Parody Detection: An Annotation, Feature Construction, and Classification Approach to the Web of Parody

  • Joshua L. Weese
  • William H. Hsu
  • Jessica C. Murphy
  • Kim Brillante Knight
Chapter
Part of the Multimedia Systems and Applications book series (MMSA)

Abstract

In this chapter, we discuss the problem of how to discover when works in a social media site are related to one another by artistic appropriation, particularly parodies. The goal of this work is to discover concrete link information from texts expressing how this may entail derivative relationships between works, authors, and topics. In the domain of music video parodies, this has general applicability to titles, lyrics, musical style, and content features, but the emphasis in this work is on descriptive text, comments, and quantitative features of songs. We first derive a classification task for discovering the “Web of Parody.” Furthermore, we describe the problems of how to generate song/parody candidates, collect user annotations, and apply machine learning approaches comprising of feature analysis, construction, and selection for this classification task. Finally, we report results from applying this framework to data collected from YouTube and explore how the basic classification task relates to the general problem of reconstructing the web of parody and other networks of influence. This points toward further empirical study of how social media collections can statistically reflect derivative relationships and what can be understood about the propagation of concepts across texts that are deemed interrelated.

Keywords

Parse Tree Class Imbalance Creative Work Video Statistic Relation Extraction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

We thank the anonymous reviewers for helpful comments, and Hui Wang and Niall Rooney for the survey of kernel methods for clustering and classification of text documents in Section “Machine Learning Task: Classification”.

References

  1. Kimono Labs, (2014). Retrieved from Kimono Labs: https://www.kimonolabs.com/
  2. E. Alpaydin, Introduction to Machine Learning, 3rd edn. (MIT Press, Cambridge, 2014)MATHGoogle Scholar
  3. API Overview Guide, (2014). Retrieved from Google Developers: https://developers.google.com/youtube/
  4. D.M. Blei, A.Y. Ng, Latent dirichlet allocation. J. Mach. Learn. Res. 2003(3), 993–1022 (2003)MATHGoogle Scholar
  5. S. Bloehdorn, A. Moschitti, Combined syntactic and semantic kernels for text classification. Adv. Inf. Retr. 4425, 307–318 (2007)CrossRefGoogle Scholar
  6. K. Bontcheva, L. Derczynski, A. Funk, M. A. Greenwood, D. Maynard, N. Aswani, TwitIE: an open-source information extraction pipeline for microblog text, in Proceedings of the International Conference on Recent Advances in Natural Language Processing (2013)Google Scholar
  7. S. Bull, Automatic Parody Detection in Sentiment Analysis (2010)Google Scholar
  8. R. Bunescu, R. Mooney, A shortest path dependency kernel for relation extraction. in Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing(2005), pp. 724–731Google Scholar
  9. C. Burfoot, T. Baldwin, in ACL-IJCNLP, Automatic Satire Detection: Are You Having A Laugh? (Suntec, Singapore, 2009), pp. 161–164Google Scholar
  10. I. Cadez, D. Heckerman, C. Meek, P. Smyth, S. White, Visualization of Navigation Patterns on a Web Site Using Model-Based Clustering, in Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2000), ed. by R. Ramakrishnan, S. J. Stolfo, R. J. Bayardo, I. Parsa (Boston 2000), pp. 280–284Google Scholar
  11. N. Cancedda, E. Gaussier, C. Goutte, J. Renders, Word sequence kernels. J. Mach. Learn. Res. 3, 1059–1082 (2003)MathSciNetMATHGoogle Scholar
  12. D. Caragea, V. Bahirwani, W. Aljandal, W. H. Hsu, Ontology-Based Link Prediction in the Livejournal Social Network, in Proceedings of the 8th Symposium on Abstraction, Reformulation and Approximation (SARA 2009), ed. by V. Bulitko, J. C. Beck, (Lake Arrowhead, CA, 2009)Google Scholar
  13. S. Choudury, J. G. Breslin, User Sentiment Detection: A Youtube Use Case, (2010)Google Scholar
  14. M. Collins, N. Duffy, Convolution kernels for natural language. Adv. Neural Inf. Proces. Syst. 1, 625–632 (2002)Google Scholar
  15. A. Culotta, J. Sorensen, Dependency tree kernels for relation extraction, in Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, (2004), p. 423-esGoogle Scholar
  16. A. Cuzzocrea, I.-Y. Song, K. C. Davis, Analytics over large-scale multidimensional data: the big data revolution!, in Proceedings of the ACM 14th International Workshop on Data Warehousing and On-Line Analytical Processing (DOLAP 2011), ed. by A. Cuzzocrea, I.-Y. Song, K. C. Davis, (ACM Press, Glasgow, 2011) pp. 101–104Google Scholar
  17. R. Dawkins, in The Meme Machine, ed. by S. Blackmore, Foreword, (Oxford: Oxford University Press, 2000). pp. i–xviiGoogle Scholar
  18. R. Dawkins, The Selfiish Gene, 30th edn. (Oxford University Press, Oxford, 2006)Google Scholar
  19. G. Doddington, A. Mitchell, M. Przybocki, L. Ramshaw, S. Strassel, R. Weischedel, The automatic content extraction (ace) program–tasks, data, and evaluation. Proc. LREC 4, 837–840 (2004)Google Scholar
  20. C. Drummond, R. E. Holte, Severe Class Imbalance: Why Better Algorithms aren't the Answer (2012). Retrieved from http://www.csi.uottawa.ca/~cdrummon/pubs/ECML05.pdf
  21. C.E. Elger, K. Lehnertz, Seizure prediction by non-linear time series analysis of brain electrical activity. Eur. J. Neurosci. 10(2), 786–789 (1998)CrossRefGoogle Scholar
  22. W. Elshamy, W. H. Hsu, in Continuous-time infinite dynamic topic models: the dim sum process for simultaneous topic enumeration and formation, ed. by W. H. Hsu, Emerging Methods in Predictive Analytics: Risk Management and Decision-Making (Hershey, IGI Global, 2014), pp. 187–222Google Scholar
  23. U. Gargi, W. Lu, V. Mirrokni, S. Yoon, Large-scale community detection on youtube for topic discovery and exploration, in Proceedings of the 5th International Conference on Weblogs and Social Media, ed. by L. A. Adamic, R. A. Baeza-Yates, S. Counts (Barcelona, Catalonia, 17–21 July 2011)Google Scholar
  24. P. Gill, M. Arlitt, Z. Li, A. Mahanti, YouTube traffic characterization: a view from the edge, in IMC'07: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement (ACM, New York, 2007), pp. 15–28Google Scholar
  25. J. Gleick, The Information: A History, a Theory, a Flood (Pantheon Books, New York, 2011)Google Scholar
  26. J. Goldstein, S. F. Roth, Using aggregation and dynamic queries for exploring large data sets, in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2004), ed. by E. Dykstra-Erickson, M. Tscheligi (ACM Press, Boston, MA, 1994), pp. 23–29Google Scholar
  27. Google. Statistics. (2012). Retrieved from YouTube: http://www.youtube.com/t/press_statistics
  28. M. Hall, E. Frank, G. Holmes, B. Pfahringer, The WEKA Data Mining Software: An Update. ACM SIGKDD Explorations Newsletter 11(1), 10–18 (2009b)CrossRefGoogle Scholar
  29. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, I.H. Witten, The WEKA Data Mining Software: An Update. SIGKDD Explorations 11(1), 10–18 (2009a)CrossRefGoogle Scholar
  30. J. Heer, N. Kong, M. Agrawala, Sizing the horizon: the effects of chart size and layering on the graphical perception of time series visualizations, in Proceedings of the 27th International Conference on Human Factors in Computing Systems (CHI 2009) (ACM Press, Boston, 2009), pp. 1303–1312Google Scholar
  31. E. Hovy, J. Lavid, Towards a ‘Science’ of corpus annotation: a new methodological challenge for corpus linguistics. Int. J. Translat. 22(1), 13–36 (2010). doi: 10.1075/target.22.1 Google Scholar
  32. W. H. Hsu, J. P. Lancaster, M. S. Paradesi, T. Weninger, Structural link analysis from user profiles and friends networks: a feature construction approach, in Proceedings of the 1st International Conference on Weblogs and Social Media (ICWSM 2007), ed. by N. S. Glance, N. Nicolov, E. Adar, M. Hurst, M. Liberman, F. Salvetti (Boulder, CO, 2007), pp. 75–80Google Scholar
  33. H. Jenkins. If it Doesn’t Spread, it’s Dead. (2009). Retrieved 06 16, 2011, from Confessions of an Aca-Fan: The Official Weblog of Henry Jenkins: http://www.henryjenkins.org/2009/02/if_it_doesnt_spread_its_dead_p.html
  34. D. A. Keim, Challenges in visual data analysis, in 10th International Conference on Information Visualisation (IV 2006), ed. by E. Banissi, K. Börner, C. Chen, G. Clapworthy, C. Maple, A. Lobben, … J. Zhang (IEEE Press, London, 2006), pp. 9–16Google Scholar
  35. D. Koller (2001). Representation, reasoning, learning: IJCAI 2001 computers and thought award lecture. Retrieved from Daphne Koller: http://stanford.io/TFV7qH
  36. A. Krishna, J. Zambreno, S. Krishnan, Polarity trend analysis of public sentiment on Youtube, in The 19Tth International Conference on Management of Data (Ahmedabad, 2013)Google Scholar
  37. N. Kumar, E. Keogh, S. Lonardi, C. A. Ratanamahatana, Time-series bitmaps: a practical visualization tool for working with large time series databases, in Proceedings of the 5th SIAM International Conference on Data Mining (SDM 2005) (Newport Beach, CA, 2005), pp. 531–535Google Scholar
  38. M. Liberman. Penn Treebank POS, (2003). Retrieved 2014, from Penn Arts and Sciences: https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html
  39. LiteraryDevices Editors, (2014). Retrieved from Literary Devices: http://literarydevices.net
  40. J. Liu, S. Ali, M. Shah, Recognizing human actions using multiple features, in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008) (2008), pp. 1–8. doi:  10.1109/CVPR.2008.4587527
  41. H. Lodhi, C. Saunders, J. Shawe-Taylor, N. Cristianini, C. Watkins, Text classification using string kernels. J. Mach. Learn. Res. 2, 419–444 (2002)MATHGoogle Scholar
  42. C. D. Manning, M. Surdeanu, J. Bauer, J. Finkel, S. J. Bethard, D. McClosky, The Stanford CoreNLP Natural Language Processing Toolkit, in Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations (2014), pp. 55–60Google Scholar
  43. C. Mario, D. Talia, The knowledge grid. Commun. ACM 46(1), 89–93 (2003)CrossRefGoogle Scholar
  44. A. K. McCallum (2002). Retrieved from MALLET: A Machine Learning for Language Toolkit: http://mallet.cs.umass.edu
  45. A. Mesaros, T. Virtanen, Automatic recognition of lyrics in singing. EURASIP J. Audio, Speech, and Music Processing 2010 (2010). doi: 10.1155/2010/546047
  46. M. Mintz, S. Bills, R. Snow, D. Jurafsky, Distant supervision for relation extraction without labeled data, in Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 (2009), pp. 1003–1011Google Scholar
  47. T.M. Mitchell, Machine learning (McGraw Hill, New York, 1997)MATHGoogle Scholar
  48. M. Monmonier, Strategies for the visualization of geographic time-series data. Cartographica: Int. J. Geogr. Inf. Geovisualization 27(1), 30–45 (1990)CrossRefGoogle Scholar
  49. A. Moschitti, A study on convolution kernels for shallow semantic parsing, in Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics (2004), p. 335-esGoogle Scholar
  50. A. Moschitti, Syntactic kernels for natural language learning: the semantic role labeling case, in Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers on XX (2006). (pp. 97–100)Google Scholar
  51. A. Moschitti, Kernel methods, syntax and semantics for relational text categorization, in Proceeding of the 17th ACM Conference on Information and Knowledge Management (2008), pp. 253–262Google Scholar
  52. A. Moschitti, D. Pighin, R. Basili, Tree kernels for semantic role labeling. Comput. Linguist. 34(2), 193–224 (2008)MathSciNetCrossRefGoogle Scholar
  53. J. C. Murphy, W. H. Hsu, W. Elshamy, S. Kallumadi, S. Volkova, Greensickness and HPV: a comparative analysis?, in New Technologies in Renaissance Studies II, ed. by T. Gniady, K. McAbee, J. C. Murphy, vol. 4 (Toronto and Tempe, AZ, USA: Iter and Arizona Center for Medieval and Renaissance Studies, 2014), pp. 171–197Google Scholar
  54. K.P. Murphy, Machine Learning: A Probabilistic Perspective (MIT Press, Cambridge, 2012)MATHGoogle Scholar
  55. T. Nguyen, A. Moschitti, G. Riccardi, Convolution kernels on constituent, dependency and sequential structures for relation extraction, in Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: volume 3 (2009), pp. 1378–1387Google Scholar
  56. T. O'reilly, What Is Web 2.0 (O'Reilly Media, Sebastopol, 2009)Google Scholar
  57. A. Reyes, P. Rosso, T. Veale, A multidemensional approach for detecting irony in twitter. Lang. Resour. Eval. 47(1), 239–268 (2012)CrossRefGoogle Scholar
  58. J. Selden, Table Talk: Being the Discourses of John Selden. London: Printed for E. Smith (1689)Google Scholar
  59. J. Shawe-Taylor, N. Cristianini, An Introduction to Support Vector Machines: And Other Kernel-Based Learning Methods (Cambridge University Press, Cambridge, 2004)MATHGoogle Scholar
  60. S. C. Siersdorfer, How useful are your comments?-Analyzing and predicting Youtube comments and comment ratings, in Proceedings of the 19th International Conference on World Wide Web, vol. 15 (2010), pp. 897–900Google Scholar
  61. V. Simmonet, Classifying Youtube channels: a practical system, in Proceedings of the 22nd International Conference on World Wibe Web Companion (2013), pp. 1295–1303Google Scholar
  62. J. Steele, N. Iliinsky (eds.), Beautiful Visualization: Looking at Data Through the Eyes of Experts (O'Reilly Media, Cambridge, 2010)Google Scholar
  63. L. A. Trindade, H. Wang, W. Blackburn, N. Rooney, Text classification using word sequence kernel methods, in Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC 2011) (Guilin, 2011), pp. 1532–1537Google Scholar
  64. L. A. Trindade, H. Wang, W. Blackburn, P. S. Taylor, Enhanced factored sequence kernel for sentiment classification, in Proceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies (WI-IAT 2014) (2014), pp. 519–525Google Scholar
  65. O. Tsur, D. Davidov, A. Rappoport, in ICWSN—A Great Catchy Name: Semi-Supervised Recognition of Sarcastic Sentences in Online Product Reviews, AAAI (2010)Google Scholar
  66. M. Wang, A re-examination of dependency path kernels for relation extraction, in Proceedings of IJCNLP (2008), 8Google Scholar
  67. H.J. Watson, B.H. Wixom, The current state of business intelligence. IEEE Comput. 40(9), 96–99 (2007)CrossRefGoogle Scholar
  68. T. Watt, Cheap Print and Popular Piety, 1550–1640 (Cambridge University Press, Cambridge, 1991)Google Scholar
  69. M. Wattenhofer, R. Wattenhofer, Z. Zhu, The YouTube Social Network, in Sixth International AAAI Conference on Weblogs and Social Media (2012), pp. 354–361Google Scholar
  70. J. L. Weese, in Emerging Methods in Predictive Analytics: Risk Management and Decision-Making, ed. by W. H. Hsu, Predictive analytics in digital signal processing: a convolutive model for polyphonic instrument identification and pitch detection using combined classification. (Hershey: IGI Global, 2014), pp. 223–253Google Scholar
  71. M. Yang, W. H. Hsu, S. Kallumadi, in Emerging Methods in Predictive Analytics: Risk Management and Decision-Making, ed. by W. H. Hsu, Predictive analytics of social networks: a survey of tasks and techniques (Hershey: IGI Global, 2014), pp. 297–333Google Scholar
  72. W. Yang, G. Toderici, Discriminative tag learning on Youtube videos with latent sub-tags. CVPR, (2011), pp. 3217–3224Google Scholar
  73. H. Yoganarasimhan. (2012). Impact of Social Network Structure on Content Propagation: A Study Using Youtube Data. Retrieved from: http://faculty.gsm.ucdavis.edu/~hema/youtube.pdf
  74. D. Zelenko, C. Aone, A. Richardella, Kernel methods for relation extraction. J. Mach. Learn. Res. 3, 1083–1106 (2003)MathSciNetMATHGoogle Scholar
  75. M. Zhang, J. Zhang, J. Su, G. Zhou, A composite kernel to extract relations between entities with both flat and structured features, in Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics (2006), pp. 825–832Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Joshua L. Weese
    • 1
  • William H. Hsu
    • 1
  • Jessica C. Murphy
    • 2
  • Kim Brillante Knight
    • 3
  1. 1.Department of Computer ScienceKansas State UniversityManhattanUSA
  2. 2.School of Arts and HumanitiesUniversity of Texas at DallasRichardsonUSA
  3. 3.School of Arts, Technology, and Emerging CommunicationUniversity of Texas at DallasRichardsonUSA

Personalised recommendations