Abstract
Individuals’ access to information in a social network depends on how it is distributed and where in the network individuals position themselves. In addition, individuals vary in how much effort they invest in managing their social connections. Using data from a social media site, we study how the interplay between effort and network position affects social media users’ access to diverse and novel information. Previous studies of the role of networks in information access were limited in their ability to measure the diversity of information. We address this problem by learning the topics of interest to social media users from the messages they share online with followers. We use the learned topics to measure the diversity of information users receive from the people they follow online. We confirm that users in structurally diverse network positions, which bridge otherwise disconnected regions of the follower network, tend to be exposed to more diverse and novel information. We also show that users who invest more effort in their activity on the site are not only located in more structurally diverse positions within the network than the less engaged users but also receive more novel and diverse information when in similar network positions. These findings indicate that the relationship between network structure and access to information in networks is more nuanced than previously thought.
- Rakesh Agrawal, Sreenivas Gollapudi, Alan Halverson, and Samuel Ieong. 2009. Diversifying search results. In Proceedings of the 2nd ACM International Conference on Web Search and Data Mining. ACM, 5--14. Google Scholar
Digital Library
- Sinan Aral and Vincent David. 2012. The anatomy 8 dynamics of vision advantages. In Proceedings of the 33rd International Conference on Information Systems. Google Scholar
Cross Ref
- Sinan Aral and Marshall W. Van Alstyne. 2011. The diversity-bandwidth tradeoff. Am. J. Sociol. 117, 1 (22 Jan. 2011), 90--171. Google Scholar
Cross Ref
- Eytan Bakshy, Itamar Rosenn, Cameron Marlow, and Lada Adamic. 2012. The role of social networks in information diffusion. In Proceedings of International Conference on the World Wide Web (WWW’12). Google Scholar
Digital Library
- David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. J. Mach. Learn. Res. 3, Jan (2003), 993--1022.Google Scholar
- Ronald S. Burt. 1995. Structural Holes: The Social Structure of Competition. Harvard University Press, Cambridge, MA.Google Scholar
- Ronald S. Burt. 2004. Structural holes and good ideas. Am. J. Sociol. 110, 2 (2004), 349--399. Google Scholar
Cross Ref
- Ronald S. Burt. 2005. Brokerage and Closure: An Introduction to Social Capital. Oxford University Press.Google Scholar
- Damon Centola. 2010. The spread of behavior in an online social network experiment. Science 329, 5996 (2010), 1194--1197. Google Scholar
Cross Ref
- Damon Centola and Michael Macy. 2007. Complex contagions and the weakness of long ties. Am. J. Sociol. 113, 3 (2007), 702--734. Google Scholar
Cross Ref
- Aafia Chaudhry, L. Michael Glodé, Matt Gillman, and Robert S. Miller. 2012. Trends in twitter use by physicians at the American society of clinical oncology annual meeting, 2010 and 2011. J. Oncol. Pract. 8, 3 (2012), 173--178. Google Scholar
Cross Ref
- William S. Cooper. 1968. Expected search length: A single measure of retrieval effectiveness based on the weak ordering action of retrieval systems. Am. Document. 19, 1 (1968), 30--41. Google Scholar
Cross Ref
- Robin Dunbar. 2003. Evolution of the social brain. Science 302, 5648 (2003), 1160--1161. Google Scholar
Cross Ref
- Robin I. M. Dunbar. 1992. Neocortex size as a constraint on group size in primates. J. Hum. Evol. 22, 6 (June 1992), 469--493. Google Scholar
Cross Ref
- Bruno Goncalves, Nicola Perra, and Alessandro Vespignani. 2011. Modeling users’ activity on twitter networks: Validation of Dunbar’s number. PLoS One 6, 8 (2011), e22656.Google Scholar
Cross Ref
- Jesús González-Rubio, Daniel Ortiz-Martínez, and Francisco Casacuberta. 2010. Balancing user effort and translation error in interactive machine translation via confidence measures. In Proceedings of the ACL 2010 Conference Short Papers (ACLShort’10). Association for Computational Linguistics, Stroudsburg, PA, USA, 173--177.Google Scholar
Digital Library
- Przemyslaw A. Grabowicz, José J. Ramasco, Esteban Moro, Josep M. Pujol, and Victor M. Eguiluz. 2012. Social features of online networks: The strength of intermediary ties in online social media. PloS One 7, 1 (2012), e29358.Google Scholar
Cross Ref
- Mark S. Granovetter. 1973. The strength of weak ties. Am. J. Sociol. 78, 6 (1973), 1360--1380. Google Scholar
Cross Ref
- Alice F. Healy, James A. Kole, Carolyn J. Buck-Gengle, and Lyle E. Bourne. 2004. Effects of prolonged work on data entry speed and accuracy. J. Exp. Psychol. Appl. 10, 3 (Sep. 2004), 188--199. Google Scholar
Cross Ref
- Jonathan L. Herlocker, Joseph A. Konstan, Al Borchers, and John Riedl. 1999. An algorithmic framework for performing collaborative filtering. In Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 230--237.Google Scholar
Digital Library
- Nathan O. Hodas and Kristina Lerman. 2012. How limited visibility and divided attention constrain social contagion. In Proceedings of the ASE/IEEE International Conference on Social Computing (SocialCom’12). ASE.Google Scholar
- Matthew Hoffman, Francis R. Bach, and David M. Blei. 2010. Online learning for latent Dirichlet allocation. In Advances in Neural Information Processing Systems. 856--864.Google Scholar
- Tad Hogg and Kristina Lerman. 2012. Social dynamics of digg. EPJ Data Sci. 1, 5 (June 2012). Google Scholar
Cross Ref
- Tad Hogg, Kristina Lerman, and Laura M. Smith. 2013. Stochastic models predict user behavior in social media. In Proceedings of ASE/IEEE International Conference on Social Computing (SocialCom).Google Scholar
- Bernardo A. Huberman, Peter L. T. Pirolli, James E. Pitkow, and Rajan M. Lukose. 1998. Strong regularities in world wide web surfing. Science 280, 5360 (1998), 95--97. Google Scholar
Cross Ref
- Jose Luis Iribarren and Esteban Moro. 2011. Affinity paths and information diffusion in social networks. Soc. Netw. 33, 2 (2011), 134--142. Google Scholar
Cross Ref
- Jeon-Hyung Kang and Kristina Lerman. 2013a. LA-CTR: A limited attention collaborative topic regression for social media. In Proceedings of AAAI Conference On Artificial Intelligence (AAAI).Google Scholar
- Jeon-Hyung Kang and Kristina Lerman. 2013b. Structural and cognitive bottlenecks to information access in social networks. In Proceedings of the ACM Hypertext and Social Media Conference (Hypertext’13). Google Scholar
Digital Library
- Jeon-Hyung Kang and Kristina Lerman. 2015. VIP: Incorporating human cognitive biases in a probabilistic model of retweeting. In Proceedings of the International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction (SBP’15). Google Scholar
Cross Ref
- Jeon-Hyung Kang, Kristina Lerman, and Lise Getoor. 2013. LA-LDA: A limited attention topic model for social recommendation. In Proceedings of the International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction. Springer, 211--220. Google Scholar
Digital Library
- Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer 42, 8 (2009), 30--37. Google Scholar
Digital Library
- Kristina Lerman and Tad Hogg. 2014. Leveraging position bias to improve peer recommendation. PLoS One 9, 6 (2014), e98914.Google Scholar
- Hao Ma, Haixuan Yang, Michael R. Lyu, and Irwin King. 2008. Sorec: Social recommendation using probabilistic matrix factorization. In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM’08). ACM, 931--940. Google Scholar
Digital Library
- Giovanna Miritello, Rubén Lara, Manuel Cebrian, and Esteban Moro. 2013a. Limited communication capacity unveils strategies for human interaction. Sci. Rep. 3 (June 2013).Google Scholar
- Giovanna Miritello, Esteban Moro, Rubén Lara, Rocío Martínez-López, John Belchamber, Sam G. B. Roberts, and Robin I. M. Dunbar. 2013b. Time as a limited resource: Communication strategy in mobile phone networks. Soc. Netw. 35, 1 (Jan. 2013), 89--95. Google Scholar
Cross Ref
- J.-P. Onnela, Jari Saramäki, Jorkki Hyvönen, György Szabó, David Lazer, Kimmo Kaski, János Kertész, and A.-L. Barabási. 2007. Structure and tie strengths in mobile communication networks. Proc. Natl. Acad. Sci. 104, 18 (2007), 7332--7336. Google Scholar
Cross Ref
- S. Purushotham, Y. Liu, and C. C. J. Kuo. 2012. Collaborative topic regression with social matrix factorization for recommendation systems. In Proceedings of the International Conference on Machine Learning.Google Scholar
- Ray Reagans and Bill McEvily. 2003. Network structure and knowledge transfer: The effects of cohesion and range. Admin. Sci. Quart. 48, 2 (2003), 240--267. Google Scholar
Cross Ref
- Ray Reagans and Ezra W. Zuckerman. 2001. Networks, diversity, and productivity: The social capital of corporate R8D teams. Organiz. Sci. 12, 4 (2001), 502--517. Google Scholar
Digital Library
- Manuel G. Rodriguez, Krishna Gummadi, and Bernhard Schoelkopf. 2014. Quantifying information overload in social media and its impact on social contagions. In Proceedings of 8th International AAAI Conference on Weblogs and Social Media.Google Scholar
- R. Salakhutdinov and A. Mnih. 2008. Probabilistic matrix factorization. Adv. Neur. Inform. Process. Syst. 20 (2008), 1257--1264.Google Scholar
- Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web. ACM, 285--295. Google Scholar
Digital Library
- Laura M. Smith, Linhong Zhu, Kristina Lerman, and Zornitsa Kozareva. 2013. The role of social media in the discussion of controversial topics. In ASE/IEEE International Conference on Social Computing. Google Scholar
Digital Library
- Brian Uzzi. 1997. Social structure and competition in interfirm networks: The paradox of embeddedness. Admin. Sci. Quart. (1997), 35--67.Google Scholar
- Chong Wang and David M. Blei. 2011. Collaborative topic modeling for recommending scientific articles. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 448--456. Google Scholar
Digital Library
- Duncan Watts and Steven Strogatz. 1998. The small world problem. Collect. Dynam. Small-World Netw. 393 (1998), 440--442.Google Scholar
- Dennis M. Wilkinson. 2008. Strong regularities in online peer production. In Proceedings of the 9th ACM Conference on Electronic Commerce (EC’08). New York, NY, 302--309. Google Scholar
Digital Library
- Bo Wu, Pedro Szekely, and Craig A. Knoblock. 2014. Minimizing user effort in transforming data by example. In Proceedings of the 19th International Conference on Intelligent User Interfaces (IUI’14). ACM, New York, NY, 317--322. Google Scholar
Digital Library
- Cai-Nicolas Ziegler, Sean M. McNee, Joseph A. Konstan, and Georg Lausen. 2005. Improving recommendation lists through topic diversification. In Proceedings of the 14th International Conference on World Wide Web. ACM, 22--32. Google Scholar
Digital Library
Index Terms
Effort Mediates Access to Information in Online Social Networks
Recommendations
The impact of network structure on breaking ties in online social networks: unfollowing on twitter
CHI '11: Proceedings of the SIGCHI Conference on Human Factors in Computing SystemsWe investigate the breaking of ties between individuals in the online social network of Twitter, a hugely popular social media service. Building on sociology concepts such as strength of ties, embeddedness, and status, we explore how network structure ...
Information Attacks on Online Social Networks
Online social networks have changed the way people interact, allowing them to stay in touch with their acquaintances, reconnect with old friends, and establish new relationships with other people based on hobbies, interests, and friendship circles. ...
Uses and gratifications of social networking sites for bridging and bonding social capital
Applying uses and gratifications theory (UGT) and social capital theory, our study examined users of four social networking sites (SNSs) (Facebook, Twitter, Instagram, and Snapchat), and their influence on online bridging and bonding social capital. ...






Comments