Kristina Lerman
ABSTRACT
The social Web is transforming the way information is created and distributed. Blog authoring tools enable users to publish content, while sites such as Digg and Del.icio.us are used to distribute content to a wider audience. With content fast becoming a commodity, interest in using social networks to promote and find content has grown, both on the side of content producers (viral marketing) and consumers (recommendation). Here we study the role of social networks in promoting content on Digg, a social news aggregator that allows users to submit links to and vote on news stories. Digg's goal is to feature the most interesting stories on its front page, and it aggregates opinions of its many users to identify them. Like other social networking sites, Digg allows users to designate other users as "friends" and see what stories they found interesting. We studied the spread of interest in news stories submitted to Digg in June 2006. Our results suggest that pattern of the spread of interest in a story on the network is indicative of how popular the story will become. Stories that spread mainly outside of the submitter's neighborhood go on to be very popular, while stories that spread mainly through submitter's social neighborhood prove not to be very popular. This effect is visible already in the early stages of voting, and one can make a prediction about the potential audience of a story simply by analyzing where the initial votes come from.
- M. Arrington. Troubles in diggville. 09/06/2006 http://www.techcrunch.com/2006/09/06/troubles-in-diggville/.Google Scholar
- M. Calore. Digg fights top users for control, 09/07/2006 Wired News.Google Scholar
- R. Crane and D. Sornette. Viral, quality, and junk videos on youtube: Separating content from noise in an information-rich environment. In Proc. of AAAI symposium on Social Information Processing, Menlo Park, CA, 2008. AAAI.Google Scholar
- P. Domingos and M. Richardson. Mining the network value of customers. In KDD '01: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pages 57--66, New York, NY, USA, 2001. ACM Press. Google ScholarDigital Library
- A. Galstyan and P. Cohen. Cascading dynamics in modular networks. Phys. Rev. E, 75:036109, 2007.Google ScholarCross Ref
- M. Girvan and M. E. Newman. Community structure in social and biological networks. Proc Natl Acad Sci U S A, 99(12):7821--7826, June 2002.Google ScholarCross Ref
- M. S. Granovetter. The strength of weak ties. The American Journal of Sociology, 78(6):1360--1380, 1973.Google ScholarCross Ref
- D. Gruhl, D. Liben-Nowell, R. Guha, and A. Tomkins. Information diffusion through blogspace. SIGKDD Explor. Newsl., 6(2):43--52, December 2004. Google ScholarDigital Library
- K. Lerman. Social information processing in social news aggregation. IEEE Internet Computing: special issue on Social Search, 11(6):16--28, 2007. Google ScholarDigital Library
- K. Lerman. User participation in social media: Digg study. Proc. of the WI-IAT Workshop on Social Media Analysis (SMA07), 2007. Google ScholarDigital Library
- K. Lerman, A. Plangprasopchok, and C. Wong. Personalizing results of image search on flickr. In AAAI workshop on Intelligent Techniques for Web Personlization, 2007.Google Scholar
- J. Leskovec, L.A. Adamic, and B.A. Huberman. The dynamics of viral marketing. In EC '06: Proceedings of the 7th ACM conference on Electronic commerce, pages 228--237, New York, NY, USA, 2006. ACM Press. Google ScholarDigital Library
- A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee. Measurement and analysis of online social networks. In Proceedings of the 5th ACM/USENIX Internet Measurement Conference (IMC'07), 2007. Google ScholarDigital Library
- A. Mislove, K.P. Gummadi, and P. Druschel. Exploiting social networks for internet search. In Proceedings of the 5th Workshop on Hot Topics in Networks (HotNets'06), 2006.Google Scholar
- M. E. J. Newman. Modularity and community structure in networks. Proc Natl Acad Sci U S A, 103(23):8577--8582, June 2006.Google ScholarCross Ref
- R. Pastor-Satorras and A. Vespignani. Epidemic dynamics and endemic states in complex networks. Phys. Rev. E, 63(6):066117, 2001.Google ScholarCross Ref
- R. Pastor-Satorras and A. Vespignani. Epidemic spreading in scale-free networks. Phys. Rev. Lett., 86(14):3200--3203, 2001.Google ScholarCross Ref
- M. Richardson and P. Domingos. Mining knowledge-sharing sites for viral marketing. In KDD '02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 61--70, New York, NY, USA, 2002. ACM. Google ScholarDigital Library
- K. Rose. Digg friends, 09/2006 http://diggtheblog.blogspot.com/2006/09/digg-friends.html.Google Scholar
- K. Rose. A couple of updates, 02/01/2007 http://blog.digg.com/?p=60.Google Scholar
- M. J. Salganik, P. S. Dodds, and D. J. Watts. Experimental study of inequality and unpredictability in an artificial cultural market. Science, 311:854, 2006.Google ScholarCross Ref
- I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco, 2nd edition edition, 2005. Google ScholarDigital Library
- F. Wu, B. Huberman, L. Adamic, and J. Tyler. Information flow in social groups. Physica A, 2003.Google Scholar
- F. Wu and B. A. Huberman. Novelty and collective attention. Proceedings of the National Academy of Sciences, 104(45):17599--17601, November 2007.Google ScholarCross Ref
Recommendations
Show me your friends, and I will tell you whom you vote for: Predicting voting behavior in social networks
ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and MiningIncreasing use of social media in campaigns raises the question of whether one can predict the voting behavior of social-network users who do not disclose their political preferences in their online profiles. Prior work on this task only considered ...
Using a model of social dynamics to predict popularity of news
WWW '10: Proceedings of the 19th international conference on World wide webPopularity of content in social media is unequally distributed, with some items receiving a disproportionate share of attention from users. Predicting which newly-submitted items will become popular is critically important for both companies that host ...
Do social networks improve e-commerce?: a study on social marketplaces
WOSN '08: Proceedings of the first workshop on Online social networksSocial networks have made a significant impact on how Internet users communicate, search for and share data today. Numerous proposals have been made to improve existing distributed systems by leveraging the inherent trust built into social links. For ...
Comments