Matches in ScholarlyData for { ?s ?p ?o. }
- item-3 hasContent ulrike-sattler.
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- 676 creator haewoon-kwak.
- 676 creator hawoong-jeong.
- 676 creator seungyeop-han.
- 676 creator sue-moon.
- 676 creator yong-yeol-ahn.
- 676 type InProceedings.
- 676 label "Analysis of Topological Characteristics of Huge Online Social Networking Services".
- 676 sameAs 676.
- 676 abstract "Social networking services are a fast-growing business in the Internet. However, it is unknown if online relationships and their growth patterns are the same as in real-life social networks. In this paper, we compare the structures of three online social networking services: Cyworld, MySpace, and orkut, each with more than 10 million users, respectively. We have access to complete data of Cyworld's ilchon (friend) relationships and analyze its degree distribution, clustering property, degree correlation, and evolution over time. We also use Cyworld data to evaluate the validity of snowball sampling method, which we use to crawl and obtain partial network topologies of MySpace and orkut. Cyworld, the oldest of the three, demonstrates a changing scaling behavior over time in degree distribution. The latest Cyworld data's degree distribution exhibits a multi-scaling behavior, while those of MySpace and orkut have simple scaling behaviors with different exponents. Very interestingly, each of the two exponents corresponds to the different segments in Cyworld's degree distribution. Certain online social networking services encourage online activities that cannot be easily copied in real life; we show that they deviate from close-knit online social networks which show a similar degree correlation pattern to real-life social networks.".
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- 676 title "Analysis of Topological Characteristics of Huge Online Social Networking Services".
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- item-1 hasContent yong-yeol-ahn.
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- item-5 hasContent hawoong-jeong.
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- 680 creator chengxiang-zhai.
- 680 creator hang-su.
- 680 creator matthew-wondra.
- 680 creator qiaozhu-mei.
- 680 creator xu-ling.
- 680 type InProceedings.
- 680 label "Topic Sentiment Mixture: Modeling Facets and Opinions in Weblogs".
- 680 sameAs 680.
- 680 abstract "In this paper, we define the problem of topic-sentiment analysis on Weblogs and propose a novel probabilistic model to capture the mixture of topics and sentiments simultaneously. The proposed Topic-Sentiment Mixture (TSM) model can reveal the latent topical facets in a Weblog collection, the subtopics in the results of an ad hoc query, and their associated sentiments. It could also provide general sentiment models that are applicable to any ad hoc topics. With a specifically designed HMM structure, the sentiment models and topic models estimated with TSM can be utilized to extract topic life cycles and sentiment dynamics. Empirical experiments on different Weblog datasets show that this approach is effective for modeling the topic facets and sentiments and extracting their dynamics from Weblog collections. The TSM model is quite general; it can be applied to any text collections with a mixture of topics and sentiments, thus has many potential applications, such as search result summarization, opinion tracking, and user behavior prediction.".
- 680 hasAuthorList authorList.
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- 680 title "Topic Sentiment Mixture: Modeling Facets and Opinions in Weblogs".
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- item-1 hasContent qiaozhu-mei.
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- item-2 hasContent xu-ling.
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- item-3 hasContent matthew-wondra.
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- item-4 hasContent hang-su.
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- item-5 hasContent chengxiang-zhai.
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- 686 creator cheng-niu.
- 686 creator hua-jun-zeng.
- 686 creator hua-li.
- 686 creator jian-hu.
- 686 creator zheng-chen.
- 686 type InProceedings.
- 686 label "Demographic Prediction based on User's Browsing Behavior".
- 686 sameAs 686.
- 686 abstract "Demographic information plays an important role in personalized web applications. However, it is usually not easy to obtain this kind of personal data such as age and gender. In this paper, we made a first approach to predict users' gender and age from their Web browsing behaviors, in which the webpage view information is treated as a hidden variable to propagate demographic information between different users. There are three main steps in our approach: First, learning from the web-page click-though data, Web pages are associated with users' (known) age and gender tendency through a discriminative model; Second, users' (unknown) age and gender are predicted from the demographic information of the associated Web pages through a Bayesian framework; Third, based on the fact that Web pages visited by similar users may be associated with similar demographic tendency, and users with similar demographic information would visit similar web pages, a smoothing component is employed to overcome the data sparseness of web click-though log. Experiments are conducted on a real web click-through log to demonstrate the effectiveness of the proposed approach. The experimental results show that the proposed algorithm can achieve up to 30.4% improvements on gender prediction and 50.3% on age prediction in terms of macro F1, comparing with baseline algorithms.".
- 686 hasAuthorList authorList.
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- 686 title "Demographic Prediction based on User's Browsing Behavior".
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- item-1 hasContent jian-hu.