Social analytics: Learning from human interactions

ICML 2010 Workshop

25th June, 2010
Haifa, Israel


Call for Participation

Important Dates

Accepted papers


Invited Talks





Jure Leskovec

The Flow of Information in Networks

Abstract: The information we experience online comes to us continuously over time, assembled from many small pieces, and conveyed through our social networks. This merging of information, network structure, and flow over time requires new ways of reasoning about the large-scale behavior of information networks. I’ll discuss approaches for tracking information as it travels and mutates in online networks, showing how to capture temporal patterns in the news over a daily time-scale – in particular, the succession of story lines that evolve, compete for attention, and collectively produce an effect that commentators refer to as the news cycle. I’ll also discuss how to quantify the influence of individual media sites on the popularity of stories and an algorithm for efficiently finding influential media sites.

This talk includes joint work with Lars Backstrom, Jon Kleinberg and JaewonYang.

Duncan Watts

Tentative topic: “Inferring relevant networks from social communication data”

Gal Oestreicher-Singer

The Breadth of Contagion of the Oprah Effect:

The Diffusion of Demand Shocks in a Recommendation Network

Abstract: A number of offline media events have a significant impact on online consumer choices, demand and search. Moreover, owing to the hyperlinked nature of the web (and specifically of e-commerce web sites), the span of impact of these offline events is not restricted to the topics or products that were their original focus. Rather, the “shocks” created by these events spread through networks of interconnected products, pages or people. In this study, we investigate the online contagion of exogenous demand shocks created by offline media events. Our context for offline media events are book reviews featured on the Oprah Winfrey television show and in the Sunday New York Times. We studied the impact and diffusion of these exogenous events on the demand for a “network” of related books that were not explicitly mentioned in a review but were located “close” to a reviewed book in an online network. This network is a visible co-purchase network created based on historical demand patterns at a leading online retailer during the years 2006-2007.

Using a difference-in-differences matched sample approach we identified the extent of the variations caused by the visibility of the online network (i.e., by consumers clicking on visible hyperlinks) and distinguished this effect from variation caused by hidden product complementarity. We found a strikingly high level of diffusion of exogenous shocks through such networks. Neighboring books (even four clicks away) experienced a dramatic increase in their demand levels, even though they were not actually featured in the review; this effect is indicative of the depth of contagion in online recommendation networks following exogenous shocks.

We found that product characteristics, assortative mixing and local network structure play an important role in explaining which books will be affected by the shock, as well as, the relative persistency of the shocks. Most interestingly, we found that clustered networks “trap” a higher fraction of the contagion closer to the reviewed book. Our research provides an important documentation of the magnitude and persistence of diffusion of demand shocks across product networks, and evidence of the important role and influence of product networks in electronic commerce in the presence of exogenous shocks.

Joint work with Eyal Carmi (Tel Aviv University) and Arun Sundararajan (New York University)

Elad Yom-Tov

The group-first approach to social churn prediction