AIDS Care, 2015 Vol. 27, No. 6, 693–696, http://dx.doi.org/10.1080/09540121.2014.991678
Social support exchanges in a social media community for people living with HIV/AIDS in China Liang Chen* and Jingyuan Shi Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore (Received 15 April 2014; accepted 19 November 2014) In recent years, social media has become an important source of social support. People living with HIV/AIDS in China created an online support group (the HIV/AIDS Weibo Group) on Weibo, the Chinese version of Twitter, in January 2011. The current study examined how social support transmitted in this social media community. First, messages over five successive weeks (2 May 2011 to 13 June 2011) were randomly selected from the HIV/AIDS Weibo Group on Weibo. Next, we employed social network analysis to map the HIV/AIDS Weibo Group’s structure and to measure the study variables. After that, a multivariate analysis of variance was applied to examine the influence of frequency of contact and reciprocity on informational and emotional social support exchanged in each dyad. The results revealed that pairs with a high level of contact frequency or reciprocity exchanged more informational support than do pairs with a low level of contact frequency or reciprocity. Moreover, dyadic partners with high frequency of contact exchanged a larger amount of emotional support than those with a low level frequency of contact; but strongly reciprocal dyads did not exchange significantly more emotional social support than their counterparts with a low level of reciprocity. Keywords: social media; social network analysis; social support; social ties; China
With the advance of web 2.0 technologies, social media have empowered patients to connect with others online to exchange social support (Yang, 2014). People living with HIV/AIDS (PLWHA) need help from supportive others to manage their uncertainty about health, identity, and social relationship (Brashers, Neigid, & Goldsmith, 2004). In 2011, PLWHA in China created a social media community, named the HIV/AIDS Weibo Group (http://q.weibo. com/267539), on Weibo (the Chinese Twitter equivalent). It provides the same microblogging services as Twitter, but Weibo users also created interest groups on it. Social support refers to “an interpersonal transaction involving one or more of the following: emotional concerns, instrumental aid, information, or appraisal” (Cutrona, Suhr, & MacFarlane, 1990, p. 30). Normally, social support is transmitted through social ties to achieve its health outcomes (Burleson, Albrecht, Goldsmith, & Sarason, 1994). Social ties are defined as the relationships built based on resource exchanges, such as information, services, and social support (Haythornthwaite, 2002; Sibanda, 2010). Three main dimensions: duration of relationship (Stefanone, Kwon, & Lackaff, 2011), frequency of contact (Lin, Dayton, & Greenwald, 1978), and reciprocity (Friedkin, 1980) have been employed to measure the strength of ties. In general, individuals who maintain weak ties engage in non-intimate, infrequent, and more casual exchanges (Granovetter, 1982; Haythornthwaite, 2002; *Corresponding author. Email: [email protected] © 2014 Taylor & Francis
Wellman & Gulia, 1999). On the contrary, strong-tie relationships have a high level of intimacy, including more self-disclosure, provision of reciprocal services, and frequent exchanges (Granovetter, 1982; Haythornthwaite, 2002; Wellman & Gulia, 1999). Furthermore, Wellman and Wortley (1990) found that strongly tied pairs generally produced emotional support, companionship, and tangible services, whereas weakly tied pairs tended to produce only informational support. In the current study, we adapt Bambina (2007)’s measurement using frequency of contact and reciprocity to measure online social ties. Moreover, Bambina (2007) found that online social tie strength was positively related to the exchanges of emotional support, but negatively associated with informational support individuals received in online support groups. Thus, we propose the following hypotheses: H1: Dyads with a high level of frequency of contact will exchange a greater amount of emotional support than will dyads with a low level of frequency of contact. H2: Dyads with a high level of reciprocity will exchange a greater amount of emotional support than will dyads with a low level of reciprocity. H3: Dyads with a high frequency of contact will exchange a smaller amount of informational support than will dyads with a low frequency of contact. H4: Dyads with a high level of reciprocity will exchange a smaller amount of informational support than will dyads with a low level of reciprocity.
694
L. Chen and J. Shi
Method Data Messages over five successive weeks (2nd May to 13th June 2011) from the group were randomly selected and extracted using the Python Web Crawler. After that, we mapped three networks: the message exchange network, the emotional support exchange network, and the informational support exchange network1 by social network analysis using Ucinet (Borgatti, Everett, & Freeman, 2002). The unit of analysis is the dyad. Two individuals who exchange one or more messages were considered as a dyad. The message exchange network showed that 191 dyadic groups were emerged (n = 191). Three networks were visualized in Figures 1–3. Measures Frequency of contact It was a dichotomous variable with a high or low level according to the number of messages exchanged in each dyad. Here, the mean of 2.01 was the cut-off point. Therefore, the pairs who exchanged two or more messages were categorized as dyads with a high frequency of contact (n = 34), while the pairs who exchanged one message were defined as dyads with a low frequency of contact (n = 157).
Figure 1. Message exchange network in the HIV/AIDS Weibo Group from 2 May 2011 to 13 June 2011. Nodes represent group members (n = 89). The size of node depends on the sum of messages a group member sent out and received. The larger the node, the greater amount of messages the individual sent out and received. Lines represent messages exchanged between group members. The weight of line depends on the number of exchanged messages. The layout is drawn by Gephi (Bastian, Heymann, & Jacomy, 2009).
Figure 2. Informational support exchange network in the HIV/ AIDS Weibo Group from 2 May 2011 to 13 June 2011. Nodes represent group members (n = 89). The size of node depends on the sum of informational support messages a group member sent out and received. The larger the node, the greater amount of messages the individual sent out and received. Lines represent informational support messages exchanged between group members. The weight of line depends on the number of exchanged informational support messages. The layout is drawn by Gephi (Bastian et al., 2009).
Figure 3. Emotional support exchange network in the HIV/ AIDS Weibo Group from 2 May 2011 to 13 June 2011. Nodes represent group members (n = 89). The size of node depends on the sum of emotional support messages a group member sent out and received. The larger the node, the greater amount of messages the individual sent out and received. Lines represent emotional support messages exchanged between group members. The weight of line depends on the number of exchanged emotional support messages. The layout is drawn by Gephi (Bastian et al., 2009).
AIDS Care
695
Reciprocity
Discussion
It refers to the symmetric relationship between dyadic members. Reciprocity was a dichotomous variable with high and low levels. Adapted from Bambina (2007), when the number of messages sent from one dyadic member to the other was more than half of the number of messages that the other sent back to him or her, the dyad was considered as a dyad with a high level of reciprocity (n = 84). Otherwise, the dyad was defined as a dyad with a low level of reciprocity (n = 107).
By examining the online social support exchanges for PLWHA in China, our study makes some important theoretical and practical contributions. In terms of emotional support, pairs with a high frequency of contact exchange a greater amount of online emotional support than pairs with a low frequency of contact. It is consistent with previous studies in the online and offline contexts (e.g., Aldrich & Ruef, 2006; Bambina, 2007; Klein, 2004; Wellman & Wortley, 1990). Besides, we cannot find a significant relationship between reciprocity and the amount of emotional support exchanged within a pair. It means that compared to the dyadic partners with a low level of reciprocity, pairs with a high level of reciprocity do not significantly exchange more emotional support. It might be explained by the similarity among group members. Particularly, most participants in this group are living with HIV/AIDS, who share similar health concerns and psychological distress. Thus, they are willing to exchange emotional support to each other even they are in an unreciprocal relationship. With respect to informational support, pairs with a high level of contact frequency or reciprocity exchange a greater amount of online informational support than their counterparts, who with a low level of contact frequency or reciprocity in the group. In other words, pairs with online strong ties are more likely to exchange informational support than weakly tied pairs. This finding, however, is inconsistent with Granovetter’s (1973) strength of weak ties theory, which proposed that weak-tie relationships provide more information to each other than strong-tie relationships. The inconsistency indicates that the original weak ties theory, which is proposed and developed based on the offline context, may need to be refined in the online context. On the other hand, the inconsistency might be because of the specific disease, HIV/AIDS, we examined here. Since the stigmatization of HIV/AIDS in the Chinese society, group members are only intent to share information, advice, and personal experience with familiar partners. In terms of practical implications, public health practitioners could create some healthcare social media, like QuitNet for smoking cessation, for PLWHA to help them share emotional encouragement and information resources.
Emotional support It was measured by the number of emotional support messages2 exchanged in a dyadic group, which can be obtained from the emotional support exchange network (M = 0.53, SD = 0.91). Informational support It was measured by the number of informational support messages3 exchanged in a dyadic group, which can be obtained from the informational support exchange network (M = 0.77, SD = 0.95). Results A 2 × 2 (frequency of contact [high, low] × reciprocity [high, low]) between-subject multivariate analysis of variance was preformed to test the hypotheses. The results showed that there were significant main effects of frequency of contact and reciprocity, but no significant interaction effects. For emotional support, dyads with a high frequency of contact exchange a greater amount of emotional support (M = 1.35, SD = 1.52) than did dyads with a low frequency of contact (M = 0.36, SD = 0.59), F (1, 187) = 33.58, p < .001, g2p = .15. It was consistent with H1. The reciprocity main effect on emotional support, however, was not significant, F (1, 187) = 2.49, p = .12, which means that dyads with a high level of reciprocity did not significantly exchange a larger amount of emotional support (M = 0.80, SD = 1.21) than did dyads with a low level of reciprocity (M = 0.38, SD = 0.64). Thus, the data were inconsistent with H2. For informational support, dyads with a high frequency of contact exchanged a greater amount of informational support (M = 1.82, SD = 1.38) than did dyads with a low level of frequency of contact (M = 0.55, SD = 0.64), F (1, 187) = 37.02, p < .001, g2p = .24. The results were contrary to the prediction in H3. Moreover, dyads with a high level of reciprocity significantly exchanged more informational support (M = 1.09, SD = 1.26) than did dyads with a low level of reciprocity (M = 0.60, SD = 0.65), F (1, 187) = 4.97, p = .006, g2p = .04. The data were inconsistent with H4.
Limitations First, the current study does not explore the effectiveness of social support, which could be investigated through interviews in the future. Second, while the frequency of contact and the reciprocity are conceptually different, the measures of these two variables are both based on the
696
L. Chen and J. Shi
concept of degrees in social network analysis. Nonetheless, it is a common weakness of social network research. For example, many social network studies (e.g., Bambina, 2007; Cho & Lee, 2008) operationalize conceptually different variables only through the various transformations of degree in the social network analysis.
Notes 1. Two authors have done a study which content analyzed all the messages of the HIV/AIDS Weibo Group from 18 January 2011 to 14 September 2012. The results showed that plenty of informational and emotional support and limited instrumental support existed in the HIV/AIDS Weibo Group. The current study randomly selected messages over five successive weeks to investigate the mechanism of social support exchanges within this social media community. Given that the number of instrumental support messages is too small for creating a network, only emotional and informational exchange networks are built and examined in the current study. For the readers who are interesting in the coding scheme and procedure of the supportive messages, please refer to Anonymous (2014). 2. Examples for emotional support messages: “Come on! We should look after ourselves”; “Don’t worry. You should have courage and faith.” 3. Examples for informational support messages: “Your CD4 cell count is below 350, so you should take medicines as early as possible to live longer”; “I just did a CD4 test several days ago. My CD4 count was 897.”
References Aldrich, H. E., & Ruef, M. (2006). Organizations evolving (2nd ed.). London: Sage. Anonymous. (2014). Social support on Weibo for people living with HIV/AIDS in China: A quantitative content analysis. Chinese Journal of Communication, 7, 285–298. doi:10. 1080/17544750.2014.926954 Bambina, A. (2007). Online social support: The interplay of social networks and computer mediated communication. New York: Cambria Press. Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: An open source software for exploring and manipulating networks. International AAAI Conference on Weblogs and Social Media, San Jose, CA. Brashers, D. E., Neidig, J. L., & Goldsmith, D. J. (2004). Social support and the management of uncertainty for people living with HIV or AIDS. Health Communication, 16, 305–331. doi:10.1207/S15327027HC1603_3 Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). Ucinet for Windows: Software for social network analysis. Harvard, MA: Analytic Technologies.
Burleson, B. R., Albrecht, T. L., Goldsmith, D. J., & Sarason, I. G. (1994). Introduction. In B. R. Burleson & T. L. Albrecht (Eds.), Communication of social support (pp. xi–xxx). Thousand Oaks, CA: Sage. Cho, H., & Lee, J. S. (2008). Collaborative information seeking in inter-cultural CMC groups: Testing the influence of social context using social network analysis. Communication Research, 35, 548–573. doi:10.1177/0093650208315982 Cutrona, C. E., Suhr, J. A., & MacFarlane, R. (1990). Interpersonal transactions and the psychological sense of support. In S. Duck (Ed.), Personal relationships and social support (pp. 30–45). London: Sage. Friedkin, N. (1980). A test of the structural features of Granovetter’s strength of weak ties theory. Social Networks, 2, 411–422. doi:10.1016/0378-8733(80)90006-4 Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78, 1360–1380. doi:10.1086/225469 Granovetter, M. S. (1982). The strength of weak ties: A network theory revisited. In P. V. Marsden & N. Lin (Eds.), Social structure and network analysis (pp. 105–130). Beverly Hills, CA: Sage. Haythornthwaite, C. (2002). Strong, weak and latent ties and the impact of new media. The Information Society, 18, 385–401. doi:10.1080/01972240290108195 Klein, A. (2004). Social support quality the Internet based information and communication: From digital divide to voice divide. Social Work & Society, 2, 71–82. Lin, N., Dayton, P., & Greenwald, P. (1978). The urban communication network and social stratification: A small world experiment. In B. D. Ruben (Ed.), Communication Yearbook (pp. 107–119). New Brunswick: Transaction Books. Sibanda, O. (2010). Social ties and the dynamics of integration in the city of Johannesburg among Zimbabwe migrants. Journal of Sociology and Social Anthropology, 1, 47–57. Stefanone, M. A., Kwon, K., & Lackaff, D. (2011, February). The value of online friends: Networked resources via social network sites. First Monday, p. 16. Retrieved from http://firstmonday.org/ojs/index.php/fm/article/view/ 3314/2763 Wellman, B., & Gulia, M. (1999). Net Surfers don’t ride alone: Virtual communities as communities. In B. Wellman (Ed.), Networks in the global village (pp. 331–366). Boulder, CO: Westview Press. Wellman, B., & Wortley, S. (1990). Different strokes from different folks. American Journal of Sociology, 96, 558– 588. doi:10.1086/229572 Yang, C. (2014, March). Healthcare informatics and big data. In C. Salmon (Chair), Big data, big ideas for smarter communities. Symposium conducted at the meeting of the Wee Kim Wee School of Communication and Information, Singapore.
Copyright of AIDS Care is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Social support exchanges in a social media community for people living with HIV/AIDS in China Liang Chen* and Jingyuan Shi Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore (Received 15 April 2014; accepted 19 November 2014) In recent years, social media has become an important source of social support. People living with HIV/AIDS in China created an online support group (the HIV/AIDS Weibo Group) on Weibo, the Chinese version of Twitter, in January 2011. The current study examined how social support transmitted in this social media community. First, messages over five successive weeks (2 May 2011 to 13 June 2011) were randomly selected from the HIV/AIDS Weibo Group on Weibo. Next, we employed social network analysis to map the HIV/AIDS Weibo Group’s structure and to measure the study variables. After that, a multivariate analysis of variance was applied to examine the influence of frequency of contact and reciprocity on informational and emotional social support exchanged in each dyad. The results revealed that pairs with a high level of contact frequency or reciprocity exchanged more informational support than do pairs with a low level of contact frequency or reciprocity. Moreover, dyadic partners with high frequency of contact exchanged a larger amount of emotional support than those with a low level frequency of contact; but strongly reciprocal dyads did not exchange significantly more emotional social support than their counterparts with a low level of reciprocity. Keywords: social media; social network analysis; social support; social ties; China
With the advance of web 2.0 technologies, social media have empowered patients to connect with others online to exchange social support (Yang, 2014). People living with HIV/AIDS (PLWHA) need help from supportive others to manage their uncertainty about health, identity, and social relationship (Brashers, Neigid, & Goldsmith, 2004). In 2011, PLWHA in China created a social media community, named the HIV/AIDS Weibo Group (http://q.weibo. com/267539), on Weibo (the Chinese Twitter equivalent). It provides the same microblogging services as Twitter, but Weibo users also created interest groups on it. Social support refers to “an interpersonal transaction involving one or more of the following: emotional concerns, instrumental aid, information, or appraisal” (Cutrona, Suhr, & MacFarlane, 1990, p. 30). Normally, social support is transmitted through social ties to achieve its health outcomes (Burleson, Albrecht, Goldsmith, & Sarason, 1994). Social ties are defined as the relationships built based on resource exchanges, such as information, services, and social support (Haythornthwaite, 2002; Sibanda, 2010). Three main dimensions: duration of relationship (Stefanone, Kwon, & Lackaff, 2011), frequency of contact (Lin, Dayton, & Greenwald, 1978), and reciprocity (Friedkin, 1980) have been employed to measure the strength of ties. In general, individuals who maintain weak ties engage in non-intimate, infrequent, and more casual exchanges (Granovetter, 1982; Haythornthwaite, 2002; *Corresponding author. Email: [email protected] © 2014 Taylor & Francis
Wellman & Gulia, 1999). On the contrary, strong-tie relationships have a high level of intimacy, including more self-disclosure, provision of reciprocal services, and frequent exchanges (Granovetter, 1982; Haythornthwaite, 2002; Wellman & Gulia, 1999). Furthermore, Wellman and Wortley (1990) found that strongly tied pairs generally produced emotional support, companionship, and tangible services, whereas weakly tied pairs tended to produce only informational support. In the current study, we adapt Bambina (2007)’s measurement using frequency of contact and reciprocity to measure online social ties. Moreover, Bambina (2007) found that online social tie strength was positively related to the exchanges of emotional support, but negatively associated with informational support individuals received in online support groups. Thus, we propose the following hypotheses: H1: Dyads with a high level of frequency of contact will exchange a greater amount of emotional support than will dyads with a low level of frequency of contact. H2: Dyads with a high level of reciprocity will exchange a greater amount of emotional support than will dyads with a low level of reciprocity. H3: Dyads with a high frequency of contact will exchange a smaller amount of informational support than will dyads with a low frequency of contact. H4: Dyads with a high level of reciprocity will exchange a smaller amount of informational support than will dyads with a low level of reciprocity.
694
L. Chen and J. Shi
Method Data Messages over five successive weeks (2nd May to 13th June 2011) from the group were randomly selected and extracted using the Python Web Crawler. After that, we mapped three networks: the message exchange network, the emotional support exchange network, and the informational support exchange network1 by social network analysis using Ucinet (Borgatti, Everett, & Freeman, 2002). The unit of analysis is the dyad. Two individuals who exchange one or more messages were considered as a dyad. The message exchange network showed that 191 dyadic groups were emerged (n = 191). Three networks were visualized in Figures 1–3. Measures Frequency of contact It was a dichotomous variable with a high or low level according to the number of messages exchanged in each dyad. Here, the mean of 2.01 was the cut-off point. Therefore, the pairs who exchanged two or more messages were categorized as dyads with a high frequency of contact (n = 34), while the pairs who exchanged one message were defined as dyads with a low frequency of contact (n = 157).
Figure 1. Message exchange network in the HIV/AIDS Weibo Group from 2 May 2011 to 13 June 2011. Nodes represent group members (n = 89). The size of node depends on the sum of messages a group member sent out and received. The larger the node, the greater amount of messages the individual sent out and received. Lines represent messages exchanged between group members. The weight of line depends on the number of exchanged messages. The layout is drawn by Gephi (Bastian, Heymann, & Jacomy, 2009).
Figure 2. Informational support exchange network in the HIV/ AIDS Weibo Group from 2 May 2011 to 13 June 2011. Nodes represent group members (n = 89). The size of node depends on the sum of informational support messages a group member sent out and received. The larger the node, the greater amount of messages the individual sent out and received. Lines represent informational support messages exchanged between group members. The weight of line depends on the number of exchanged informational support messages. The layout is drawn by Gephi (Bastian et al., 2009).
Figure 3. Emotional support exchange network in the HIV/ AIDS Weibo Group from 2 May 2011 to 13 June 2011. Nodes represent group members (n = 89). The size of node depends on the sum of emotional support messages a group member sent out and received. The larger the node, the greater amount of messages the individual sent out and received. Lines represent emotional support messages exchanged between group members. The weight of line depends on the number of exchanged emotional support messages. The layout is drawn by Gephi (Bastian et al., 2009).
AIDS Care
695
Reciprocity
Discussion
It refers to the symmetric relationship between dyadic members. Reciprocity was a dichotomous variable with high and low levels. Adapted from Bambina (2007), when the number of messages sent from one dyadic member to the other was more than half of the number of messages that the other sent back to him or her, the dyad was considered as a dyad with a high level of reciprocity (n = 84). Otherwise, the dyad was defined as a dyad with a low level of reciprocity (n = 107).
By examining the online social support exchanges for PLWHA in China, our study makes some important theoretical and practical contributions. In terms of emotional support, pairs with a high frequency of contact exchange a greater amount of online emotional support than pairs with a low frequency of contact. It is consistent with previous studies in the online and offline contexts (e.g., Aldrich & Ruef, 2006; Bambina, 2007; Klein, 2004; Wellman & Wortley, 1990). Besides, we cannot find a significant relationship between reciprocity and the amount of emotional support exchanged within a pair. It means that compared to the dyadic partners with a low level of reciprocity, pairs with a high level of reciprocity do not significantly exchange more emotional support. It might be explained by the similarity among group members. Particularly, most participants in this group are living with HIV/AIDS, who share similar health concerns and psychological distress. Thus, they are willing to exchange emotional support to each other even they are in an unreciprocal relationship. With respect to informational support, pairs with a high level of contact frequency or reciprocity exchange a greater amount of online informational support than their counterparts, who with a low level of contact frequency or reciprocity in the group. In other words, pairs with online strong ties are more likely to exchange informational support than weakly tied pairs. This finding, however, is inconsistent with Granovetter’s (1973) strength of weak ties theory, which proposed that weak-tie relationships provide more information to each other than strong-tie relationships. The inconsistency indicates that the original weak ties theory, which is proposed and developed based on the offline context, may need to be refined in the online context. On the other hand, the inconsistency might be because of the specific disease, HIV/AIDS, we examined here. Since the stigmatization of HIV/AIDS in the Chinese society, group members are only intent to share information, advice, and personal experience with familiar partners. In terms of practical implications, public health practitioners could create some healthcare social media, like QuitNet for smoking cessation, for PLWHA to help them share emotional encouragement and information resources.
Emotional support It was measured by the number of emotional support messages2 exchanged in a dyadic group, which can be obtained from the emotional support exchange network (M = 0.53, SD = 0.91). Informational support It was measured by the number of informational support messages3 exchanged in a dyadic group, which can be obtained from the informational support exchange network (M = 0.77, SD = 0.95). Results A 2 × 2 (frequency of contact [high, low] × reciprocity [high, low]) between-subject multivariate analysis of variance was preformed to test the hypotheses. The results showed that there were significant main effects of frequency of contact and reciprocity, but no significant interaction effects. For emotional support, dyads with a high frequency of contact exchange a greater amount of emotional support (M = 1.35, SD = 1.52) than did dyads with a low frequency of contact (M = 0.36, SD = 0.59), F (1, 187) = 33.58, p < .001, g2p = .15. It was consistent with H1. The reciprocity main effect on emotional support, however, was not significant, F (1, 187) = 2.49, p = .12, which means that dyads with a high level of reciprocity did not significantly exchange a larger amount of emotional support (M = 0.80, SD = 1.21) than did dyads with a low level of reciprocity (M = 0.38, SD = 0.64). Thus, the data were inconsistent with H2. For informational support, dyads with a high frequency of contact exchanged a greater amount of informational support (M = 1.82, SD = 1.38) than did dyads with a low level of frequency of contact (M = 0.55, SD = 0.64), F (1, 187) = 37.02, p < .001, g2p = .24. The results were contrary to the prediction in H3. Moreover, dyads with a high level of reciprocity significantly exchanged more informational support (M = 1.09, SD = 1.26) than did dyads with a low level of reciprocity (M = 0.60, SD = 0.65), F (1, 187) = 4.97, p = .006, g2p = .04. The data were inconsistent with H4.
Limitations First, the current study does not explore the effectiveness of social support, which could be investigated through interviews in the future. Second, while the frequency of contact and the reciprocity are conceptually different, the measures of these two variables are both based on the
696
L. Chen and J. Shi
concept of degrees in social network analysis. Nonetheless, it is a common weakness of social network research. For example, many social network studies (e.g., Bambina, 2007; Cho & Lee, 2008) operationalize conceptually different variables only through the various transformations of degree in the social network analysis.
Notes 1. Two authors have done a study which content analyzed all the messages of the HIV/AIDS Weibo Group from 18 January 2011 to 14 September 2012. The results showed that plenty of informational and emotional support and limited instrumental support existed in the HIV/AIDS Weibo Group. The current study randomly selected messages over five successive weeks to investigate the mechanism of social support exchanges within this social media community. Given that the number of instrumental support messages is too small for creating a network, only emotional and informational exchange networks are built and examined in the current study. For the readers who are interesting in the coding scheme and procedure of the supportive messages, please refer to Anonymous (2014). 2. Examples for emotional support messages: “Come on! We should look after ourselves”; “Don’t worry. You should have courage and faith.” 3. Examples for informational support messages: “Your CD4 cell count is below 350, so you should take medicines as early as possible to live longer”; “I just did a CD4 test several days ago. My CD4 count was 897.”
References Aldrich, H. E., & Ruef, M. (2006). Organizations evolving (2nd ed.). London: Sage. Anonymous. (2014). Social support on Weibo for people living with HIV/AIDS in China: A quantitative content analysis. Chinese Journal of Communication, 7, 285–298. doi:10. 1080/17544750.2014.926954 Bambina, A. (2007). Online social support: The interplay of social networks and computer mediated communication. New York: Cambria Press. Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: An open source software for exploring and manipulating networks. International AAAI Conference on Weblogs and Social Media, San Jose, CA. Brashers, D. E., Neidig, J. L., & Goldsmith, D. J. (2004). Social support and the management of uncertainty for people living with HIV or AIDS. Health Communication, 16, 305–331. doi:10.1207/S15327027HC1603_3 Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). Ucinet for Windows: Software for social network analysis. Harvard, MA: Analytic Technologies.
Burleson, B. R., Albrecht, T. L., Goldsmith, D. J., & Sarason, I. G. (1994). Introduction. In B. R. Burleson & T. L. Albrecht (Eds.), Communication of social support (pp. xi–xxx). Thousand Oaks, CA: Sage. Cho, H., & Lee, J. S. (2008). Collaborative information seeking in inter-cultural CMC groups: Testing the influence of social context using social network analysis. Communication Research, 35, 548–573. doi:10.1177/0093650208315982 Cutrona, C. E., Suhr, J. A., & MacFarlane, R. (1990). Interpersonal transactions and the psychological sense of support. In S. Duck (Ed.), Personal relationships and social support (pp. 30–45). London: Sage. Friedkin, N. (1980). A test of the structural features of Granovetter’s strength of weak ties theory. Social Networks, 2, 411–422. doi:10.1016/0378-8733(80)90006-4 Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78, 1360–1380. doi:10.1086/225469 Granovetter, M. S. (1982). The strength of weak ties: A network theory revisited. In P. V. Marsden & N. Lin (Eds.), Social structure and network analysis (pp. 105–130). Beverly Hills, CA: Sage. Haythornthwaite, C. (2002). Strong, weak and latent ties and the impact of new media. The Information Society, 18, 385–401. doi:10.1080/01972240290108195 Klein, A. (2004). Social support quality the Internet based information and communication: From digital divide to voice divide. Social Work & Society, 2, 71–82. Lin, N., Dayton, P., & Greenwald, P. (1978). The urban communication network and social stratification: A small world experiment. In B. D. Ruben (Ed.), Communication Yearbook (pp. 107–119). New Brunswick: Transaction Books. Sibanda, O. (2010). Social ties and the dynamics of integration in the city of Johannesburg among Zimbabwe migrants. Journal of Sociology and Social Anthropology, 1, 47–57. Stefanone, M. A., Kwon, K., & Lackaff, D. (2011, February). The value of online friends: Networked resources via social network sites. First Monday, p. 16. Retrieved from http://firstmonday.org/ojs/index.php/fm/article/view/ 3314/2763 Wellman, B., & Gulia, M. (1999). Net Surfers don’t ride alone: Virtual communities as communities. In B. Wellman (Ed.), Networks in the global village (pp. 331–366). Boulder, CO: Westview Press. Wellman, B., & Wortley, S. (1990). Different strokes from different folks. American Journal of Sociology, 96, 558– 588. doi:10.1086/229572 Yang, C. (2014, March). Healthcare informatics and big data. In C. Salmon (Chair), Big data, big ideas for smarter communities. Symposium conducted at the meeting of the Wee Kim Wee School of Communication and Information, Singapore.
Copyright of AIDS Care is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
