Social Network Analysis #sna
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Social Network Analysis #sna
Social Network Analysis
Curated by ukituki
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Rescooped by ukituki from Influence et contagion
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#Predicting Successful #Memes using Network and Community Structure | #SNA #contagion

#Predicting Successful #Memes using Network and Community Structure | #SNA #contagion | Social Network Analysis #sna | Scoop.it

Via luiy
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luiy's curator insight, March 27, 2014 1:44 PM

We investigate the predictability of successful memes using their early spreading patterns in the underlying social networks. We propose and analyze a comprehensive set of features and develop an accurate model to predict future popularity of a meme given its early spreading patterns. Our paper provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentration, and characteristics of adoption time series. We find that features based on community structure are the most powerful predictors of future success. We also find that early popularity of a meme is not a good predictor of its future popularity, contrary to common belief. Our methods outperform other approaches, particularly in the task of detecting very popular or unpopular memes.

António F Fonseca's curator insight, April 2, 2014 6:01 AM

Another paper about popularity prediction.

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Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer Influence in Networks by Sinan Aral, Dylan Walker :: SSRN

Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer Influence in Networks by Sinan Aral, Dylan Walker :: SSRN | Social Network Analysis #sna | Scoop.it

We examine how firms can create word-of-mouth peer influence and social contagion by designing viral features into their products and marketing campaigns.

 

Word-of-mouth (WOM) is generally considered to be more effective at promoting product contagion when it is personalized and active. Unfortunately, the relative effectiveness of different viral features has not been quantified, nor has their effectiveness been definitively established, largely because of difficulties surrounding econometric identification of endogenous peer effects. We therefore designed a randomized field experiment on a popular social networking website to test the effectiveness of a range of viral messaging capabilities in creating peer influence and social contagion among the 1.4 million friends of 9,687 experimental users.

 

 

Overall, we find that viral product design features can indeed generate econometrically identifiable peer influence and social contagion effects. More surprisingly, we find that passive-broadcast viral messaging generates a 246% increase in local peer influence and social contagion effects, while adding active-personalized viral messaging only generates an additional 98% increase in contagion.

 

Although active-personalized messaging is more effective in encouraging adoption per message and is correlated with more user engagement and sustained product use, passive-broadcast messaging is used more often enough to eclipse those benefits, generating more total peer adoption in the network. In addition to estimating the effects of viral product design on social contagion and product diffusion, our work also provides a model for how randomized trials can be used to identify peer influence effects in networks.

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