Antyscam – practical web spam classifier

To avoid of manipulating search engines results by web spam, anti spam system use machine learning techniques to detect spam. However, if the learning set for the system is out of date the quality of classification falls rapidly. We present the web spam recognition system that periodically refreshes the learning set to create an adequate classifier. A new classifier is trained exclusively on data collected during the last period. We have proved that such strategy is better than an incrementation of the learning set. The system solves the starting–up issues of lacks in learning set by minimisation of learning examples and utilization of external data sets. The system was tested on real data from the spam traps and common known web services: Quora, Reddit, and Stack Overflow. The test performed among ten months shows stability of the system and improvement of the results up to 60 percent at the end of the examined period.

Non-collaborative Content Detecting On Video Sharing Social Networks

In this work we are concerned with detecting non-collaborative videos in video sharing social networks. Specifically, we investigate how much visual content-based analysis can aid in detecting ballot stuffing and spam videos in threads of video responses. That is a very challenging task, because of the high-level semantic concepts involved; of the assorted nature of social networks, preventing the use of constrained a priori information; and, which is paramount, of the context-dependent nature of non-collaborative videos. Content filtering for social networks is an increasingly demanded task: due to their popularity, the number of abuses also tends to increase, annoying the user and disrupting their services. We propose two approaches, each one better adapted to a specific non-collaborative action: ballot stuffing, which tries to inflate the popularity of a given video by giving "fake" responses to it, and spamming, which tries to insert a non-related video as a response in popular videos. We endorse the use of low-level features combined into higher-level features representation, like bag-of-visual-features and latent semantic analysis. Our experiments show the feasibility of the proposed approaches. © 2012 Springer Science+Business Media, LLC.70210491067Avila, S., Luz Jr., A., Araújo, A., VSUMM: A simple and efficient approach for automatic video summarization (2008) International Conference on Systems, Signals and Image Processing (IWSSIP' 08), pp. 449-452Benevenuto, F., Rodrigues, T., Almeida, V., Almeida, J., Gonçalves, M., Detecting spammers and content promoters in online video social networks (2009) International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 620-627Blanzieri, E., Bryl, A., A survey of learning-based techniques of email spam filtering (2008) Artif Intell Rev, 29 (1), pp. 63-92Caicedo, J.C., Moreno, J., Niño, E., Gonzalez, F., Combining visual features and text data for medical image retrieval using latent semantic kernels (2010) International Conference on Multimedia Information Retrieval (MIR'10), pp. 359-366Cormack, G., Email spam filtering: A systematic review (2008) Found Trends Inf Retr, 1 (4), pp. 335-455Cortes, C., Vapnik, V., Support-vector networks (1995) Mach Learn, 20 (3), pp. 273-297Crane, R., Sornette, D., Robust dynamic classes revealed by measuring the response function of a social system (2008) Proc Natl Acad Sci, 105 (41), pp. 15649-15653Deselaers, T., Pimenidis, L., Ney, H., Bag-of-visual-words models for adult image classification and filtering (2008) International Conference on Pattern Recognition (ICPR'08), pp. 1-4Gerard, S., Buckley, C., Term-weighting approaches in automatic text retrieval (1988) Inf Process Manag, 24 (5), pp. 513-523Heymann, P., Koutrika, G., Garcia-Molina, H., Fighting spam on social web sites: A survey of approaches and future challenges (2007) IEEE Internet Computing, 11 (6), pp. 36-45. , DOI 10.1109/MIC.2007.125Jiang, Y.-G., Ngo, C.-W., Yang, J., Towards optimal bag-of-features for object categorization and semantic video retrieval (2007) 6th ACM International Conference on Image and Video Retrieval (CIVR'07), pp. 494-501Landauer, T., Foltz, P., Laham, D., Introduction to latent semantic analysis (1998) Discourse Process, 25 (2-3), pp. 259-284Langbehn, H., Ricci, S., Gonçalves, M., Almeida, J., Pappa, G., Benevenuto, F., A multi-view approach for detecting non-cooperative users in online video sharing systems (2010) J Inf Data Manag, 1 (3), pp. 313-328Lee, C.-H., Chiang, K.-C., Latent semantic analysis for classifying scene images (2010) International MultiConference of Engineers and Computer Scientists (IMECS 2010), 2, pp. 1467-1470Lowe, D., Distinctive image features from scale-invariant keypoints (2004) Int J Comput Vis, 60 (2), pp. 91-110Mikolajczyk, K., Schmid, C., A performance evaluation of local descriptors (2005) IEEE Transactions on Pattern Analysis and Machine Intelligence, 27 (10), pp. 1615-1630. , DOI 10.1109/TPAMI.2005.188Sivic, J., Zisserman, A., Video Google: A text retrieval approach to object matching in videos (2003) IEEE International Conference on Computer Vision (ICCV'03), pp. 1470-1477Valle, E., Cord, M., Advanced techniques in CBIR: Local descriptors, visual dictionaries and bags of features (2009) XXII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI'09), Tutorials, pp. 72-78Yanai, K., Barnard, K., Region-based automatic web image selection (2010) International Conference on Multimedia Information Retrieval (MIR'10), pp. 305-312Yang, J., Jiang, Y.-G., Hauptmann, A., Ngo, C.-W., Evaluating bag-of-visual-words representations in scene classification (2007) International Workshop on Multimedia Information Retrieval (MIR'07), pp. 197-20