Results 1 - 10
of
67
Can cascades be predicted?
"... On many social networking web sites such as Facebook and Twit-ter, resharing or reposting functionality allows users to share oth-ers ’ content with their own friends or followers. As content is reshared from user to user, large cascades of reshares can form. While a growing body of research has foc ..."
Abstract
-
Cited by 32 (4 self)
- Add to MetaCart
(Show Context)
On many social networking web sites such as Facebook and Twit-ter, resharing or reposting functionality allows users to share oth-ers ’ content with their own friends or followers. As content is reshared from user to user, large cascades of reshares can form. While a growing body of research has focused on analyzing and characterizing such cascades, a recent, parallel line of work has argued that the future trajectory of a cascade may be inherently un-predictable. In this work, we develop a framework for addressing cascade prediction problems. On a large sample of photo reshare cascades on Facebook, we find strong performance in predicting whether a cascade will continue to grow in the future. We find that the relative growth of a cascade becomes more predictable as we observe more of its reshares, that temporal and structural features are key predictors of cascade size, and that initially, breadth, rather than depth in a cascade is a better indicator of larger cascades. This prediction performance is robust in the sense that multiple distinct classes of features all achieve similar performance. We also dis-cover that temporal features are predictive of a cascade’s eventual shape. Observing independent cascades of the same content, we find that while these cascades differ greatly in size, we are still able to predict which ends up the largest.
Mining Structural Hole Spanners Through Information Diffusion in Social Networks
"... The theory of structural holes [4] suggests that individuals would benefit from filling the “holes ” (called as structural hole spanners) between people or groups that are otherwise disconnected. A few empirical studies have verified that structural hole spanners play a key role in the information d ..."
Abstract
-
Cited by 24 (13 self)
- Add to MetaCart
(Show Context)
The theory of structural holes [4] suggests that individuals would benefit from filling the “holes ” (called as structural hole spanners) between people or groups that are otherwise disconnected. A few empirical studies have verified that structural hole spanners play a key role in the information diffusion. However, there is still lack of a principled methodology to detect structural hole spanners from a given social network. In this work, we precisely define the problem of mining top-k structural hole spanners in large-scale social networks and provide an objective (quality) function to formalize the problem. Two instantiation models have been developed to implement the objective function. For the first model, we present an exact algorithm to solve it and prove its convergence. As for the second model, the optimization is proved to be NP-hard, and we design an efficient algorithm with provable approximation guarantees. We test the proposed models on three different networks: Coauthor, Twitter, and Inventor. Our study provides evidence for the theory of structural holes, e.g., 1 % of Twitter users who span structural holes control 25 % of the information diffusion on Twitter. We compare the proposed models with several alternative methods and the results show that our models clearly outperform the comparison methods. Our experiments also demonstrate that the detected structural hole spanners can help other social network applications, such as community kernel detection and link prediction. To the best of our knowledge, this is the first attempt to address the problem of mining structural hole spanners in large social networks.
Social Influence Locality for Modeling Retweeting Behaviors
"... We study an interesting phenomenon of social influence locality in a large microblogging network, which suggests that users ’ behaviors are mainly influenced by close friends in their ego networks. We provide a formal definition for the notion of social influence locality and develop two instantiati ..."
Abstract
-
Cited by 19 (10 self)
- Add to MetaCart
We study an interesting phenomenon of social influence locality in a large microblogging network, which suggests that users ’ behaviors are mainly influenced by close friends in their ego networks. We provide a formal definition for the notion of social influence locality and develop two instantiation functions based on pairwise influence and structural diversity. The defined influence locality functions have strong predictive power. Without any additional features, we can obtain a F1-score of 71.65 % for predicting users ’ retweet behaviors by training a logistic regression classifier based on the defined functions. Our analysis also reveals several intriguing discoveries. For example, though the probability of a user retweeting a microblog is positively correlated with the number of friends who have retweeted the microblog, it is surprisingly negatively correlated with the number of connected circles that are formed by those friends. 1
Modeling Information Propagation with Survival Theory
, 2013
"... Networks provide a ‘skeleton’ for the spread of contagions, like, information, ideas, behaviors and diseases. Many times networks over which contagions diffuse are unobserved and need to be inferred. Here we apply survival theory to develop general additive and multiplicative risk models under which ..."
Abstract
-
Cited by 14 (5 self)
- Add to MetaCart
Networks provide a ‘skeleton’ for the spread of contagions, like, information, ideas, behaviors and diseases. Many times networks over which contagions diffuse are unobserved and need to be inferred. Here we apply survival theory to develop general additive and multiplicative risk models under which the network inference problems can be solved efficiently by exploiting their convexity. Our additive risk model generalizes several existing network inference models. We show all these models are particular cases of our more general model. Our multiplicative model allows for modeling scenarios in which a node can either increase or decrease the risk of activation of another node, in contrast with previous approaches, which consider only positive risk increments. We evaluate the performance of our network inference algorithms on large synthetic and real cascade datasets, and show that our models are able to predict the length and duration of cascades in real data.
Characterizing the Life Cycle of Online News Stories Using Social Media Reactions
"... This paper presents a study of the life cycle of news articles posted online. We consider user activity both from the perspective of their visitation patterns and from their social media reactions. We show that we can use this information to characterize distinct classes of articles, and that we can ..."
Abstract
-
Cited by 9 (3 self)
- Add to MetaCart
(Show Context)
This paper presents a study of the life cycle of news articles posted online. We consider user activity both from the perspective of their visitation patterns and from their social media reactions. We show that we can use this information to characterize distinct classes of articles, and that we can use social media reactions to predict future visitation patterns early and accurately. We validate our methods using qualitative analysis as well as quantitative analysis on data from the website of Al Jazeera in English, for a set of articles generating more than 3,000,000 visits and 200,000 social media reactions. We show that it is possible to predict the overall traffic an article will receive with the first ten minutes of social media reactions; the prediction accuracy is equivalent to the one based solely on visits after three hours. We also describe significant improvements on the accuracy of the prediction of shelf-life for news stories.
The Bursty Dynamics of the Twitter Information Network
"... In online social media systems users are not only posting, consum-ing, and resharing content, but also creating new and destroying existing connections in the underlying social network. While each of these two types of dynamics has individually been studied in the past, much less is known about the ..."
Abstract
-
Cited by 8 (0 self)
- Add to MetaCart
(Show Context)
In online social media systems users are not only posting, consum-ing, and resharing content, but also creating new and destroying existing connections in the underlying social network. While each of these two types of dynamics has individually been studied in the past, much less is known about the connection between the two. How does user information posting and seeking behavior interact with the evolution of the underlying social network structure? Here, we study ways in which network structure reacts to users posting and sharing content. We examine the complete dynamics of the Twitter information network, where users post and reshare information while they also create and destroy connections. We find that the dynamics of network structure can be characterized by steady rates of change, interrupted by sudden bursts. Information diffusion in the form of cascades of post re-sharing often creates such sudden bursts of new connections, which significantly change users ’ local network structure. These bursts transform users ’ net-works of followers to become structurally more cohesive as well as more homogenous in terms of follower interests. We also explore the effect of the information content on the dynamics of the net-work and find evidence that the appearance of new topics and real-world events can lead to significant changes in edge creations and deletions. Lastly, we develop a model that quantifies the dynam-ics of the network and the occurrence of these bursts as a function of the information spreading through the network. The model can successfully predict which information diffusion events will lead to bursts in network dynamics.
Social Influence Based Clustering of Heterogeneous Information Networks
"... Social networks continue to grow in size and the type of infor-mation hosted. We witness a growing interest in clustering a so-cial network of people based on both their social relationships and their participations in activity based information networks. In this paper, we present a social influence ..."
Abstract
-
Cited by 7 (3 self)
- Add to MetaCart
(Show Context)
Social networks continue to grow in size and the type of infor-mation hosted. We witness a growing interest in clustering a so-cial network of people based on both their social relationships and their participations in activity based information networks. In this paper, we present a social influence based clustering framework for analyzing heterogeneous information networks with three u-nique features. First, we introduce a novel social influence based vertex similarity metric in terms of both self-influence similarity and co-influence similarity. We compute self-influence and co-influence based similarity based on social graph and its associat-ed activity graphs and influence graphs respectively. Second, we compute the combined social influence based similarity between each pair of vertices by unifying the self-similarity and multiple co-influence similarity scores through a weight function with an iterative update method. Third, we design an iterative learning al-gorithm, SI-Cluster, to dynamically refine the K clusters by con-tinuously quantifying and adjusting the weights on self-influence similarity and on multiple co-influence similarity scores toward-s the clustering convergence. To make SI-Cluster converge fast, we transformed a sophisticated nonlinear fractional programming problem of multiple weights into a straightforward nonlinear para-metric programming problem of single variable. Our experiment results show that SI-Cluster not only achieves a better balance be-tween self-influence and co-influence similarities but also scales extremely well for large graph clustering.
Cascading outbreak prediction in networks: a data-driven approach
- In KDD’13
"... Cascades are ubiquitous in various network environments such as epidemic networks, traffic networks, water distribution networks and social networks. The outbreaks of cascades will often bring bad or even devastating effects. How to accurately predict the cascading outbreaks in early stage is of par ..."
Abstract
-
Cited by 4 (3 self)
- Add to MetaCart
(Show Context)
Cascades are ubiquitous in various network environments such as epidemic networks, traffic networks, water distribution networks and social networks. The outbreaks of cascades will often bring bad or even devastating effects. How to accurately predict the cascading outbreaks in early stage is of paramount importance for people to avoid these bad effects. Although there have been some pioneering works on cascading outbreaks detection, how to predict, rather than detect, the cascading outbreaks is still an open problem. In this pa-per, we attempt harnessing historical cascade data, propose a novel data driven approach to select important nodes as sensors, and pre-dict the outbreaks based on the cascading behaviors of these sen-sors. In particular, we propose Orthogonal Sparse LOgistic Regres-sion (OSLOR) method to jointly optimize node selection and out-break prediction, where the prediction loss are combined with an orthogonal regularizer and L1 regularizer to guarantee good predic-tion accuracy, as well as the sparsity and low-redundancy of select-ed sensors. We evaluate the proposed method on a real online social network dataset including 182.7 million information cascades. The experimental results show that the proposed OSLOR significantly and consistently outperform topological measure based method and other data driven methods in prediction performances.
