Inferring the impacts of social media on crowdfunding

Crowdfunding -- in which people can raise funds through collaborative contributions of general public (i.e., crowd) -- has emerged as a billion dollars business for supporting more than one million ventures. However, very few research works have examined the process of crowdfunding. In particular, none has studied how social networks help crowdfunding projects to succeed. To gain insights into the effects of social networks in crowdfunding, we analyze the hidden connections between the fundraising results of projects on crowdfunding websites and the corresponding promotion campaigns in social media. Our analysis considers the dynamics of crowdfunding from two aspects: how fundraising activities and promotional activities on social media simultaneously evolve over time, and how the promotion campaigns influence the final outcomes. From our investigation, we identify a number of important principles that provide a useful guide for devising effective campaigns. For example, we observe temporal distribution of customer interest, strong correlations between a crowdfunding project's early promotional activities and the final outcomes, and the importance of concurrent promotion from multiple sources. We then show that these discoveries can help predict several important quantities, including overall popularity and the success rate of the project. Finally, we show how to use these discoveries to help design crowdfunding sites.

[1]  Shou-De Lin,et al.  Finding influential seed successors in social networks , 2012, WWW.

[2]  Philip S. Yu,et al.  On Influential Node Discovery in Dynamic Social Networks , 2012, SDM.

[3]  Bernardo A. Huberman,et al.  Predicting the popularity of online content , 2008, Commun. ACM.

[4]  Kristina Lerman,et al.  Information Contagion: An Empirical Study of the Spread of News on Digg and Twitter Social Networks , 2010, ICWSM.

[5]  Tad Hogg,et al.  Using a model of social dynamics to predict popularity of news , 2010, WWW '10.

[6]  Philip S. Yu,et al.  Inferring social roles and statuses in social networks , 2013, KDD.

[7]  Andreas Kemper,et al.  Valuation of Network Effects in Software Markets , 2010 .

[8]  Li-Te Cheng,et al.  Crowdfunding inside the enterprise: employee-initiatives for innovation and collaboration , 2013, CHI.

[9]  Gueorgi Kossinets,et al.  Empirical Analysis of an Evolving Social Network , 2006, Science.

[10]  Aristides Gionis,et al.  Correlating financial time series with micro-blogging activity , 2012, WSDM '12.

[11]  Ethan Mollick,et al.  The Dynamics of Crowdfunding: Determinants of Success and Failure , 2013 .

[12]  Bernard J. Jansen,et al.  Twitter power: Tweets as electronic word of mouth , 2009, J. Assoc. Inf. Sci. Technol..

[13]  Saverio Niccolini,et al.  A peek into the future: predicting the evolution of popularity in user generated content , 2013, WSDM.

[14]  Jure Leskovec,et al.  Information diffusion and external influence in networks , 2012, KDD.

[15]  Scott Counts,et al.  Predicting the Speed, Scale, and Range of Information Diffusion in Twitter , 2010, ICWSM.

[16]  Didier Sornette,et al.  Robust dynamic classes revealed by measuring the response function of a social system , 2008, Proceedings of the National Academy of Sciences.

[17]  Jure Leskovec,et al.  Modeling Information Diffusion in Implicit Networks , 2010, 2010 IEEE International Conference on Data Mining.

[18]  Christos Faloutsos,et al.  Rise and fall patterns of information diffusion: model and implications , 2012, KDD.

[19]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[20]  Bernardo A. Huberman,et al.  Predicting the Future with Social Media , 2010, 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.

[21]  Anindya Ghose,et al.  An Empirical Examination of the Antecedents and Consequences of Contribution Patterns in Crowd-Funded Markets , 2013, Inf. Syst. Res..

[22]  Jussara M. Almeida,et al.  Using early view patterns to predict the popularity of youtube videos , 2013, WSDM.