well-being or the happiness of others. Findings may also be applied toward measures aimed at preventing negative affect and associated psychological disorders. Specifically, a lack of pleasurable experiences have been cited as the cause of some mental illnesses, such as depression, and regular small bursts in well-being could help individuals maintain elevated levels of contentment (Lyubomirsky, King, and Diener, 2005). The World Health Organization estimates that by the year 2030, depression will be the number one source of disability in both developed and developing countries (World Health Organization, 2008). In the United Kingdom, the government has undertaken a nation-wide initiative to measure the well-being of 200,000 citizens in order to inform a range of public policies (UK National Statistics, 2011). For example, according to the cabinet secretary, improving the mental health and well-being of the unemployed could motivate them to find work (Ramesh, 2007), which would benefit society at-large. As such, there is a clear need for ways to effectively enhance well-being and increase happiness. In light of the findings, it might be more accurate to frame the hypotheses in terms of mediated communication rather than online communication, focusing on environments that, like Twitter and text messaging, may be analogously information-poor. Subjects’ perceived similarity and care for Twitter contacts considered to be weak ties, demonstrated boosts in well-being, heightened responses associated with replying and rewteeting, a lack of expectation regarding the responsiveness of the community, minimal disappointment when a post is not recognized by others, and increased feelings of comfort, among other notable findings, suggest the possibility for a much larger, more impactful conclusion
  58. 58. 54 regarding how and why we communicate online and ways in which correspondences and programs may be directed to improve health.