User grouping behavior in online forums

Shi, Xiaolin; Zhu, Jun; Cai, Rui; Zhang, Lei

doi:10.1145/1557019.1557105

Cited by 71 publications

(56 citation statements)

References 26 publications

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“…Supervised learning methods have also been popular for link prediction [15,16]. These methods learn predictive models on labeled training data with a set of manually designed features that capture the statistics of the network.…”

Section: Well-conceived Feature Modelsmentioning

confidence: 99%

Latent feature models for large-scale link prediction

Zhu

Chen

2017

Big Data Anal

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Link prediction is one of the most fundamental tasks in statistical network analysis, for which latent feature models have been widely used. As large-scale networks are available in various application domains, how to develop effective models and scalable algorithms becomes a new challenge. In this paper, we provide a review of the recent progress on latent feature models for the task of link prediction in large-scale networks, including the nonparametric Bayesian models which can automatically infer the latent social dimensions and the max-margin models which can learn strongly discriminative latent features for highly accurate predictions as well as dealing with the imbalance issue in large real networks. We also review the progress on scalable algorithms for posterior inference in such models, including stochastic variational methods and MCMC methods with data augmentation.

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Section: Well-conceived Feature Modelsmentioning

confidence: 99%

Latent feature models for large-scale link prediction

Zhu

Chen

2017

Big Data Anal

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“…Xiaolin Shi and his research group focus to characterizing user grouping behavior in online social environments [7]. This characterizing not only help researchers to understand many of sociological problems of human behavior, also facilitates them to improve various applications in the online environment [8].…”

Section: User Grouping Behaviormentioning

confidence: 99%

Forum Reply Classification Using Clustering

Kalaiarasi¹,

Rooba²

2015

International Journal of Advanced Research in Computer and Comm

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Due to richness of information in forums, researchers are increasingly interested in mining knowledge from forums. From this observation, the forum posts and replies are clustered and analyzed in order to improve the user knowledge in the field. To harvest knowledge from the forum the contents must be downloaded. Forum board or thread is usually divided into multiple pages which are linked by page flipping links. The forum sites contain different pages like entry pages, thread pages and page flipping.The forum mining have three phases: preprocessing, mining the data by applying various data mining strategies such as clustering and post processing. In preprocessing raw data is transformed into a usable format, mainly by parsing and cleaning. While preprocessing, the pages are downloaded as the html file and the files are invoked into parsing and assign attributes like forum id, forum title, thread count, post count. The parsing process is accomplished; data cleaning process is applied to the downloaded post sets and automatically remove noise data and irrelevant data. Clustering algorithm is applied for the preprocessed data to groups the forums into various clusters. The clustering is accomplished by using all topics and sub topics of the forum. The four dimensions of clustering are number of posts/topics, average sentiment values/topics, positive percentage of posts/topics and negative percentage of posts/topics. The posts/topics dimension are determined by number of replies for a post, the sentiment values of this topics are identified from user replies, it describe the user opinion, the positive and negative dimensions are determined from user replies, describe the user perception in the posts. The positive and negative dimensions are also used to identifying the user attitude and pros and cons of the specific topics are discussed in the particular forum. In the post processing stage numbers of clusters are obtained. The obtained final clusters are grouped based on the topics with similar sentiment values and user opinions. Based on the sentiment values, the positive and negative posts are clustered for each thread. Information seekers, decision makers can benefit from this clustering. It simplifies the decision making process.

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“…Ding et al [4] extract contexts and answers for the questions from online forums; Shi et al [15] analyze the user grouping behaviors in online forums; Cong et al [3] detect the question-answer pairs in the threaded discussions.…”

Section: Related Workmentioning

confidence: 99%

Learning online discussion structures by conditional random fields

Wang

Zhai

et al. 2011

Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Online forum discussions are emerging as valuable information repository, where knowledge is accumulated by the interaction among users, leading to multiple threads with structures. Such replying structure in each thread conveys important information about the discussion content. Unfortunately, not all the online forum sites would explicitly record such replying relationship, making it hard for both users and computers to digest the information buried in a discussion thread.In this paper, we propose a probabilistic model in the Conditional Random Fields framework to predict the replying structure for a threaded online discussion. Different from previous replying relation reconstruction methods, most of which fail to consider dependency between the posts, we cast the problem as a supervised structure learning problem to incorporate the features capturing the structural dependency and learn their relationship. Experiment results on three different online forums show that the proposed method can well capture the replying structures in online discussion threads, and multiple tasks such as forum search and question answering can benefit from the reconstructed replying structures.

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User grouping behavior in online forums

Cited by 71 publications

References 26 publications

Latent feature models for large-scale link prediction

Latent feature models for large-scale link prediction

Forum Reply Classification Using Clustering

Learning online discussion structures by conditional random fields

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