Wednesday, 4:00–5:30 PM
Chair: Andrew Tomkins

Randomization Tests for Distinguishing Social Influence and Homophily Effects

Jennifer Neville, Timothy La Fond

Relational autocorrelation is ubiquitous in relational domains. This observed correlation between class labels of linked instances in a network (e.g., two friends are more likely to share political beliefs than two randomly selected people) can be due to the effects of two different social processes. If \emph{social influence} effects are present, instances are likely to change their attributes to conform to their neighbor values. If \emph{homophily} effects are present, instances are likely to link to other individuals with similar attribute values. Both these effects will result in autocorrelated attribute values. When analyzing static relational networks it is impossible to determine how much of the observed correlation is due each of these factors. However with the recent surge of interest in social networks, the availability of dynamic network data has increased. In this paper, we present a randomization technique for temporal network data where the attributes and links change over time. Given data from two time steps, we measure the gain in correlation and assess whether a significant portion of this gain is due to influence and/or homophily. We demonstrate the efficacy of our method on semi-synthetic data and then apply the method to a real-world social networks dataset, showing the impact of both influence and homophily effects.

Exploiting Social Context for Review Quality Prediction

Yue Lu, Panayiotis Tsaparas, Alex Ntoulas, Livia Polanyi

Online reviews in which users publish detailed commentary on web portals about their experiences and opinions with products, services or events are extremely valuable to users who rely on them to make informed decisions. However, because reviews vary greatly in quality, automatic assessment of review helpfulness is of growing importance. Previous work has addressed the review quality prediction problem by treating a review as a stand-alone text document, extracting features from the review text, and learning a function based on these features for predicting the review quality. In this work, we exploit contextual information about authors’ identity and social networks on top of review text features for review quality prediction. We propose a generic framework for incorporating social context information by adding novel regularization constraints to the text-based predictor. Our approach can effectively use the social context information available for large amount of unlabeled reviews. It also has the advantage that the resulting predictor is usable even if social context is unavailable. We validate our framework within a real commerce portal and experimentally demonstrate that using social contextual information can help improve the accuracy of review quality prediction especially when the available training data is sparse.

Context-aware Citation Recommendation

Qi He, Jian Pei, Daniel Kifer, Prasenjit Mitra, C. Lee Giles

When you write papers, how many times do you want to make some citations at a place but you are not sure which papers to cite? Do you wish to have a recommendation system which can recommend a small number of good candidates for every place that you want to make some citations? In this paper, we present our initiative of building a context-aware citation recommendation system. High quality citation recommendation is challenging: not only should the citations recommended be relevant to the paper under composition, but also should match the local contexts of the places citations are made. Moreover, it is far from trivial to model how the topic of the whole paper and the contexts of the citation places should affect the selection and ranking of citations. To tackle the problem, we develop a context-aware approach. The core idea is to design a novel non-parametric probabilistic model which can measure the context-based relevance between a citation context and a document. Our approach can recommend citations for a context effectively. Moreover, it can recommend a set of citations for a paper with high quality. We implement a prototype system in CiteSeerX. An extensive empirical evaluation in the CiteSeerX digital library against many baselines demonstrates the effectiveness and the scalability of our approach.


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