The Experts below are selected from a list of 12474 Experts worldwide ranked by ideXlab platform

Yirong Lin - One of the best experts on this subject based on the ideXlab platform.

  • an analysis of network structure and Post content for Blog Post recommendation
    Database Systems for Advanced Applications, 2011
    Co-Authors: Wanshiou Yang, Yirong Lin
    Abstract:

    The acceleration of WeBlogs has increased the perceived information overload for Bloggers attempting to find interested or relevant information. Helping Bloggers to efficiently locate relevant and high-quality information is imperative. In this research, we therefore propose four approaches that exploit the Post citation network, Blog-based social network, and Post content to facilitate the automatic construction of an authoritative Blog Post recommender system. The proposed approaches were tested with Blog data collected from Baidu Space, and the experimental results revealed that the proposed approaches outperform the content-only approach and the explicit citation approach.

  • DASFAA Workshops - An analysis of network structure and Post content for Blog Post recommendation
    Database Systems for Adanced Applications, 2011
    Co-Authors: Wanshiou Yang, Yirong Lin
    Abstract:

    The acceleration of WeBlogs has increased the perceived information overload for Bloggers attempting to find interested or relevant information. Helping Bloggers to efficiently locate relevant and high-quality information is imperative. In this research, we therefore propose four approaches that exploit the Post citation network, Blog-based social network, and Post content to facilitate the automatic construction of an authoritative Blog Post recommender system. The proposed approaches were tested with Blog data collected from Baidu Space, and the experimental results revealed that the proposed approaches outperform the content-only approach and the explicit citation approach.

Anand Mahendran - One of the best experts on this subject based on the ideXlab platform.

  • comment spam classification in Blogs through comment analysis and comment Blog Post relationships
    International Conference on Computational Linguistics, 2012
    Co-Authors: Ashwin Rajadesingan, Anand Mahendran
    Abstract:

    Spamming refers to the process of providing unwanted and irrelevant information to the users. It is a widespread phenomenon that is often noticed in e-mails, instant messages, Blogs and forums. In our paper, we consider the problem of spamming in Blogs. In Blogs, spammers usually target commenting systems which are provided by the authors to facilitate interaction with the readers. Unfortunately, spammers abuse these commenting systems by Posting irrelevant and unsolicited content in the form of spam comments. Thus, we propose a novel methodology to classify comments into spam and non-spam using previously-undescribed features including certain Blog Post-comment relationships. Experiments conducted using our methodology produced a spam detection accuracy of 94.82% with a precision of 96.50% and a recall of 95.80%.

Wanshiou Yang - One of the best experts on this subject based on the ideXlab platform.

  • an analysis of network structure and Post content for Blog Post recommendation
    Database Systems for Advanced Applications, 2011
    Co-Authors: Wanshiou Yang, Yirong Lin
    Abstract:

    The acceleration of WeBlogs has increased the perceived information overload for Bloggers attempting to find interested or relevant information. Helping Bloggers to efficiently locate relevant and high-quality information is imperative. In this research, we therefore propose four approaches that exploit the Post citation network, Blog-based social network, and Post content to facilitate the automatic construction of an authoritative Blog Post recommender system. The proposed approaches were tested with Blog data collected from Baidu Space, and the experimental results revealed that the proposed approaches outperform the content-only approach and the explicit citation approach.

  • DASFAA Workshops - An analysis of network structure and Post content for Blog Post recommendation
    Database Systems for Adanced Applications, 2011
    Co-Authors: Wanshiou Yang, Yirong Lin
    Abstract:

    The acceleration of WeBlogs has increased the perceived information overload for Bloggers attempting to find interested or relevant information. Helping Bloggers to efficiently locate relevant and high-quality information is imperative. In this research, we therefore propose four approaches that exploit the Post citation network, Blog-based social network, and Post content to facilitate the automatic construction of an authoritative Blog Post recommender system. The proposed approaches were tested with Blog data collected from Baidu Space, and the experimental results revealed that the proposed approaches outperform the content-only approach and the explicit citation approach.

Craig Macdonald - One of the best experts on this subject based on the ideXlab platform.

  • is spam an issue for opinionated Blog Post search
    International ACM SIGIR Conference on Research and Development in Information Retrieval, 2009
    Co-Authors: Craig Macdonald, Iadh Ounis, Ian Soboroff
    Abstract:

    In opinion-finding, the retrieval system is tasked with retrieving not just relevant documents, but those that also express an opinion towards the query target entity. This task has been studied in the context of the Blogosphere by groups participating in the 2006-2008 TREC Blog tracks. Spam Blogs (splogs) are thought to be a problem on the Blogosphere. In this paper, we investigate the extent to which spam has affected the participating groups' retrieval systems over the three years of the TREC Blog track opinion-finding task. Our results show that spam can be an issue, with most systems retrieving some spam for every topic. However, removing spam from the rankings does not markedly change the relative performance of opinion-finding approaches.

  • an effective statistical approach to Blog Post opinion retrieval
    Conference on Information and Knowledge Management, 2008
    Co-Authors: Craig Macdonald, Iadh Ounis
    Abstract:

    Finding opinionated Blog Posts is still an open problem in information retrieval, as exemplified by the recent TREC Blog tracks. Most of the current solutions involve the use of external resources and manual efforts in identifying subjective features. In this paper, we propose a novel and effective dictionary-based statistical approach, which automatically derives evidence for subjectivity from the Blog collection itself, without requiring any manual effort. Our experiments show that the proposed approach is capable of achieving remarkable and statistically significant improvements over robust baselines, including the best TREC baseline run. In addition, with relatively little computational costs, our proposed approach provides an effective performance in retrieving opinionated Blog Posts, which is as good as a computationally expensive approach using Natural Language Processing techniques.

  • CIKM - An effective statistical approach to Blog Post opinion retrieval
    Proceeding of the 17th ACM conference on Information and knowledge mining - CIKM '08, 2008
    Co-Authors: Craig Macdonald, Iadh Ounis
    Abstract:

    Finding opinionated Blog Posts is still an open problem in information retrieval, as exemplified by the recent TREC Blog tracks. Most of the current solutions involve the use of external resources and manual efforts in identifying subjective features. In this paper, we propose a novel and effective dictionary-based statistical approach, which automatically derives evidence for subjectivity from the Blog collection itself, without requiring any manual effort. Our experiments show that the proposed approach is capable of achieving remarkable and statistically significant improvements over robust baselines, including the best TREC baseline run. In addition, with relatively little computational costs, our proposed approach provides an effective performance in retrieving opinionated Blog Posts, which is as good as a computationally expensive approach using Natural Language Processing techniques.

Chong Feng - One of the best experts on this subject based on the ideXlab platform.

  • micro Blog Post topic drift detection based on lda model
    Social Informatics, 2013
    Co-Authors: Quanchao Liu, Heyan Huang, Chong Feng
    Abstract:

    Micro-Blog Posts imply a large number of topics, which contain a lot of useful information as well as a lot of junk information making the micro-Blog Post topic a characteristic of high drift. The changes of micro-Blog Post topic over time and noises introduced with the increase of the number of micro-Blog Posts are two main aspects of micro-Blog Post topic drift. We propose a method of topic drift detection based on LDA model, using Gibbs sampling algorithm to obtain the probability distribution of micro-Blog Post words based on words correlation, identifying the topic boundary in dynamic constant method, extracting topic words by computing lexical information entropy in the topic field, and detecting the topic drift by topic words sequence alignment based on discrete-time model. According to the experiment on topic drift detection based on LDA model, we find our method very effective in micro-Blog Post topic drift detection.

  • BSI@PAKDD/BSIC@IJCAI - Micro-Blog Post Topic Drift Detection Based on LDA Model
    Behavior and Social Computing, 2013
    Co-Authors: Quanchao Liu, Heyan Huang, Chong Feng
    Abstract:

    Micro-Blog Posts imply a large number of topics, which contain a lot of useful information as well as a lot of junk information making the micro-Blog Post topic a characteristic of high drift. The changes of micro-Blog Post topic over time and noises introduced with the increase of the number of micro-Blog Posts are two main aspects of micro-Blog Post topic drift. We propose a method of topic drift detection based on LDA model, using Gibbs sampling algorithm to obtain the probability distribution of micro-Blog Post words based on words correlation, identifying the topic boundary in dynamic constant method, extracting topic words by computing lexical information entropy in the topic field, and detecting the topic drift by topic words sequence alignment based on discrete-time model. According to the experiment on topic drift detection based on LDA model, we find our method very effective in micro-Blog Post topic drift detection.