• Publications
    • Conference Papers
    • Workshop Papers
    • Journal Papers
    • Publicity
    • Books
    • Theses
    • Submitted
  • Professional Activities
  • Teaching
  • About
  • Contact

Edgar Meij

semantic search research ッ

  • Publications
    • Conference Papers
    • Workshop Papers
    • Journal Papers
    • Publicity
    • Books
    • Theses
    • Submitted
  • Professional Activities
  • Teaching
  • About
  • Contact
social media icons

A Framework for Unsupervised Spam Detection in Social Networking Sites

23/11/2011 Conference Papers Publications 1 Comment

Social networking sites offer users the option to submit user spam reports for a given message, indicating this message is inappropriate. In this paper we present a framework that uses these user spam reports for spam detection. The framework is based on the HITS web link analysis framework and is instantiated in three models. The models subsequently introduce propagation between messages reported by the same user, messages authored by the same user, and messages with similar content. Each of the models can also be converted to a simple semi-supervised scheme. We test our models on data from a popular social network and compare the models to two baselines, based on message content and raw report counts. We find that our models outperform both baselines and that each of the additions (reporters, authors, and similar messages) further improves the performance of the framework.

  • [PDF] M. Bosma, E. Meij, and W. Weerkamp, “A framework for unsupervised spam detection in social networking sites,” in Advances in information retrieval – 34th european conference on ir research, ecir 2012, 2012.
    [Bibtex]
    @inproceedings{ECIR:2012:bosma,
    Author = {Maarten Bosma and Meij, Edgar and Weerkamp, Wouter},
    Booktitle = {Advances in Information Retrieval - 34th European Conference on IR Research, ECIR 2012},
    Date-Added = {2011-11-23 18:10:33 +0100},
    Date-Modified = {2012-10-28 23:00:37 +0000},
    Title = {A Framework for Unsupervised Spam Detection in Social Networking Sites},
    Year = {2012}}
a-framework-for-unsupervised-spam-detection-in-social-networking-sitesa-geometric-framework-for-unsupervised-spam-detection-in-soical-networkingan-unsuppervised-frameowkr-maarten-bosmabosma-unsupperviseddetection-spam-in-social-network-2012HITSMachine learningonline social network and spammer detection frameworkqueries-on-detecting-spammers-on-social-networkingsemi-supervisedsocial mediasocial-networking-spam-detection-typepdfspamspam detectionspam-detection-in-social-networkspam-detection-modelsspam-detection-on-social-networkspam-in-social-networksunsupervisedunsupervised-spam-detection

Hello world!

Adaptive Temporal Query Modeling

1 thought on “A Framework for Unsupervised Spam Detection in Social Networking Sites”

  1. Pingback: info over social network analysis
Leave a Reply Cancel reply

Time limit is exhausted. Please reload CAPTCHA.

Edgar Meij logo

Welcome!

This is the website of Edgar Meij. I lead several groups of researchers and engineers at Bloomberg working on knowledge graphs, question answering, information retrieval, machine learning, and more…

Search

Tweets by @edgarmeij

Tags

AIDA Artificial Intelligence CLEF DBpedia Document priors edgar-meij entity-linking-and-retrieval entity-linking-and-retrieval-tutorial entity-linking-tutorial Entity finding Entity linking Information retrieval Knowledge base population Knowledge Graph Language modeling Linking Open Data LOD logo-penerbit-buku-internasional Lucene Machine learning meij MeSH Microblogs penerbit-buku-internasional Query log analysis Query modeling Relevance modeling Semanticizing Semantic linking Semantic query analysis Semantic search Teaching Text mining TREC Blog TREC Enterprise TREC Genomics TREC KBA TREC Microblog TREC Relevance Feedback Tutorial Twitter Web services Wikipedia Workflows Workshop
Proudly powered by WordPress | Theme: Doo by ThemeVS.