Yes, there is a correlation: - from social networks to personal behavior on the web
By Parag Singla, Matthew Richardson. The Web Conference, 2008
364 citations recorded by the source corpus.
Subfields: Management Science and Operations Research, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/3581ff32946ed9ed07be8e307bffd57d3b8d995e
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In Management Science and Operations Research
In Statistical and Nonlinear Physics
Citations in the corpus
- Microscopic Social Influence (2012, background): “It is well recognized that social influence is one complex and subtle force that governs these dynamics [4, 5]: the actions of one individual may induce his/her friends to behave in a similar way via their social connections.”
- Collaborative Filtering beyond the User-Item Matrix (2014, background): “Previous work has shown that there is a correlation between user online communication behavior and social relationships [Singla and Richardson 2008], which may also indicate the potential correlation between recommender and showed a positive correlation between the two.”
- Exploring social influence for recommendation: a generative model approach (2012, background): “surprising that recommendation techniques that exploit the similar item selection behavior of friends have achieved a certain degree of success, as several research studies on social network analysis have indicated that a user’s behavior indeed often correlates to the behavior of her friends [8, 29]”
- Information and Influence Propagation in Social Networks (2013, methodology): “In Singla and Richardson [2008] researchers used Microsoft data from instant messaging platform MSN Messenger, together with searches in the Microsoft search engine Bing. eir conclusion was that users who are friends in the messaging platform do tend to issue similar queries. Something similar was ”
- Community Detection and Mining in Social Media (2010, background): “4 DISTINGUISHING INFLUENCE AND CORRELATION It has been widely observed that user attributes and behaviors tend to correlate with their social networks (Singla and Richardson, 2008).”
- Harvesting ambient geospatial information from social media feeds (2011, background): “Studies support the driving role of homophily in social network activities at large (McPherson et al. 2001; Singla and Richardson 2008) and twitter in particular (Choudhury et al.”
- Scalable Learning of Collective Behavior (2012, background): “Prodigious numbers of online volunteers collaboratively write encyclopedia articles of unprecedented scope and scale; online marketplaces recommend products by investigating user shopping behavior and interactions; and political movements also exploit new forms of engagement and collective action.”
- Modeling relationship strength in online social networks (2010, background): “Singla and Richardson [18] investigate the correlation between individual search topics among people that interact using instant messaging, and show that not only does a correlation exist but that it increases with the amount of time the users communicate.”
- Fast shortest path distance estimation in large networks (2009, methodology): “Given a graph G = ( V, E ) select the minimum number of landmarks D ⊆ V so that all pairs of vertices ( s, t ) ∈ V × V are covered.”
- Social influence analysis in large-scale networks (2009, methodology): “For social influence analysis, [2] and [21] propose methods to qualitatively measure the existence of influence.”
- Planetary-scale views on a large instant-messaging network (2008, methodology): “Recently, using the data we generated, Singla and Richardson further investigated the homophily within the Messenger network and found that people who communicate are also more likely to search the web for content on similar topics [14].”
- Social Network Data Analytics (2011, methodology): “Spectral clustering [60] is a body of algorithms that clusters datapoints xi using eigenvectors of a matrix derived from the af nity matrix A constructed from the data.”
- SoRec: social recommendation using probabilistic matrix factorization (2008, background): “In the most recent research work conducted in [21], by analyzing the who talks to whom social network on the MSN instant messenger(2) over 10 million people with their related search records on the Live Search Engine(3), P.”