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112
Using a Model of Social Dynamics to Predict Popularity of News. WWW
, 2010
"... Popularity of content in social media is unequally distributed, with some items receiving a disproportionate share of attention from users. Predicting which newly-submitted items will become popular is critically important for both companies that host social media sites and their users. Accurate and ..."
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Cited by 57 (6 self)
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Popularity of content in social media is unequally distributed, with some items receiving a disproportionate share of attention from users. Predicting which newly-submitted items will become popular is critically important for both companies that host social media sites and their users. Accurate and timely prediction would enable the companies to maximize revenue through differential pricing for access to content or ad placement. Prediction would also give consumers an important tool for filtering the ever-growing amount of content. Predicting popularity of content in social media, however, is challenging due to the complex interactions among content quality, how the social media site chooses to highlight content, and influence among users. While these factors make it difficult to predict popularity a priori, we show that stochastic models of user behavior on these sites allows predicting popularity based on early user reactions to new content. By incorporating aspects of the web site design, such models improve on predictions based on simply extrapolating from the early votes. We validate this claim on the social news portal Digg using a previously-developed model of social voting based on the Digg user interface.
Discovering leaders from community actions
- In In Proceedings of ACM 17th Conference on Information and Knowledge Management (CIKM
, 2008
"... We introduce a novel frequent pattern mining approach to discover leaders and tribes in social networks. In particular, we consider social networks where users perform actions. Actions may be as simple as tagging resources (urls) as in del.icio.us, rating songs as in Yahoo! Music, or movies as in Ya ..."
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Cited by 37 (6 self)
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We introduce a novel frequent pattern mining approach to discover leaders and tribes in social networks. In particular, we consider social networks where users perform actions. Actions may be as simple as tagging resources (urls) as in del.icio.us, rating songs as in Yahoo! Music, or movies as in Yahoo! Movies, or users buying gadgets such as cameras, handhelds, etc. and blogging a review on the gadgets. The assumption is that actions performed by a user can be seen by their network friends. Users seeing their friends ’ actions are sometimes tempted to perform those actions. We are interested in the problem of studying the propagation of such “influence”, and on this basis, identifying which users are leaders when it comes to setting the trend for performing various actions. We consider alternative definitions of leaders based on frequent patterns and develop algorithms for their efficient discovery. Our definitions are based on observing the way influence propagates in a time window, as the window is moved in time. Given a social graph and a table of user actions, our algorithms can discover leaders of various flavors by making one pass over the actions table. We run detailed experiments to evaluate the utility and scalability of our algorithms on real-life data. The results of our experiments confirm on the one hand, the efficiency of the proposed algorithm, and on the other hand, the effectiveness and relevance of the overall framework. To the best of our knowledge, this the first frequent pattern based approach to social network mining.
The pulse of news in social media: Forecasting popularity
- In ICWSM
, 2012
"... News articles are extremely time sensitive by nature. There is also intense competition among news items to propagate as widely as possible. Hence, the task of pre-dicting the popularity of news items on the social web is both interesting and challenging. Prior research has dealt with predicting eve ..."
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Cited by 27 (0 self)
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News articles are extremely time sensitive by nature. There is also intense competition among news items to propagate as widely as possible. Hence, the task of pre-dicting the popularity of news items on the social web is both interesting and challenging. Prior research has dealt with predicting eventual online popularity based on early popularity. It is most desirable, however, to predict the popularity of items prior to their release, fostering the possibility of appropriate decision mak-ing to modify an article and the manner of its publi-cation. In this paper, we construct a multi-dimensional feature space derived from properties of an article and evaluate the efficacy of these features to serve as predic-tors of online popularity. We examine both regression and classification algorithms and demonstrate that de-spite randomness in human behavior, it is possible to predict ranges of popularity on twitter with an overall 84 % accuracy. Our study also serves to illustrate the differences between traditionally prominent sources and those immensely popular on the social web. 1
Graph Mining Applications to Social Network Analysis.
- Managing and Mining Graph Data
, 2010
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Blogging at work and the corporate attention economy
- in SIGCHI Conference on Human Factors in Computing. 2009
"... The attention economy motivates participation in peerproduced sites on the Web like YouTube and Wikipedia. However, this economy appears to break down at work. We studied a large internal corporate blogging community using log files and interviews and found that employees expected to receive attenti ..."
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Cited by 18 (2 self)
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The attention economy motivates participation in peerproduced sites on the Web like YouTube and Wikipedia. However, this economy appears to break down at work. We studied a large internal corporate blogging community using log files and interviews and found that employees expected to receive attention when they contributed to blogs, but these expectations often went unmet. Like in the external blogosphere, a few people received most of the attention, and many people received little or none. Employees expressed frustration if they invested time and received little or no perceived return on investment. While many corporations are looking to adopt Web-based communication tools like blogs, wikis, and forums, these efforts will fail unless employees are motivated to participate and contribute content. We identify where the attention economy breaks down in a corporate blog community and suggest mechanisms for improvement. Author Keywords Blogging, blog readers, attention economy, workplace,
Studying scientific discourse on the Web using bibliometrics: A chemistry blogging case study. Presented at the WebSci10: Extending the Frontiers of Society On-Line. Raleigh, NC. Retrieved from http://wiki.few.vu.nl/sms/images/9/9c/Websci10-FINAL-29-4-201
- First Monday
, 2010
"... Scientific discourse occurs both in the academic literature and, increasingly, on the Web. What is discussed in the literature influences what is discussed on the web, and the reverse. However, the study of this discourse has largely been isolated based on medium either using bibliometrics for acade ..."
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Cited by 17 (2 self)
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Scientific discourse occurs both in the academic literature and, increasingly, on the Web. What is discussed in the literature influences what is discussed on the web, and the reverse. However, the study of this discourse has largely been isolated based on medium either using bibliometrics for academic literature or webometrics for Web-based communication. In this work, the science blog aggregator Researchblogging.org is used to enable the study of scientific discourse on the Web using
A Classification for Community Discovery Methods in Complex Networks
, 2011
"... Many real-world networks are intimately organized according to a community structure. Much research effort has been devoted to develop methods and algorithms that can efficiently highlight this hidden structure of a network, yielding a vast literature on what is called today community detection. S ..."
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Cited by 16 (6 self)
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Many real-world networks are intimately organized according to a community structure. Much research effort has been devoted to develop methods and algorithms that can efficiently highlight this hidden structure of a network, yielding a vast literature on what is called today community detection. Since network representation can be very complex and can contain different variants in the traditional graph model, each algorithm in the literature focuses on some of these properties and establishes, explicitly or implicitly, its own definition of community. According to this definition, each proposed algorithm then extracts the communities, which typically reflect only part of the features of real communities. The aim of this survey is to provide a ‘user manual’ for the community discovery problem. Given a meta definition of what a community in a social network is, our aim is to organize the main categories of community discovery methods based on the definition of community they adopt. Given a desired definition of community and the features of a problem (size of network, direction of edges, multidimensionality, and so on) this review paper is designed to provide a set of approaches that researchers could focus on. The proposed classification of community discovery methods is also useful for putting into perspective the many open
Exploiting Vulnerability to Secure User Privacy on Social Networking Site
"... Social media gives users an efficient way to communicate and network with each other on an unprecedented scale and at rates unseen in traditional media. As his social network expands, a user’s privacy protection goes beyond his privacy setting and becomes a social networking problem. In this researc ..."
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Cited by 16 (4 self)
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Social media gives users an efficient way to communicate and network with each other on an unprecedented scale and at rates unseen in traditional media. As his social network expands, a user’s privacy protection goes beyond his privacy setting and becomes a social networking problem. In this research, we aim to address some critical issues related to privacy protection: Would the highest privacy setting guarantee a secure protection? Given the open nature of a social networking site, is it possible to manage one’s privacy protection? With the diversity of one’s social media friends, how can one figure out an effective approach to balance between vulnerability and privacy? We present a novel way to define a vulnerable friend from an individual user’s perspective as dependent on whether or not the user’s friends’ security and privacy settings protect the friend and the individual’s network of friends (which includes the user). A single vulnerable friend in a user’s social network can place all friends at risk. Using experiments, we demonstrate how much security an individual user can improve by unfriending a vulnerable friend. We also show how security and privacy weakens if newly accepted friends are unguarded or unprotected. This work provides a large-scale evaluation of new security and privacy indexes using a Facebook dataset. A new perspective for reasoning about social networking security is presented and discussed. When a user accepts a new friend, the user should ensure the new friend is not an increased security risk with the potential of negatively impacting the entire friend network. Additionally, by leveraging the indexes proposed and employing new strategies for unfriending vulnerable friends, it is possible to further improve security and privacy without changing the social networking site’s existing architecture.
Social network analysis and mining for business applications
- ACM Trans. Intell. Syst. Technol
"... Social network analysis has gained significant attention in recent years, largely due to the success of online social networking and media-sharing sites, and the consequent availability of a wealth of social network data. In spite of the growing interest, however, there is little understanding of th ..."
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Cited by 14 (1 self)
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Social network analysis has gained significant attention in recent years, largely due to the success of online social networking and media-sharing sites, and the consequent availability of a wealth of social network data. In spite of the growing interest, however, there is little understanding of the potential business applications of mining social networks. While there is a large body of research on different problems and methods for social network mining, there is a gap between the techniques developed by the research community and their deployment in real-world applications. Therefore the potential business impact of these techniques is still largely unexplored. In this article we use a business process classification framework to put the research topics in a business context and provide an overview of what we consider key problems and techniques in social network analysis and mining from the perspective of business applications. In particular, we discuss data acquisition and preparation, trust, expertise, community structure, network dynamics, and information propagation. In each case we present a brief overview of the problem, describe state-of-the art approaches, discuss business application examples, and map each of the topics to a business process classification framework. In addition, we provide insights on prospective business applications, challenges, and future research directions. The main contribution of this article is to provide a state-of-the-art overview of current techniques while providing a critical perspective on business applications of social network analysis and mining.
User reputation in a comment rating environment
- In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD’11
, 2011
"... Reputable users are valuable assets of a web site. We focus on user reputation in a comment rating environment, where users make comments about content items and rate the comments of one another. Intuitively, a reputable user posts high quality comments and is highly rated by the user community. To ..."
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Cited by 13 (2 self)
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Reputable users are valuable assets of a web site. We focus on user reputation in a comment rating environment, where users make comments about content items and rate the comments of one another. Intuitively, a reputable user posts high quality comments and is highly rated by the user community. To our surprise, we find that the quality of a comment judged editorially is almost uncorrelated with the ratings that it receives, but can be predicted using standard text features, achieving accuracy as high as the agreement between two editors! However, extracting a pure reputation signal from ratings is difficult because of data sparseness and several confounding factors in users ’ voting behavior. To address these issues, we propose a novel bias-smoothed tensor model and empirically show that our model significantly outperforms a number of alternatives based on Yahoo! News, Yahoo! Buzz and Epinions datasets.
