Computer-Mediated Communication and Social Support.

An extensive breadth of research has analyzed social support in face-to-face (FtF) conditions and reported its ample spectrum of positive effects on well-being by allowing recipients to cope better with stressful and problematic events in their lives (e.g., Burleson, 2009; High & Dillard, 2012). Furthermore, more recent studies have been evaluating how social support theories and its effects equate in online environments due to an increasing number of people turning to online social support networks due to the anonymity, heterogeneity, objectivity and asynchronicity such environments provide (e.g. Kim et al, 2012; Yoo et al., 2014; Wright et al., 2013). Anonymity and privacy provided by computer-mediated environments have been found to be key factors to facilitate self-disclosure and motivate individuals to reach out for support and information without fears of being judged, secluded or victimized (Rains, 2013; DeHaan et al., 2013). Moreover, computer-mediated social support (CMSS) has allowed individuals – especially those with health-related and stigmatized conditions – to compensate for a lack of access to information and ability to form offline relationships with others (often weak-tied) who share the same condition. In addition, CMSS allows individuals to seek and provide support more easily without feelings of embarrassment or limitations related to gender attributions and time-space constraints (Spottsswood et al., 2013; DeHaan et al., 2013, McKenna & Bargh, 1998; Robinson et al., 2009).

Overall, CMSS research has put forward many interesting findings. For example, it has been found that emotional well-being and social support satisfaction are moderated by communication competence (how skillful a person is in communicational interactions). Yoo et al. (2013) and Wright et al. (2014) found that social support satisfaction and well-being are positively affected by recipient’s communication competence so that the higher communication competence, the more social support satisfaction and well-being. Another research reports that the level of emotional expression (emotional bandwidth) in support seeking affects support provision in the sense that emotionally charged self-disclosures (high level emotional bandwidth) from the part of the support seeker do not necessarily equate to receiving more supportive messages; but rather, that the willingness to provide support is mediated by the sex, level of perceived sense of community and preference for online social interaction (POSI) from the part of the support provider. Results of this research suggest that only females and those with a strong POSI and a sense of community are willing to comfort individuals who disclose their distress through high levels of emotional bandwidth (High et al., 2014). Regarding preferences for CMSS and based in self-consciousness theory, Lim et al. (2012) posit that support seekers seem to prefer CMC mediums over FtF when their perceived inter-personal cost is rather high. In other words, support seekers would prefer CMC when they perceive that they may fail to meet others’ expectations.

Although CMSS research has been able to assess many effects of online network use on supportive communication, sampling mechanisms and self-report data collection instrumentation have been constantly mentioned as two of the most important limitations in this type of research. Problems in sampling have arisen due to the limited number of participants and the difficult task to enroll and analyze individuals who are dealing with stressors, especially if the stressors are related to health debilitating or stigmatized conditions (e.g. Lawlor et al., 2014; Crowson & Goulding, 2013). One way study designs have addressed this limitation is by having participants fill online surveys anonymously (e.g. Bockting et al., 2013). Yet, researchers have mentioned that this type of online recruitment may be contacting individuals who are already coping fairly well with their conditions and thus limit the generalizability of findings. Another problem in sampling is that the majority of participants in many studies have been female which may mean that recruitment mechanisms are underrepresenting men in samples (e.g. Wright & Rains, 2013). Finally, data collection has relied heavily on self-report instrumentation (i.e. surveys, questionnaires) which have been linked to error and lack of objectivity which may lead in turn to inaccuracies in results  (DeHaan & Mustanski, 2013; Bockting et al., 2013; Wright et al, 2013; Berger et al., 2005).

In order to address a few of these limitations, efforts have been made to conduct data analysis employing automated methodologies in online networks that do not rely on traditional recruitment procedures and self-report data. In this sense, social support communication scholars have employed content analytic software like InfoTrend (e.g. Yoo et al., 2013; Kim et al., 2012) in order to automatize the measurement of support exchange levels (by identifying the number of supportive messages provided and received in a forum for a particular patient) in an online community, but then again, these methodologies have been limited by the constraints imposed by the use of self-report instruments at the time of assessing impact of level of support on sentiment change in patients which has, in turn, limited the number of participants (to about 236 at best).

Working in this direction, informatics researchers have tried to improve this automated methodologies and tackle the need for self-report data, by employing text-mining and machine learning theories in the analysis of user behavior in online social networks (e.g. Zhao et al., 2014; Wang et al, 2014). For example, Zhao et al. (2014) has employed text-mining and data mining classifiers (artificial intelligence) to identify influential supporters in an entire online social support community based on message features and emotional outcomes of support seekers. More specifically, the methodology of Zhao’s study consisted in training classifiers with an example of 298 randomly selected forum posts with negative and positive sentiments. Positive and negative sentiment posts were differentiated by several lexical and style features of the posts (exclamation marks, question marks, paralinguistic symbols, slang, etc.). For example, the words “happy”, “joy” and “lol” as well as the emoticon “J” would belong to a positive sentiment message whereas the words “disappointed” and “painful” as well as the emoticon “L”would belong to a negative sentiment message. Next, a comparison between sentiment strength between the thread originator’s first post and the subsequent first self-reply was used to set a metric (influential responding reply or IRR metric) that would determine the strength of intermediate responding replies (support messages from other users of the forum in response to the thread originator’s post) in order to identify influential users in a forum in the sense that their posts would be the ones that generated the most significant sentiment change in threads overall. Consequently, this approach helps identify providers whose enacted support leads to the greatest recipient’s emotional improvement.

A limitation of this automated methodology, however, is that it relies on observational data and therefore is not able to fully address causal effects regarding original support seeker’s sentiment. In other words, the change in seeker’s sentiment could have been not caused by the intermediate thread posts necessarily, but rather by other factors such as communication through other channels like private messaging, e-mail, offline interventions, etc. Nonetheless, the implications of this type of research are interesting and have already laid down the foundations for strategies that could be used by online support network managers in order to identify potential good supporters in an efficient way at any given moment and state of an ever-changing online social network structure. Once good supporters are identified, online community managers could further encourage their involvement and participation in forums via public recognition of their contributions to the online community (e.g. virtual badges, awards, prizes, etc.)

Future research in this line of research may serve to strengthen and broaden the CMSS communication literature. For example, effects of features of supportive messages (verbal person centeredness, length, etc.) as well as gender attributions (e.g. High & Solomon, 2014; Spottswood et al., 2013) could be further assessed and validated by analyzing the posts generated by the most influential support providers. In a similar way, studies analyzing the most common types of support based on the optimal matching model and type of stressor (e.g. Rains et al., 2015) could be validated by the rapid identification of type of support of the most influential user’s posts in online forums that deal with different type of stressors. Finally, the scarce social support literature on support seeking could be strengthened by further analyzing support seeker’s initial posts in order to detect message features that provoke the best supportive responses (those that draw the attention of the most influential users) and that ultimately led to the greatest and most positive emotional changes.

Although automated massive data extraction/analysis mechanisms can lead to potentially interesting findings, another way researchers from communication and informatics fields could advance CMSS literature is by exploring supportive communication patterns in mobile communication platforms. Based on social awareness streams (SAS) literature (Naaman et al., 2010), which describes the way information is produced and consumed by mobile devices (e.g. semi-public short messages) and theories that describe selective attention and multitasking (e.g. limited capacity model, staccato attention), communication scholars have started to analyze social supportive communication enacted in SASs; also known as staccato social support (SSS). This type of social support refers to mobile-mediated supportive communication that is characterized by individuals who are processing information while multitasking (doing multiple activities at the same time). This cognitive processing of information is known as staccato attention (Hembrooke, 2003) and is characterized by unobtrusive bursts of short mediated behaviors such as skimming a Facebook newsfeed or sending a tweet that do not disrupt daily activities. This type of behavior is expected from individuals using smartphone technology which allows them to communicate online while on the move.

In spite that this type of frequent online communication may be replacing traditional local social support networks (Stefanone et al, 2012), few initial attempts to study SSS have been published, Adams et al. (2014), for instance, developed a mobile application (VERA) that encourages users to post their health decisions in a SAS. When making a health-related decision, the user would start VERA and take a photo that would document the decision. The user then records a subjective healthiness rating, enters an optional text caption, sets a binary option which tell whether the depicted action was performed or not, and finally selects a picture describing how the user feels about the health decision. Other users can see the post on their newsfeed and write messages to the original poster. The pilot of VERA was tested with participants who downloaded the app from Google Play or Apple store and their interactions were monitored for a span of 4 weeks. Message content was analyzed and categorized according to the type of support provided (informational, tangible, esteem, emotional or network). Results of this study report a significant frequency of informational and esteem support production. Given that the great majority of users posted about health concerns which were fairly under the recipient’s control (e.g. weight-loss, general well-being habits, etc.), the high production of informational support validates the optimal matching model proposed by Cutrona and colleagues. However, the high production of esteem support contrasts with the same optimal matching model which posits that nurturant types of support (emotional and esteem) are more adequate for uncontrollable stressors. Furthermore, the high prevalence of esteem support also differs with findings of meta-analytic reviews of supportive messages in online social networks, which indicate that emotional and informational support messages are produced more frequently than esteem support messages (Rains et al., 2015). A possible explanation for the results could be that SSS may preclude certain styles of interaction due to staccato attention (e.g. no long narratives, users trained to “like” statuses). Another explanation could be that features of the medium (e.g. small keyboards of mobile phones) make the production of esteem support more prevalent in mobile communication platforms. Future research on SSS should validate these findings and further assess the impact of messages (both positive and negative) on recipients. In addition, lurking (not actively participating in an online community) could also be analyzed as to determine whether lurkers may still benefit from SSS environments as seems to be the case in traditional CMSS platforms given that it has been found that lurking may have similar empowering and positive effects to those accrued to active participants without having to actually communicate (post messages) with others in the online community (Phoenix & Coulson, 2010; Mickelson, 1997).

Evidently, interdisciplinary research is already leading to a better understanding of how people communicate support. Furthermore, findings are aiding design and development of customized social support computer applications as well as more efficient automated data collection/analysis mechanisms that address and minimize the limitations of traditional methodologies.

References

Adams, Phil, Eric PS Baumer, and Geri Gay. “Staccato social support in mobile health applications.” Proceedings of the 32nd annual ACM conference on Human factors in computing systems. ACM, 2014.

Albrecht, Terrance L., and Mara B. Adelman. Communicating social support. Sage Publications, Inc, 1987.

Berger, M., Wagner, T. H., & Baker, L. C. (2005). Internet use and stigmatized illness. Social science & medicine, 61(8), 1821-1827.

Bockting, W. O., Miner, M. H., Swinburne Romine, R. E., Hamilton, A., & Coleman, E. (2013). Stigma, Mental Health, and Resilience in an Online Sample of the US Transgender Population. American journal of public health, 103(5), 943-951.

Burleson, B. R. (2009). Understanding the outcomes of supportive communication: A dual-process approach. Journal of Social and Personal Relationships, 26(1), 21-38.

Cutrona, Carolyn E., and Daniel W. Russell. “Type of social support and specific stress: Toward a theory of optimal matching.” (1990)

Crowson, M., Goulding, A., (2013), Virtually Homosexual: Technoromanticism, demarginalisation and identity formation among homosexual males. Computers in Human Behavior.

DeHaan, S., Kuper, L. E., Magee, J. C., Bigelow, L., & Mustanski, B. S. (2013). The interplay between Online and offline explorations of identity, relationships, and sex: A Mixed-methods study with LGBT youth. Journal of Sex Research, 50(5), 421-434.

Hembrooke, H., and Gay, G. The laptop and the lecture: The effects of multitasking in learning environments. J of Computing in Higher Ed. 15, 1 (2003), 46–54.

High, A. C., & Solomon, D. H. (2014). Communication Channel, Sex, and the Immediate and Longitudinal Outcomes of Verbal Person-centered Support. Communication Monographs, 81(4), 439-468.

High, A. C., & Dillard, J. P. (2012). A review and meta-analysis of person-centered messages and social support outcomes. Communication Studies, 63, 99-118. doi: 10.7080/10510974.2011.598208

High, A. C., Oeldorf-Hirsch, A., & Bellur, S. (2014). Misery rarely gets company: The influence of emotional bandwidth on supportive communication in Facebook. Computers in Human Behavior, 34, 79-88. doi: 10.1016/j.chb.2014.01.037

Kim, E., Han, J. Y., Moon, T. J., Shaw, B., Shah, D. V., McTavish, F. M., & Gustafson, D. H. (2012). The process and effect of supportive message expression and reception in online breast cancer support groups. Psycho‐Oncology, 21(5), 531-540.

Lawlor, A., & Kirakowski, J. (2014). Online support groups for mental health: A space for challenging self-stigma or a means of social avoidance?. Computers in Human Behavior, 32, 152-161.

Lim, V. K., Teo, T. S., & Zhao, X. (2013). Psychological costs of support seeking and choice of communication channel. Behaviour & Information Technology, 32(2), 132-146.

McKenna, K. Y., & Bargh, J. A. (1998). Coming out in the age of the Internet: Identity” demarginalization” through virtual group participation. Journal of personality and social psychology, 75(3), 681-694.

Mickelson, K. Seeking social support: Parents in electronic support groups. In Culture of the Internet,S. Kiesler, Ed., Erlbaum (Mahwah, NJ, 1997), 157–178.

Naaman, M., Boase, J., and Lai, C.-H. Is it really about me?: message content in social awareness streams. InProc. CSCW, ACM (2010), 189–192.

Rains, S. A., Peterson, E. B., & Wright, K. B. (2015). Communicating social support in computer-mediated contexts: A meta-analytic review of content analyses examining support messages shared online among individuals coping with illness. Communication Monographs, 1-28. doi: 10.1080/03637751.2015.1019530

Rains, S. A. (2013). The Implications of Stigma and Anonymity for Self-Disclosure in Health Blogs. Health communication, (ahead-of-print), 1-9.
Robinson, J. D., & Turner, J. (2009). Impersonal, interpersonal, and hyperpersonal social support: Cancer and older adults. Health communication, 15(2), 227-234.

Spottswood, E. L., B Walther, J., Holmstrom, A. J., & B Ellison, N. (2013). Person‐centered emotional support and gender attributions in computer‐mediated communication. Human Communication Research, 39, 295-316.

Tan, Ah-Hwee. “Text mining: The state of the art and the challenges.” Proceedings of the PAKDD 1999 Workshop on Knowledge Disocovery from Advanced Databases. Vol. 8. 1999.

Witten, Ian H., and Eibe Frank. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2005.

Wright, K. B., Rosenberg, J., Egbert, N., Ploeger, N. a, Bernard, D. R., & King, S. (2013). Communication competence, social support, and depression among college students: A model of Facebook and face-to-face support network influence. Journal of Health Communication, 18, 41-57. doi:10.1080/10810730.2012.688250.

Yoo, W., Namkoong, K., Choi, M., Shah, D. V., Tsang, S., Hong, Y., … & Gustafson, D. H. (2014). Giving and receiving emotional support online: Communication competence as a moderator of psychosocial benefits for women with breast cancer. Computers in human behavior, 30, 13-22.

Zhao, K., Greer, G. & Yen, J. (2014) Finding influential users of an online health community: a new metric based on sentiment influence. American Medical Informatics Association.

Computer-mediated Communication and Stigma.

Extensive research has elaborated and transitioned from the traditional definition of stigma as being a mark on someone of questionable moral status (Goffman, 1963), to support and propose a more complex view of stigma as a social construct based on human perceptions of differences in a particular culture and time (Meisenbach, R., 2010). Furthermore, scholars have considered stigma as a natural human communication phenomenon in response to the unknown or non-normative that further supports group identification (Burke, 1969). The latter notion being very important as it gives support to the argument that stigma is a necessary and inevitable construct of a society, for it promotes group cohesion and solidarity by allowing individuals to recognize insiders and outsiders of their groups (Fulk, 2001). As for stigma classification, literature does not give a clear, unified and standardized typology of stigma, yet researchers tend to categorize it in relation to different dimensions of an individual’s life; being health, work and sexual aspects the most frequently studied (e.g. DeHaan, S., & Mustanski, B. S., 2013; Wright, K. B., & Rains, S. A., 2013; Meisenbach, R. J., 2010). In addition, research also categorizes stigma according to whether it is concealable or not (McKenna, K. Y., & Bargh, J. A., 1998) and whether it is felt or enacted (Bockting, W. O., & Coleman, E., 2013). It is important to note, however, that stigma exists as long as it is perceived by both non-stigmatized and stigmatized individuals and, as Meisenback (2010) argues, the degree to which stigma affects individuals is related to the valence and salience of such perceptions.

Although, stigma may be a natural and even a necessary human condition that promotes group solidarity as argued by Fulk (2001), it has been linked to a great array of negative outcomes such as discrimination, identity devaluation, prejudice and deterioration of physical and psychological health (Meisenbach, R., 2010). Consequently, much of the research on stigma has been focused on the identification of coping strategies that allow individuals to ameliorate the negative effects of stigma in their lives. Yet, and in spite of a few studies that have tried to forward a unified set or typology of coping strategies that may be useful for all type of stigmas (e.g. Meisenbach, R., 2010), the majority of research has been conducted on very specific groups of stigmatized individuals (e.g. lgbt, health, occupational) and, thus, has been able to identify coping strategies that may pertain only to those specific groups (e.g. Wright, K. B., & Rains, S. A., 2013; McKenna, K. Y., & Bargh, J. A., 1998; DeHaan, S., & Mustanski, B. S., 2013). Meisenbach (2010), nonetheless, argues that her typology of coping strategies may be useful for all types of stigma. The coping strategies proposed by Meisenbach are organized under the following main categories: acceptance, avoidance, evading responsibility, reducing offensiveness and denying. Overall, these coping strategies have been found to improve individuals’ quality of life by improving self-esteem, health and the provision of social support.

Regardless of coping strategy, the use of Internet based technology has been identified to greatly facilitate the access and application of almost all coping strategies due to the anonymity, privacy and self-disclosure fostered by a computer-mediated environment. In this sense, many studies have been devised to determine how stigmatized groups employ computer-mediated communication (CMC) in order to manage and cope with stigma as well as to establish the conditions under which benefits are maximized or minimized (e.g. Rains, S. A., 2013; DiNapoli, J. M., & Siller, J., 2013). Research has also tried to identify the way CMC affects offline behavior and access to offline resources (e.g. DeHaan, S., & Mustanski, B. S., 2013). Yet, while most of the findings in this type of research support the notion of CMC as being an aid in stigma management, there are few others that challenge the idea of CMC always leading to positive outcomes by trying to identify situations in which CMC may actually have a deleterious effect on stigmatized individuals (e.g. Lawlor, A., & Kirakowski, J., 2014).

In addition, across all studies, there has been a trend to use Internet based technology such as web surveys, blogs and e-mail in order to contact and collect data from stigmatized individuals who would, otherwise, be reluctant to participate in face-to-face scenarios due to embarrassment and concerns regarding the revelation of their true identity. Thus, CMC not only has been beneficial for those who have been impacted by stigma but also it has been a tremendous help for those who study it (e.g. DeHaan, S., & Mustanski, B. S., 2013, Rains, S. A., 2013).

Review of Findings

Studies on stigmatized communities have identified people turning to the internet to compensate for a lack of access to information and the inability to form offline relationships with others who share the stigmatized condition (e.g. DeHaan, S., & Mustanski, B. S., 2013, McKenna, K. Y., & Bargh, J. A., 1998). Moreover, the Internet has provided a means for them to more easily disclose information regarding their stigmatized conditions and experience the positive benefits of such self-disclosure like decreased levels of stress and access to social support, without having to reveal their identities and feel embarrassment (Rains, S. A., 2013). Anonymity and privacy provided by computer-mediated environments, thus, have been key factors that facilitate self-disclosure and motivate individuals to reach out for support and information without the fear of being judged, secluded or victimized (e.g. DeHaan, S., & Mustanski, B. S., 2013,Rains, S. A., 2013). Yet, research has identified that trust acts as a moderator in the level of self-disclosure. In other words, if an individual does not trust the target of his or her disclosures, self-disclosure would be greatly reduced. (Buchanan, T., & Reips, U. D., 2007)

In addition, Internet use has been found to promote group participation (especially in the case of concealable stigmas) that facilitate the incorporation of a group identity to the stigmatized individual’s social identity (McKenna, K. Y., & Bargh, J. A., 1998). This group identification, and more importantly, the importance the stigmatized person gives to it, has been found to lead to self-acceptance, higher self-esteem and a decreased sense of estrangement from society (McKenna, K. Y., & Bargh, J. A., 1998). As a result, individuals using the Internet, and more importantly, those participating more in CMC (posting and reading messages), come to terms with their identities and feel a greater sense of control of their coming-out process, making it easier for them to find social support and resources –which are not easily available in offline settings- that help them cope better with stigma and improve their overall well-being (McKenna, K. Y., & Bargh, J. A., 1998, Lawlor, A., & Kirakowski, J., 2014). In the case of men having sex with other men, for example, CMC has allowed them to better negotiate sex and make better decisions based on the disclosed information such as serostatus and condom use (Carballo-Diéguez, A., & Jacoby, S., 2006).

Although, in general, Internet use has been found to improve access to information as well as promote the formation of online relationships and group participation, easier access to information and online relationships does not always lead to positive outcomes. Information, for instance, could be of variable quality and veracity which makes difficult for individuals to find beneficial information. For example, an individual may think that he or she suffers from depression based on the information gathered from the Internet when, in fact, they are not clinically depressed, and thus, reach invalid or inaccurate assumptions about their health (Berger, M., & Baker, L. C., 2005). Similarly, while findings support that anonymity provided by computer-mediated environments improves disclosure of information and, as a result, allows the creation of bonds and the formation of online relationships, these bonds are not always strong enough to create meaningful relationships that would make individuals trust each other and meet offline, which may be a negative outcome in the case of identity stigmas (DeHaan, S., & Mustanski, B. S., 2013). Nevertheless, research has also identified that for the case of health stigmas, individuals may actually benefit from lower levels of stress and depression when they engage in weak-tie online relationships in their support networks as objectivity and access to a more varied array of information is maximized (Wright, K. B., & Rains, S. A., 2013).

Group participation has also led to mixed results in the sense that although more active participation may aid the application of coping strategies and allow individuals to benefit from social support and overall management of stigma, it could also further isolate them from offline social interactions and exacerbate their levels of distress if they show an excessive dependence on online interaction and allow their stigmatized identities to dominate them (Lawlor, A., & Kirakowski, J., 2014). In this sense, and as Lawlor, A., & Kirakowski, J. (2013), suggest: “perceived benefits of active participation are more likely to be attributable to other underlying factors that encourage [individuals] to actively participate than active participation per se”.

Further Research

Despite the mixed implications and findings in this line of research, CMC nonetheless has proven to be a great way for stigmatized individuals to cope with stigma and seek support that otherwise would be difficult to find or get in offline scenarios.Moreover, recent research seems to suggest that for the advantages of CMC to have a positive impact on an individual’s interpersonal and intrapersonal growth, they have to promote and foster positive changes in the offline world as well (Crowson, M., Goulding, A., 2013). Therefore, further research is needed to explore how CMC can maximize its benefits and how these could be transferred to the offline world as well as determine conditions under which CMC might actually have deleterious effects on individuals such as access to information of questionable value, social avoidance and pathological use of CMC that may impede interpersonal and intrapersonal growth.

One of the most important limitations of research on stigma has been sampling mechanisms that limit the range of action in terms of sample selection and data retrieval. Understandably, the fact that stigmatized people find it difficult to discuss about their stigmatized conditions in offline scenarios, has prompted researchers to rely on internet based mechanisms to contact and collect data. Online recruitment though may be contacting individuals who are already coping fairly well with stigma, and thus limit the application of findings to those who have already sought support. Besides, certain stigmatized conditions such as transgendered individuals may be reluctant to participate in studies that analyze them because such experimental settings may be perceived as a form of enacted stigma that further reminds them of their stigmatized condition. To note, is the fact that in almost all the samples of reviewed research, the majority of the participants are female (in many studies more than 70% are women) (e.g. Wright, K. B., & Rains, S. A., 2013, DeHaan, S., & Mustanski, B. S., 2013). Consequently, maybe men are being underrepresented in these samples. This situation raises a question as to whether women seek the most support on CMC platforms, or if it is because women find studies on their stigmatized conditions more appealing, or whether it is just because of sample size. Furthermore, online data collection relies heavily on self-report instrumentation which has been linked to error and lack of objectivity. All of these limitations in sampling techniques deserve the attention of future research.

Another limitation has been that research tends to analyze ways in which specific stigmatized groups benefit from CMC interactions, yet it has failed to perform parallel studies with people from different stigmatized groups in order to observe commonalities that would help determine those aspects which should be present and given importance in any type of online resource meant for stigmatized groups. Furthermore, research should explore more ways in which stigmatized individuals can integrate better their online and offline lives so that empowerment and support experienced in the virtual world translate into offline behavior that allows the individual to cope with stigma in the real world and, more importantly, encourage individuals to seek professional support that is very often only available in offline social networks.

Finally, a line of research should be urgently started in order to address the role and behavior of non-stigmatized individuals rather than those being stigmatized, for the whole problem of stigma may not make any significant progress unless both victims and those who give momentum to stigma are taken into account. Educational plans should be devised in order to rectify incorrect perceptions and prejudices as well as to get non-stigmatized individuals familiarized with differences that may be feared or rejected because of ignorance. The lgbtq community, for instance, has been devising plans to educate people and reduce their perceived stigma in society. Results have been promising so far, yet more research is needed in this area as to identify better mechanisms –online and offline- through which “stigmatizers” could help reduce the negative impact of stigma. After all, having not to cope at all with any stigma may be the best way to help those who are different.

References:

Buchanan, T., Joinson, A. N., Paine, C., & Reips, U. D. (2007). Looking for medical information on the Internet: self-disclosure, privacy and trust. He@ lth Information on the Internet,58(1), 8-9.

Berger, M., Wagner, T. H., & Baker, L. C. (2005). Internet use and stigmatized illness.Social science & medicine, 61(8), 1821-1827.

Bockting, W. O., Miner, M. H., Swinburne Romine, R. E., Hamilton, A., & Coleman, E. (2013). Stigma, Mental Health, and Resilience in an Online Sample of the US Transgender Population. American journal of public health, 103(5), 943-951.

Carballo-Diéguez, A., Miner, M., Dolezal, C., Rosser, B. S., & Jacoby, S. (2006). Sexual negotiation, HIV-status disclosure, and sexual risk behavior among Latino men who use the internet to seek sex with other men. Archives of sexual behavior, 35(4), 473-481.

Crowson, M., Goulding, A., (2013), Virtually Homosexual: Technoromanticism, demarginalisation and identity formation among homosexual males. Computers in Human Behavior.

DeHaan, S., Kuper, L. E., Magee, J. C., Bigelow, L., & Mustanski, B. S. (2013). The interplay between Online and offline explorations of identity, relationships, and sex: A Mixed-methods study with LGBT youth. Journal of Sex Research, 50(5), 421-434.

DiNapoli, J. M., Garcia-Dia, M. J., Garcia-Ona, L., O’Flaherty, D., & Siller, J. (2013). A theory-based computer mediated communication intervention to promote mental health and reduce high-risk behaviors in the LGBT population. Applied Nursing Research, 27, 91-93.

Lawlor, A., & Kirakowski, J. (2014). Online support groups for mental health: A space for challenging self-stigma or a means of social avoidance?. Computers in Human Behavior, 32, 152-161.

McKenna, K. Y., & Bargh, J. A. (1998). Coming out in the age of the Internet: Identity” demarginalization” through virtual group participation. Journal of personality and social psychology, 75(3), 681-694.

Meisenbach, R. J. (2010). Stigma management communication: A theory and agenda for applied research on how individuals manage moments of stigmatized identity. Journal of Applied Communication Research, 38(3), 268-292.

Rains, S. A. (2013). The Implications of Stigma and Anonymity for Self-Disclosure in Health Blogs. Health communication, (ahead-of-print), 1-9.

Wright, K. B., & Rains, S. A. (2013). Weak-Tie Support Network Preference, Health-Related Stigma, and Health Outcomes in Computer-Mediated Support Groups. Journal of Applied Communication Research, (ahead-of-print), 1-16.

Supportive Communication: An Overview.

Social support has been studied from a sociological, psychological and communicative perspectives. All of them have recognized the protective effects of social support in the physical and mental health of individuals. The sociological and psychological perspectives operationalize social support as social integration (e.g. participation and integration in social networks) and perceived availability of support (the perception that support is going to be available if needed), respectively. Yet, both of these views present theoretical issues regarding the exact causal mechanisms linking their operationalizations of social support with health outcomes. In other words, researchers have failed to understand exactly how social integration or perceived availability of support provide protection against health-damaging factors [8, 16].

In both perspectives, there had always been an implicit notion that communication played a role in the social support process [16]. And thus, a third alternative known as supportive communication has been explored more recently which assesses socialsupport as a communicative process rather than a “hidden mechanism or a perceptual outcome” [16]. This third perspective studies – in a more direct way – the verbal and nonverbal behaviors enacted when seeking or providing support. In other words, supportive communication analyzes more closely the mechanisms that connect different forms of supportive communication with physical and mental health in order to address the unresolved questions and go beyond the theoretical issues of the sociological and psychological approaches.

Working from the communication perspective, scholars in the field of communication sciences have been trying to explain findings of previous research with the aid of a more rigorous theoretical background derived from a detailed analysis of interactionsand message features. Burleson et al. (1994) indicate that “social support ascommunication means studying the messages through which people both seek andexpress support; studying the interactions in which supportive messages are produced and interpreted; and studying the relationships that are created by and contextualize the supportive interactions in which people engage.” In this sense, researchers has identified factors that moderate the outcomes of social support communication. In other words, researchers have found that in order for supportive communication to lead topositive outcomes (a distressed person feeling better), there is a set of factors that haveto be in place so that the provision of support – and its effects – can reach maximum effects. These factors are varied and could be related to message content, nonverbal behaviors, motivation of provider and recipient, communicative ability, interactants’ relationship, trust, physical and psychological features, among others [5, 11, 12, 17]. For example, research has found that when the recipient’s cognitive complexity (ability to scrutinize verbal messages) is low, or when the supporter does not have the necessary social-cognitive abilities to produce sophisticated supportive messages, the supportive communication interaction has a lesser impact on the recipient’s emotional state [5, 7].

As a result of the multidimensionality of factors affecting the supportive communication process, Burleson has proposed a dual-process theory of supportive communication outcomes which groups all of these factors into a simplified model that takes into account the verbal person centeredness (VPC) in emotional supportive messages, the recipient’s cognitive complexity, and motivation to process supportive messages and the outcomes of the supportive intervention (message outcomes). In sum, the theory proposes that when messages receive a high level of scrutiny form the receiver, the outcomes will be influenced mainly by verbal person centeredness of the messages. However, if the recipient assigns little scrutiny to the messages, then features of the context or the helper, or nonverbal behaviors may influence the outcomes of the supportive interaction [1, 3, 5].

VPC has received enormous attention form the research field due to its centrality in emotional support and appraisal theories [3, 6, 10, 15]. Emotional support, which is considered the most important type of social support by many scholars, is understood as the communicative behavior between a supporter and an emotionally distressed recipient. Studies have found that the more person-centeredness supportive messages have, the better the emotional outcomes (e.g. [10, 12, 14]). VPC derives from a constructivist perspective originally proposed by Applegate (1978) that refers to the extent to which messages acknowledge and legitimize the feelings and perspective of the recipients. This constructivist approach proposed a nine-level hierarchical coding system that helps determine the person-centeredness of a message. Eventually, Burleson (1982) adapted this hierarchical coding system to emotional social support communication research and it is what most researchers have been using in order to “measure” person-centeredness as a feature of emotionally supportive messages and provider’s sensitivity [7, 10]. Although the hierarchy has nine levels, the majority of studies in social support communication generally collapse all nine categories into three main levels: highly person-centered (HPC) messages which explicitly elaborate and recognize the other’s feelings (e.g. “It is understandable what you are feeling after what happened,”) moderately person-centered (MPC) messages which infer recognition (rather than explicit acknowledge) of the other’s feelings and perspectives (e.g. “I am sorry you guys broke up. But things like these happen in many relationships”), and low person-centered (LPC) which deny the other’s feelings (e.g. “You are worrying about nothing!”) [5, 7]. Initial research studies dealing with VPC have identified that, on average, people were more likely to produce MPC messages, although LPC and HPC messages were also commonly observed, being the HPC messages the least frequent[7].

Consequently,  a  great  many  studies  have  tried  to  understand  how  VPC affects the communication of social support (even before the advent of the dual-processtheory). These studies have relied on a methodology that consists of dyads of college students interacting in face-to-face or computer-mediated conditions (e.g. [7, 21].) Experiments have followed either a naturalistic paradigm (e.g. [3]) (recipients recalling messages received in response to a stressor), a message perception paradigm (e.g. [3,7]) (recipients’ evaluation of messages used in hypothetical dialogues or scenarios) or an experimental paradigm (recipients participating in actual emotional support situation) (e.g. [2, 14, 15]). Across the literature, limitations and lack of ecological validity have been found in the naturalist and message perception paradigms, respectively. For example, recipients’ evaluations may not be accurate as they rely on retrospective self-report data, or when recipients are exposed to hypothetical situations. The experimental paradigm overcomes these limitations by having the recipients interact with another person (often times a confederate) in a video-taped conversation (often times of about five minutes in duration) where person- centeredness (PC) of supportive messages is manipulated by researchers (e.g. [1, 10, 14]).

The design of these studies have identified the level of PC in messages (operationalized via the nine-level hierarchy) as the main independent variable. On the other hand, the dependent variables have been the recipients’ emotional outcome after receiving the messages (a.k.a. MO: message outcome), perception of message quality(a.k.a. ME: message evaluation) and evaluation of supporter’s competence [7, 14, 15]. These variables have been generally measured via coding of conversation transcriptsand with the use of a set of Likert scales which are filled by participants, confederatesand/or experimental observers (e.g. [7, 15]). In addition, quite recently, physiological measures such as cortisol levels, blood pressure and heart rate have been used in a few studies in order to assess the impact of VPC on stress (e.g. [2, 18]).

Although VPC has been found to be universally linked to positive outcomes, few studies have been unpacking the interactions between VPC and supportive communication outcomes.  In this sense, ME has been found to mediate the effect of VPC on MO (entirely according to Bodie et al. (2012) and partially according to High & Dillard (2012),) further supporting the notion of message scrutiny of the dual-process approach. In addition, while Bodie (2011) did not found a direct linear effect of message VPC on cardiovascular reactivity [2], Priem & Solomon (2014) have provided empiricalevidence of the interactions (at a conversation rather than a message level) between supportive communication and cortisol levels in distressed individuals [18].

Precisely, these newer studies have shed light on the limitations that VPC research hashad so far. One of these limitations is the lack of verification for support adequacy. Support adequacy has been found to have effects on how recipients evaluate support provision. For example, a provider’s HPC messages could have different effects on emotional outcomes depending on the type of support (and how much of that support)the recipient wants [4, 18]. Another limitation is a lack of analysis on how VPC equates in invisible support. Research has found that there are benefits of receiving invisible support (not perceived when provided), for it reduces the costs of enacted support [9, 18]. In this sense, HPC messages may make support more visible and thus lower their positive effects on emotional outcomes. A distressed person may prefer MPC rather than HPC supportive messages, for the lower-level PC messages may be less facethreatening and may spare the recipient from being obliged to return favors to the provider in the future. Another limitation is that the effect of time in supportive communication is not always taken into account in the sense of longitudinal outcomes (persistency of outcomes). VPC research lacks an experimental approach to assess actual – rather than perceived – emotional improvement. Five- minute conversations may make it difficult for researchers to really know whether person- centeredness in support provision is actually helping individuals in the long term. Yet another shortcoming of VPC research has been the isolated assessment of recipient’s perceptions rather than collecting and finding linkages between both recipients’ and providers’ impressions on the same supportive interaction in a conversational rather than message-level approach. Moreover, a meta-analysis of research on person-centeredness and support outcomes done by High & Dillard (2012), found that the interaction between strangers attenuates the effects of PC on support outcomes due probably to the absence of real relational history between interactants. Finally, the majority of studies rely on college students for their experiments and this lack ofdiversity in terms of age, culture and socio-economical background could limit the validity and generalizability of findings.

Consequently, future research should further assess the effects of support adequacy, invisibility, outcomes persistency and both interactants’ perceptions on VPC in supportive communication using a more diverse group of real relational partners. Moreover, in order to encourage a more conversation-level approach in VPC research,it would be interesting to explore a unification of the VPC and message design logics perspectives, for the latter perspective has identified certain ways of talking that are more effective than others at the moment of eliciting support provision depending on the situation and relational goals of the interactants [20]. In addition, there seems to bea sense of impracticality of the nine-level hierarchy that operationalizes VPC messages; after all, many studies group all those nine categories into three main levels, as explained previously. In this sense, as High & Dillard (2012) point out, more research is needed to confirm the validity of all nine levels in the hierarchy.

Last but not least, the majority of research on social support has been conducted in Face- to-Face (FtF) interactions. An increasing number of people are using video-mediated communication (VMC) as a practical means for communicating from geographically dispersed locations. Video-based technologies such as Skype, Google Hangout and Facetime have become very popular due to a cheaper, faster and moreubiquitous broadband Internet access [13]. Thus, it is very likely to infer that people maybe using video-mediated platforms to seek and provide social support. Nevertheless, there is a complete lack of research that studies video-mediated social support (VMSS). Given the importance of social support communication and its increasing prevalence in VMC platforms, it is quite important to continue research that assesses towhat extent VMC environments are affecting the communication of social support.

Research on social support in CMC environments has found that an increasing number of people are turning to online social support networks because of the anonymity, heterogeneity, objectivity and asynchronicity such environments provide, making it easier for individuals to disclose information and access a varied array of problem-focused resources while being honest and open about their feelings without having to worry about embarrassment, inter-personal costs, stigma-related and time-space constraints [19, 21, 22]. Therefore, VPC should be extensively explored in CMCdue to the benefits such digital environments provide for support communication. This line of research has already found that VPC effects have different interactions in CMC. For example, Brock and Lawrence (2009) found that, unlike what takes place in FtF interactions, men evaluated as more effective the HPC messages coming from male supporters [21]. However, one of the most noticeable shortcomings of this type ofresearch is the lack of customized software that would allow researchers to have more control over how digital environments affect outcomes in experiments. For instance,customized and controlled access to historical data of logged computer- mediated support interactions could aid in the assessment of actual effectiveness and long term effects of support. Eventually, research on VPC, CMC and VMSS will lay down theguidelines for the design and development of computer-assisted support software in thenear future. For example, it would be interesting to create knowledge-based interactive computer applications that could assist a supporter in the elaboration of VPC messages while they are interacting remotely with a support seeker in real time.

 

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