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15
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 ..."
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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.
Predicting Successful Memes using Network and Community Structure
"... We investigate the predictability of successful memes using their early spreading patterns in the underlying social net-works. We propose and analyze a comprehensive set of fea-tures and develop an accurate model to predict future popu-larity of a meme given its early spreading patterns. Our pa-per ..."
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We investigate the predictability of successful memes using their early spreading patterns in the underlying social net-works. We propose and analyze a comprehensive set of fea-tures and develop an accurate model to predict future popu-larity of a meme given its early spreading patterns. Our pa-per provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentra-tion, 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 popular-ity 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.
Online popularity and topical interests through the lens of Instagram
- HT
, 2014
"... Online socio-technical systems can be studied as proxy of the real world to investigate human behavior and social in-teractions at scale. Here we focus on Instagram, a media-sharing online platform whose popularity has been rising up to gathering hundred millions users. Instagram exhibits a mixture ..."
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Online socio-technical systems can be studied as proxy of the real world to investigate human behavior and social in-teractions at scale. Here we focus on Instagram, a media-sharing online platform whose popularity has been rising up to gathering hundred millions users. Instagram exhibits a mixture of features including social structure, social tag-ging and media sharing. The network of social interac-tions among users models various dynamics including fol-lower/followee relations and users ’ communication by means of posts/comments. Users can upload and tag media such as photos and pictures, and they can “like ” and comment each piece of information on the platform. In this work we inves-tigate three major aspects on our Instagram dataset: (i) the structural characteristics of its network of heterogeneous in-teractions, to unveil the emergence of self organization and topically-induced community structure; (ii) the dynamics of content production and consumption, to understand how global trends and popular users emerge; (iii) the behavior of users labeling media with tags, to determine how they de-vote their attention and to explore the variety of their topical interests. Our analysis provides clues to understand human behavior dynamics on socio-technical systems, specifically users and content popularity, the mechanisms of users ’ in-teractions in online environments and how collective trends emerge from individuals ’ topical interests. 1.
Supporting a Social Media Observatory with Customizable Index Structures - Architecture and Performance
, 2014
"... Abstract. The intensive research activity in analysis of social media and micro-blogging data in recent years suggests the necessity and great potential of platforms that can efficiently store, query, analyze, and visualize social media data. To support these “social media observatories ” effectivel ..."
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Abstract. The intensive research activity in analysis of social media and micro-blogging data in recent years suggests the necessity and great potential of platforms that can efficiently store, query, analyze, and visualize social media data. To support these “social media observatories ” effectively, a storage platform must satisfy special requirements for loading and storage of multi-terabyte datasets, as well as efficient evaluation of queries involving analysis of the text of millions of social updates. Traditional inverted indexing techniques do not meet such requirements. As a solution, we propose a general indexing framework, IndexedHBase, to build specially customized index structures for facilitating efficient queries on an HBase distributed data storage system. IndexedHBase is used to support a social media observatory that collects and analyzes data obtained through the Twitter streaming API. We develop a parallel query evaluation strategy that can explore the customized index structures efficiently, and test it on a set of typical social media data queries. We evaluate the performance of IndexedHBase on FutureGrid and compare it with Riak, a widely adopted commercial NoSQL database system. The results show that IndexedHBase provides a data loading speed that is six times faster than Riak and is significantly more efficient in evaluating queries involving large result sets.
Social Media Data Analysis with IndexedHBase and Iterative MapReduce
"... As data intensive applications evolve, many research projects involving Big Data require efficient extraction and analysis of specific data subsets, rather than the whole dataset. Social media data analysis is one such example. While social media platforms provide tremendous data about all kinds of ..."
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As data intensive applications evolve, many research projects involving Big Data require efficient extraction and analysis of specific data subsets, rather than the whole dataset. Social media data analysis is one such example. While social media platforms provide tremendous data about all kinds of social activities, most research analyses focus on specific social events along the lines of presidential elections or protests. In order to support the requirements of such research use cases, the storage platform needs to provide not only a scalable solution for the overall large dataset, but also mechanisms for efficiently querying the target subsets and applying post-query analyses. This paper introduces IndexedHBase, a storage platform specially designed to support end-to-end analysis of social media data. IndexedHBase uses HBase as the storage substrate, and provides a customizable
COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution
"... Abstract Information diffusion in online social networks is affected by the underlying network topology, but it also has the power to change it. Online users are constantly creating new links when exposed to new information sources, and in turn these links are alternating the way information spread ..."
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Abstract Information diffusion in online social networks is affected by the underlying network topology, but it also has the power to change it. Online users are constantly creating new links when exposed to new information sources, and in turn these links are alternating the way information spreads. However, these two highly intertwined stochastic processes, information diffusion and network evolution, have been predominantly studied separately, ignoring their co-evolutionary dynamics. We propose a temporal point process model, COEVOLVE, for such joint dynamics, allowing the intensity of one process to be modulated by that of the other. This model allows us to efficiently simulate interleaved diffusion and network events, and generate traces obeying common diffusion and network patterns observed in real-world networks. Furthermore, we also develop a convex optimization framework to learn the parameters of the model from historical diffusion and network evolution traces. We experimented with both synthetic data and data gathered from Twitter, and show that our model provides a good fit to the data as well as more accurate predictions than alternatives.
A Visibility-based Model for Link Prediction in Social Media
"... A core task of social network analysis is to predict the for-mation of new social links. In the context of social media, link prediction serves as the foundation for forecasting the evolution of the follower graph and predicting interactions and the flow of information between users. Previous link p ..."
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A core task of social network analysis is to predict the for-mation of new social links. In the context of social media, link prediction serves as the foundation for forecasting the evolution of the follower graph and predicting interactions and the flow of information between users. Previous link prediction methods have generally represented the social network as a graph and leveraged topological and seman-tic measures of similarity between two nodes to estimate the probability of a new link between them. In this work, we suggest another link creation mechanism for social me-dia that is based on the ease of discovering the new node. Specifically, a user v creates a link to another user u af-ter seeing u’s name on his or her screen; in other words, visibility of a user (name) is a necessary condition for new link formation. We propose a model for link prediction, which estimates the probability a user will see another user’s name, and use this model to predict new links. We estimate a set of parameters in the proposed model using Maximum-Likelihood and Minorize-Maximize methods. Empirical re-sults show that the proposed model can more accurately predict both follow and co-mention links than alternative state-of-the-art methods. Our work suggests that the effort required to discover a new social contact is negatively cor-related with link formation, and the easier it is to discover a user, the higher the likelihood a link will be created. 1
Supporting End-to-End Social Media Data Analysis with the IndexedHBase Platform
- in Invited talk at 6th Workshop on Many-Task Computing on Clouds, Grids, and Supercomputers (MTAGS) SC13. November 17
, 2013
"... As data intensive applications evolve, many research projects involving Big Data require efficient extraction and analysis of specific data subsets, rather than the whole dataset. Social media data analysis is one such example. While social media platforms such as Twitter provide tremendous data abo ..."
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Cited by 1 (1 self)
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As data intensive applications evolve, many research projects involving Big Data require efficient extraction and analysis of specific data subsets, rather than the whole dataset. Social media data analysis is one such example. While social media platforms such as Twitter provide tremendous data about all kinds of social activities, most research analyses focus on specific social events, such as presidential elections or protests. In order to support the requirements of such research use cases, the storage platform needs to provide not only a scalable solution for the overall large dataset, but also mechanisms for efficiently querying the target subsets and applying post-query analyses. This paper introduces IndexedHBase, a storage platform specially designed to support end-to-end analysis of social media data. IndexedHBase uses HBase as the storage substrate, and provides a customizable
