Advertisement

Misleading Metadata Detection on YouTube

  • Priyank Palod
  • Ayush Patwari
  • Sudhanshu Bahety
  • Saurabh Bagchi
  • Pawan GoyalEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11438)

Abstract

YouTube is the leading social media platform for sharing videos. As a result, it is plagued with misleading content that includes staged videos presented as real footages from an incident, videos with misrepresented context and videos where audio/video content is morphed. We tackle the problem of detecting such misleading videos as a supervised classification task. We develop UCNet - a deep network to detect fake videos and perform our experiments on two datasets - VAVD created by us and publicly available FVC [8]. We achieve a macro averaged F-score of 0.82 while training and testing on a 70:30 split of FVC, while the baseline model scores 0.36. We find that the proposed model generalizes well when trained on one dataset and tested on the other.

Notes

Acknowledgement

This material is based in part upon work supported by a Google Faculty Award to Saurabh. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsor.

References

  1. 1.
    Ammari, A., Dimitrova, V., Despotakis, D.: Semantically enriched machine learning approach to filter YouTube comments for socially augmented user models. In: UMAP, pp. 71–85 (2011)Google Scholar
  2. 2.
    Becker, H., Naaman, M., Gravano, L.: Learning similarity metrics for event identification in social media. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 291–300. ACM (2010)Google Scholar
  3. 3.
    Koutrika, G., Effendi, F.A., Gyöngyi, Z., Heymann, P., Garcia-Molina, H.: Combating spam in tagging systems. In: Proceedings of the 3rd International Workshop on Adversarial Information Retrieval on the Web, pp. 57–64. ACM (2007)Google Scholar
  4. 4.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)Google Scholar
  5. 5.
    Ott, M., Choi, Y., Cardie, C., Hancock, J.T.: Finding deceptive opinion spam by any stretch of the imagination. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 309–319 (2011)Google Scholar
  6. 6.
    Papadopoulos, S.A.: Towards automatic detection of misinformation in social media (2017)Google Scholar
  7. 7.
    Papadopoulou, O., Zampoglou, M., Papadopoulos, S., Kompatsiaris, Y.: Web video verification using contextual cues. In: Proceedings of the 2nd International Workshop on Multimedia Forensics and Security, pp. 6–10. ACM (2017)Google Scholar
  8. 8.
    Papadopoulou, O., Zampoglou, M., Papadopoulos, S., Kompatsiaris, Y., Teyssou, D.: InVID fake video corpus v2.0, January 2018.  https://doi.org/10.5281/zenodo.1147958
  9. 9.
    Radulescu, C., Dinsoreanu, M., Potolea, R.: Identification of spam comments using natural language processing techniques. In: 2014 IEEE International Conference on Intelligent Computer Communication and Processing, ICCP, pp. 29–35. IEEE (2014)Google Scholar
  10. 10.
    Viswanath, B., et al.: Towards detecting anomalous user behavior in online social networks. In: 23rd USENIX Security Symposium, USENIX Security 2014, pp. 223–238 (2014)Google Scholar
  11. 11.
    Wang, A.H.: Don’t follow me: spam detection in Twitter. In: Proceedings of the 2010 International Conference on Security and Cryptography, SECRYPT, pp. 1–10. IEEE (2010)Google Scholar
  12. 12.
    Zhao, Z., Resnick, P., Mei, Q.: Enquiring minds: early detection of rumors in social media from enquiry posts. In: Proceedings of the 24th International Conference on World Wide Web. International WWW Conferences Steering Committee (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Priyank Palod
    • 1
  • Ayush Patwari
    • 2
  • Sudhanshu Bahety
    • 3
  • Saurabh Bagchi
    • 2
  • Pawan Goyal
    • 1
    Email author
  1. 1.IIT KharagpurKharagpurIndia
  2. 2.Purdue UniversityWest LafayetteUSA
  3. 3.Salesforce.comSan FranciscoUSA

Personalised recommendations