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. 197-206