Skip to main content
No Access

Video spam and public opinion in current Middle Eastern conflicts

Published Online:pp 318-333https://doi.org/10.1504/IJSNM.2013.059071

Social networks studies aim to discover the public opinions related to products, news, issues, etc. However, the level of trust or credibility of such public opinion evaluations may have the risk of being influenced artificially by opposite groups. Such deception can be easier to accomplish through the web in comparison with real life where face to face verification may challenge such deception methods. In this paper, we evaluated credibility in social networks. YouTube is used as a case study and we studied spamming techniques (e.g., keyword stuffing) that are widely used in this video streaming in order to improve visibility and bring public attention to the subject videos. Results and evaluation showed that currently such streaming applications do not impose any structural techniques on the kind of tagging or keywords that are included in the uploaded videos to ensure that such tags or keywords are relevant to the video content.

Keywords

web spam, video spam, information retrieval, social networks

References

  • 1. Al-Kabi, M. , Wahsheh, H. , Alsmadi, I. , Al-Shawakfa, E. , Wahbeh, A. , Al-Hmoud, A. (2012). ‘Content based analysis to detect Arabic web spam’. Journal of Information Science. (accessed 19 April 2012), 38, 3, 284-296 Google Scholar
  • 2. Ammari, A. , Dimitrova, V. , Despotakis, D. (2011). ‘Semantically enriched machine learning approach to filter YouTube comments for socially augmented user models’. in Proceedings of the International Workshop on Augmenting User Models with Real World Experiences to Enhance Personalization and Adaptation (UMAP2011). 15 July, Girona, Spain, 1-12 Google Scholar
  • 3. Benevenuto, F. , Duarte, F. , Rodrigues, T. , Almeida, V. , Almeida, J. , Ross, K. (2008a). ‘Understanding video interactions in YouTube’. in Proceedings of the 16th ACM international conference on Multimedia (MM ‘08). 26–31 October, Vancouver, Canada, 761-764 Google Scholar
  • 4. Benevenuto, F. , Rodrigues, T. , Almeida, V. , Almeida, J. , Zhang, C. , Ross, K. (2008b). ‘Identifying video spammers in online social networks’. in 4th International Workshop on Adversarial Information Retrieval on the Web (AIRWeb 2008), 21–25 April, Beijing, China, 1-8 Google Scholar
  • 5. Benevenuto, F. , Rodrigues, T. , Almeida, V. , Almeida, J. , Gonçalves, M. (2009). ‘Detecting spammers and content promoters in online video social networks’. in Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ‘09). 19–23 July, Boston, MA, USA, 620-627 Google Scholar
  • 6. Brown, G. , Howe, T. , Ihbe, M. , Prakash, A. , Borders, K. (2008). ‘Social networks and context-aware spam’. in Proceedings of the 2008 ACM Conference on Computer Supported Cooperative Work (CSCW ‘08). 8–12 November, ACM, San Diego, CA, USA, 403-412 Google Scholar
  • 7. Cha, M. , Kwak, H. , Rodriguez, P. , Ahn, Y. , Moon, S. (2007). ‘I tube, you tube, everybody tubes: analyzing the world’s largest user generated content video system’. in Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement (IMC ‘07). 24–26 October, San Diego, California, USA, 1-14 Google Scholar
  • 8. Cherubini, M. , de Oliveira, R. , Oliver, N. (2009). ‘Understanding near-duplicate videos: a user-centric approach’. in Proceedings of the 17th ACM international conference on Multimedia (MM’09). 19–24 October, Beijing, China, 35-44 Google Scholar
  • 9. Da Luz, A. , Valle, E. , Araujo, A. (2011). ‘Content-based spam filtering on video sharing social networks’. The Computing Research Repository (CoRR). 18, 1-4 Google Scholar
  • 10. Freeman, B. , Chapman, S. (2007). ‘Is YouTube telling or selling you something? Tobacco content on the YouTube video-sharing website’. British Medical Journal: Tobbaco Control. 16, 3, 207-210 Google Scholar
  • 11. Geisler, G. , Burns, S. (2007). ‘Tagging video: conventions and strategies of the YouTube community’. in Proceeding of the JCDL ‘07 Proceedings of the 7th ACM/IEEE-CS Joint Conference on Digital Libraries. 18–23 June, Vancouver, British Columbia, Canada, 480-480 Google Scholar
  • 12. Harris, C. (2012). ‘An evaluation of search strategies for user-generated video content’. in CrowdSearch 2012 Workshop at WWW 2012, 17 April, Lyon, France, 48-53 Google Scholar
  • 13. Hess, A. , Klaue, J. (2004). ‘A video-spam detection approach for unprotected multimedia flows based on active networks’. in Proceedings of the 30th EUROMICRO Conference. 31 August–3 September, Rennes, France, 461-465 Google Scholar
  • 14. Heymann, P. , Koutrika, G. , Garcia-Molina, H. (2007). ‘Fighting spam on social web sites: a survey of approaches and future challenges’. IEEE Internet Computing. 11, 6, 36-45 Google Scholar
  • 15. Huang, C. , Fu, T. , Hsinchun, C. (2010). ‘Text-based video content classification for online video-sharing sites’. Journal of the American Society for Information Science and Technology. 61, 5, 891-906 Google Scholar
  • 16. Lee, K. , Caverlee, J. , Kamath, K. , Cheng, Z. (2012). ‘Detecting collective attention spam’. in Proceedings of the 2nd Joint WICOW/AIRWeb Workshop on Web Quality (WebQuality ‘12). 16 April, Lyon, France, 48-55 Google Scholar
  • 17. Mustafaraj, E. , Metaxas, P. , Grevet, C. (2009). ‘The use of online videos in the 2008 U.S. Congressional Elections’. International Conference on Computational Science and Engineering (CSE ‘09), 29–31 August, Vancouver, Canada, 320-325 Google Scholar
  • 18. O’Callaghan, D. , Harrigan, M. , Carthy, J. , Cunningham, P. (2012a). ‘Network analysis of recurring YouTube spam campaigns’. The Computing Research Repository (CoRR) 2012. 19, 531-534 Google Scholar
  • 19. O’Callaghan, D. , Harrigan, M. , Carthy, J. , Cunningham, P. (2012b). ‘Identifying discriminating network motifs in YouTube spam’. The Computing Research Repository (CoRR). 19, 1-8 Google Scholar
  • 20. Rodrigues, T. , Benevenuto, F. , Almeida, V. , Almeida, J. , Gonçalves, M. (2010). ‘Equal but different: a contextual analysis of duplicated videos on YouTube’. Journal of the Brazilian Computer Society. 16, 3, 201-214 Google Scholar
  • 21. Sureka, A. (2011). ‘Mining user comment activity for detecting forum spammers in YouTube’. in 1st International Workshop on Usage Analysis and the Web of Data (USEWOD2011) at the 20th International World Wide Web Conference (WWW2011), 28 March–1 April, Hydebarabad, India, 1-4 Google Scholar
  • 22. Thomas, K. , Grier, C. , Paxson, V. (2012). ‘Adapting social spam infrastructure for political censorship’. 5th USENIX Workshop on Large-Scale Exploits and Emergent Threats, 24 April, San Jose, CA, USA Google Scholar
  • 23. Wahsheh, H. , Al-Kabi, M. , Alsmadi, I. (2012a). ‘Evaluating Arabic spam classifiers using link analysis’. The 3rd International Conference on Information and Communication Systems (ICICS 2012), 3–5 April, Irbid, Jordan, 1-5 Google Scholar
  • 24. Wahsheh, H. , Al-Kabi, M. , Alsmadi, I. (2012b). ‘Spam detection methods for Arabic web pages’. in Proceeding of 1st Taibah University International Conference on Computing and Information Technology (ICCIT 2012). 12–14 March, Al-Madinah Al-Munawwarah, Saudi Arabia, 486-490 Google Scholar
  • 25. Wang, Y. , Ma, M. (2007). ‘Strider search ranger: towards an autonomic anti-spam search engine’. in Proceedings of 4th International Conference on Autonomic Computing (ICAC’07). 11–15 June, Jacksonville, Florida, USA, 32 Google Scholar