Abstract
Nowadays people have many overlapping relationships across different social network sites. Will the communication frequency of a relationship on a focal SNS increase or decrease after the corresponding parties begin interacting with each other on a new SNS? What kinds of relationships and parties on the focal SNS are more robust to relationship overlapping across different sites? We conducted a case study on two Chinese popular social network sites (Weibo and Douban). Our results indicate that relationships' interactions on a new SNS have negative effect on the parties' communication frequency on the focal SNS. The communication frequency of older relationships on the focal SNS is more susceptible to the influence of the interacting on the new SNS, while relationships with more common groups and more extensive posts are less likely to be influenced. Our findings imply opportunities for SNS designers to strengthen their existing fragile ties and help users to build much more robust relationships.
- Bert N Adams.1967. Interaction theory and the social network. Sociometry (1967), 64--78.Google Scholar
- Joshua D Angrist and Alan B Krueger.1999. Empirical strategies in labor economics. Handbook of Labor Economics Vol. 3 (1999), 1277--1366.Google ScholarCross Ref
- Jaime Arguello, Brian S Butler, Elisabeth Joyce, Robert Kraut, Kimberly S Ling, Carolyn Rosé, and Xiaoqing Wang.2006. Talk to me: Foundations for successful individual-group interactions in online communities. In Proceedings of the 2006 SIGCHI Conference on Human Factors in Computing Systems. ACM, 959--968. Google ScholarDigital Library
- Alan P Bates and Nicholas Babchuk.1961. The primary group: A reappraisal. The Sociological Quarterly Vol. 2, 3 (1961), 181--191.Google ScholarCross Ref
- Nancy K Baym, Yan Bing Zhang, and Mei-Chen Lin.2004. Social interactions across media: Interpersonal communication on the Internet, telephone and face-to-face. New Media & Society, Vol. 6, 3 (2004), 299--318.Google ScholarCross Ref
- Jeffrey Boase, John B Horrigan, Lee Rainie, and Barry Wellman.2006. The strength of Internet ties. Pew Internet & American Life Project Vol. 253, 1--2 (2006).Google Scholar
- Petter Bae Brandtzæg.2010. Towards a unified Media-User Typology (MUT): A meta-analysis and review of the research literature on media-user typologies. Computers in Human Behavior Vol. 26, 5 (2010), 940--956. Google ScholarDigital Library
- Rupert Brown.2000. Social identity theory: Past achievements, current problems and future challenges. European Journal of Social Psychology Vol. 30, 6 (2000), 745--778.Google ScholarCross Ref
- Moira Burke and Robert Kraut.2013. Using Facebook after losing a job: Differential benefits of strong and weak ties Proceedings of the 2013 ACM Conference on Computer Supported Cooperative Work. ACM, 1419--1430. Google ScholarDigital Library
- Moira Burke and Robert E Kraut.2014. Growing closer on facebook: Changes in tie strength through social network site use Proceedings of the 2014 SIGCHI Conference on Human Factors in Computing Systems. ACM, 4187--4196. Google ScholarDigital Library
- Shu-Chuan Chu and Yoojung Kim.2011. Determinants of consumer engagement in electronic word-of-mouth (eWOM) in social networking sites. International Journal of Advertising Vol. 30, 1 (2011), 47--75.Google ScholarCross Ref
- CINIC.2016. Statistical report on internet development in China. (2016). Retrieved August 3, 2016 from http://www.cnnic.net.cn/gywm/xwzx/rdxw/2016/201608/t20160803_54389.htmGoogle Scholar
- Yi Cui, Jian Pei, Guanting Tang, Wo-Shun Luk, Daxin Jiang, and Ming Hua.2013. Finding email correspondents in online social networks. World Wide Web, Vol. 16, 2 (2013), 195--218. Google ScholarDigital Library
- Nicole B Ellison, Charles Steinfield, and Cliff Lampe.2007. The benefits of Facebook "friends:" Social capital and college students' use of online social network sites. Journal of Computer-Mediated Communication Vol. 12, 4 (2007), 1143--1168.Google ScholarCross Ref
- Beverley Fehr.1988. Prototype analysis of the concepts of love and commitment. Journal of Personality and Social Psychology, Vol. 55, 4 (1988), 557.Google ScholarCross Ref
- Ming Gao, Ee-Peng Lim, David Lo, Feida Zhu, Philips Kokoh Prasetyo, and Aoying Zhou.2015. CNL: Collective network linkage across heterogeneous social platforms 2015 IEEE International Conference on Data Mining (ICDM). IEEE, 757--762. Google ScholarDigital Library
- Eric Gilbert.2012. Predicting tie strength in a new medium. In Proceedings of the 2012 ACM Conference on Computer Supported Cooperative Work. ACM, 1047--1056. Google ScholarDigital Library
- Eric Gilbert and Karrie Karahalios.2009. Predicting tie strength with social media. In Proceedings of the 2009 SIGCHI Conference on Human Factors in Computing Systems. ACM, 211--220. Google ScholarDigital Library
- Oana Goga, Patrick Loiseau, Robin Sommer, Renata Teixeira, and Krishna P Gummadi.2015. On the reliability of profile matching across large online social networks Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1799--1808. Google ScholarDigital Library
- Scott A Golder, Dennis M Wilkinson, and Bernardo A Huberman.2007. Rhythms of social interaction: Messaging within a massive online network. Communities and Technologies 2007 (2007), 41--66.Google Scholar
- Przemyslaw A Grabowicz, Luca Maria Aiello, Víctor M Eguíluz, and Alejandro Jaimes. 2013. Distinguishing topical and social groups based on common identity and bond theory Proceedings of the Sixth ACM International Conference on Web Search and Data Mining. ACM, 627--636. Google ScholarDigital Library
- Caroline Haythornthwaite.2002. Strong, weak, and latent ties and the impact of new media. The Information Society Vol. 18, 5 (2002), 385--401.Google ScholarCross Ref
- Caroline Haythornthwaite.2005. Social networks and Internet connectivity effects. Information, Community & Society Vol. 8, 2 (2005), 125--147.Google ScholarCross Ref
- George C Homans.2013. The human group. Vol. Vol. 7. Routledge.Google Scholar
- Adam N Joinson.2008. Looking at, looking up or keeping up with people?: Motives and use of facebook Proceedings of the 2008 SIGCHI Conference on Human Factors in Computing Systems. ACM, 1027--1036. Google ScholarDigital Library
- Andreas M Kaplan and Michael Haenlein.2010. Users of the world, unite! The challenges and opportunities of social media. Business Horizons, Vol. 53, 1 (2010), 59--68.Google ScholarCross Ref
- Juuso Karikoski and Matti Nelimarkka.2010. Measuring social relations: Case otasizzle. In 2010 IEEE Second International Conference on Social Computing (SocialCom). IEEE, 257--263. Google ScholarDigital Library
- Jan H Kietzmann, Kristopher Hermkens, Ian P McCarthy, and Bruno S Silvestre.2011. Social media? Get serious! Understanding the functional building blocks of social media. Business Horizons, Vol. 54, 3 (2011), 241--251.Google ScholarCross Ref
- Xiangnan Kong, Jiawei Zhang, and Philip S Yu.2013. Inferring anchor links across multiple heterogeneous social networks Proceedings of the 22nd ACM International Conference on Information & Knowledge Management. ACM, 179--188. Google ScholarDigital Library
- Robert Kraut, Michael Patterson, Vicki Lundmark, Sara Kiesler, Tridas Mukophadhyay, and William Scherlis.1998. Internet paradox: A social technology that reduces social involvement and psychological well-being? American Psychologist Vol. 53, 9 (1998), 1017--1031.Google ScholarCross Ref
- Robert E Kraut, Paul Resnick, Sara Kiesler, Moira Burke, Yan Chen, Niki Kittur, Joseph Konstan, Yuqing Ren, and John Riedl.2012. Building successful online communities: Evidence-based social design. Mit Press. Google ScholarDigital Library
- Brett Laursen and Willard W Hartup.2002. The origins of reciprocity and social exchange in friendships. New Directions for Child and Adolescent Development, Vol. 2002, 95 (2002), 27--40.Google ScholarCross Ref
- Siyuan Liu, Shuhui Wang, Feida Zhu, Jinbo Zhang, and Ramayya Krishnan.2014. Hydra: Large-scale social identity linkage via heterogeneous behavior modeling Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data. ACM, 51--62. Google ScholarDigital Library
- Ido Liviatan, Yaacov Trope, and Nira Liberman.2008. Interpersonal similarity as a social distance dimension: Implications for perception of others's actions. Journal of Experimental Social Psychology Vol. 44, 5 (2008), 1256--1269.Google ScholarCross Ref
- Chun-Ta Lu, Hong-Han Shuai, and Philip S Yu.2014. Identifying your customers in social networks. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. ACM, 391--400. Google ScholarDigital Library
- Anshu Malhotra, Luam Totti, Wagner Meira Jr, Ponnurangam Kumaraguru, and Virgilio Almeida.2012. Studying user footprints in different online social networks 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 1065--1070. Google ScholarDigital Library
- Michael D Miller and C Cryss Brunner.2008. Social impact in technologically-mediated communication: An examination of online influence. Computers in Human Behavior Vol. 24, 6 (2008), 2972--2991. Google ScholarDigital Library
- Jun-Ki Min, Jason Wiese, Jason I Hong, and John Zimmerman.2013. Mining smartphone data to classify life-facets of social relationships Proceedings of the 2013 ACM Conference on Computer Supported Cooperative Work. ACM, 285--294. Google ScholarDigital Library
- Xin Mu, Feida Zhu, Ee-Peng Lim, Jing Xiao, Jianzong Wang, and Zhi-Hua Zhou.2016. User identity linkage by latent user space modelling Proceedings of 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1775--1784. Google ScholarDigital Library
- Gwenn Schurgin O'Keeffe, Kathleen Clarke-Pearson, et almbox..2011. The impact of social media on children, adolescents, and families. Pediatrics, Vol. 127, 4 (2011), 800--804.Google ScholarCross Ref
- Pietro Panzarasa, Tore Opsahl, and Kathleen M Carley.2009. Patterns and dynamics of users' behavior and interaction: Network analysis of an online community. Journal of the American Society for Information Science and Technology, Vol. 60, 5 (2009), 911--932. Google ScholarDigital Library
- Sarah Perez.2009. Who uses social networks and what are they like? (Part 1). (2009). Retrieved December 31, 2009 from https://readwrite.com/2009/12/31/who_uses_social_networks_and_what_are_they_like_pa/Google Scholar
- Yuqing Ren, Robert Kraut, and Sara Kiesler.2007. Applying common identity and bond theory to design of online communities. Organization Studies, Vol. 28, 3 (2007), 377--408.Google ScholarCross Ref
- Mark T Rivera, Sara B Soderstrom, and Brian Uzzi.2010. Dynamics of dyads in social networks: Assortative, relational, and proximity mechanisms. Annual Review of Sociology Vol. 36 (2010), 91--115.Google ScholarCross Ref
- Bartunov Sergey, Park Seung-Taek, Ryu Wonho, and Lee Hyungdong.2012. Joint link-attribute user identity resolution in online social networks Proceedings of the 6th International Conference on Knowledge Discovery and Data Mining, Workshop on Social Network Mining and Analysis. ACM.Google Scholar
- Manya Sleeper, William Melicher, Hana Habib, Lujo Bauer, Lorrie Faith Cranor, and Michelle L Mazurek.2016. Sharing personal content online: Exploring channel choice and multi-channel behaviors Proceedings of the 2016 SIGCHI Conference on Human Factors in Computing Systems. ACM, 101--112. Google ScholarDigital Library
- Lei Tang and Huan Liu.2010. Community detection and mining in social media. Synthesis Lectures on Data Mining and Knowledge Discovery, Vol. 2, 1 (2010), 1--137. Google ScholarDigital Library
- Bimal Viswanath, Alan Mislove, Meeyoung Cha, and Krishna P Gummadi.2009. On the evolution of user interaction in Facebook. Proceedings of the 2nd ACM Workshop on Online Social Networks. ACM, 37--42. Google ScholarDigital Library
- Jessica Vitak.2014. Facebook makes the heart grow fonder: Relationship maintenance strategies among geographically dispersed and communication-restricted connections Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing. ACM, 842--853. Google ScholarDigital Library
- Xiaoqing Wang, Brian S Butler, and Yuqing Ren.2013. The impact of membership overlap on growth: An ecological competition view of online groups. Organization Science, Vol. 24, 2 (2013), 414--431. Google ScholarDigital Library
- Mary Beth Watson-Manheim and France Bélanger.2007. Communication media repertoires: Dealing with the multiplicity of media choices. MIS Quarterly (2007), 267--293. Google ScholarDigital Library
- Donghee Yvette Wohn and Wei Peng.2015. Understanding perceived social support through communication time, frequency, and media multiplexity. In Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems. ACM, 1911--1916. Google ScholarDigital Library
- Zhi Yang, Christo Wilson, Ben Zhao, Yafei Dai, et almbox..2015. Uncovering user interaction dynamics in online social networks Proceedings of the Ninth International AAAI Conference on Web and Social Media. 698--701.Google Scholar
- Reza Zafarani and Huan Liu.2013. Connecting users across social media sites: A behavioral-modeling approach Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 41--49. Google ScholarDigital Library
- Reza Zafarani, Lei Tang, and Huan Liu.2015. User identification across social media. ACM Transactions on Knowledge Discovery from Data (TKDD), Vol. 10, 2 (2015), 16. Google ScholarDigital Library
- Yutao Zhang, Jie Tang, Zhilin Yang, Jian Pei, and Philip S Yu.2015. Cosnet: Connecting heterogeneous social networks with local and global consistency Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1485--1494. Google ScholarDigital Library
- Xuan Zhao, Cliff Lampe, and Nicole B Ellison.2016. The social media ecology: User perceptions, strategies and challenges Proceedings of the 2016 SIGCHI Conference on Human Factors in Computing Systems. ACM, 89--100. Google ScholarDigital Library
- Haiyi Zhu, Robert E Kraut, and Aniket Kittur.2014. The impact of membership overlap on the survival of online communities Proceedings of the 2014 SIGCHI Conference on Human Factors in Computing Systems. ACM, 281--290. Google ScholarDigital Library
Index Terms
- Understanding Relationship Overlapping on Social Network Sites: A Case Study of Weibo and Douban
Recommendations
Identifying User Identity across Social Network Sites based on Overlapping Relationship and Social Interaction
ChineseCSCW '17: Proceedings of the 12th Chinese Conference on Computer Supported Cooperative Work and Social ComputingMost people have identities on multiple social network sites (SNSs) simultaneously to meet their diverse needs for social media use. User identity identification across SNSs has been a significant research focus in recent years as it is important for ...
How Do You Interact with Your Old Friends on a New Site: Understanding Social Ties among Different Social Network Sites
ChineseCSCW '17: Proceedings of the 12th Chinese Conference on Computer Supported Cooperative Work and Social ComputingUser footprints analysis across different SNSs have become emerging topics in research areas of social network analysis and social media mining. However, the features leveraged for user footprints analysis always have weak discriminability, high ...
Cultivating Social Resources on Social Network Sites: Facebook Relationship Maintenance Behaviors and Their Role in Social Capital Processes
This study explores the relationship between perceived bridging social capital and specific Facebook-enabled communication behaviors using survey data from a sample of U.S. adults N=614. We explore the role of a specific set of Facebook behaviors that ...
Comments