Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
A Comparison Among Significance Tests and Other Feature Building Methods for Sentiment Analysis: A First Study Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Detecting Sockpuppets in Deceptive Opinion Spam

verfasst von : Marjan Hosseinia, Arjun Mukherjee

Erschienen in: Computational Linguistics and Intelligent Text Processing

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

This paper explores the problem of sockpuppet detection in deceptive opinion spam using authorship attribution and verification approaches. Two methods are explored. The first is a feature subsampling scheme that uses the KL-Divergence on stylistic language models of an author to find discriminative features. The second is a transduction scheme, spy induction that leverages the diversity of authors in the unlabeled test set by sending a set of spies (positive samples) from training set to retrieve hidden samples in the unlabeled test set using nearest and farthest neighbors. Experiments using ground truth sockpuppet data show the effectiveness of the proposed schemes.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Comment Relevance Classification in Facebook
Nächstes Kapitel Reading the Author and Speaker: Towards a Holistic and Deep Approach on Automatic Assessment of What is in One’s Words
Fußnoten
Literatur
1.
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. Association for Computational Linguistics (2011)
2.
Feng, S., Banerjee, R., Choi, Y.: Syntactic stylometry for deception detection. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers, vol. 2, pp. 171–175. Association for Computational Linguistics (2012)
3.
Mukherjee, A., et al.: Spotting opinion spammers using behavioral footprints. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 632–640. ACM (2013)
4.
Lim, E.P., Nguyen, V.A., Jindal, N., Liu, B., Lauw, H.W.: Detecting product review spammers using rating behaviors. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 939–948. ACM (2010)
5.
Li, H., Chen, Z., Mukherjee, A., Liu, B., Shao, J.: Analyzing and detecting opinion spam on a large-scale dataset via temporal and spatial patterns. In: Ninth International AAAI Conference on Web and Social Media (2015)
6.
Mukherjee, A., Liu, B., Glance, N.: Spotting fake reviewer groups in consumer reviews. In: Proceedings of the 21st International Conference on World Wide Web, pp. 191–200. ACM (2012)
7.
Stamatatos, E.: A survey of modern authorship attribution methods. J. Am. Soc. Inf. Sci. Technol. 60, 538–556 (2009)CrossRef
8.
Layton, R., Watters, P., Dazeley, R.: Authorship attribution for twitter in 140 characters or less. In: 2010 Second Cybercrime and Trustwor-thy Computing Workshop (CTC), pp. 1–8. IEEE (2010)
9.
Luyckx, K., Daelemans, W.: Authorship attribution and verification with many authors and limited data. In: Proceedings of the 22nd International Conference on Computa-tional Linguistics, vol. 1, pp. 513–520. Association for Compu-tational Linguistics (2008)
10.
Koppel, M., Schler, J.: Authorship verification as a one-class classification problem. In: Proceedings of the Twenty-first International Conference on Machine Learning, p. 62. ACM (2004)
11.
12.
Chapelle, O., Zien, A.: Semi-supervised classification by low density separation. In: AISTATS, pp. 57–64 (2005)
13.
Joachims, T.: Transductive inference for text classification using support vector machines. In: ICML, vol. 99, pp. 200–209 (1999)
14.
Graham, N., Hirst, G., Marthi, B.: Segmenting documents by stylistic character. Nat. Lang. Eng. 11, 397–415 (2005)CrossRef
15.
Gamon, M.: Linguistic correlates of style: authorship classification with deep linguistic analysis features. In: Proceedings of the 20th International Conference on Computational Linguistics, p. 611. Association for Computational Linguistics (2004)
16.
Sapkota, U., Bethard, S., Montes-y Gómez, M., Solorio, T.: Not all character n-grams are created equal: a study in authorship attribution. In: Human Language Technologies: The 2015 Annual Conference of the North American Chapter of the ACL, pp. 93–102 (2015)
17.
Qian, T., et al.: Tri-training for authorship attribution with limited training data: a comprehensive study. Neurocomputing 171, 798–806 (2016)CrossRef
18.
Seroussi, Y., Bohnert, F., Zukerman, I.: Authorship attribution with author-aware topic models. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers, vol. 2, pp. 264–269. Association for Computational Linguistics (2012)
19.
Koppel, M., Winter, Y.: Determining if two documents are written by the same author. J. Assoc. Inf. Sci. Technol. 65, 178–187 (2014)CrossRef
20.
Sanderson, C., Guenter, S.: Short text authorship attribution via sequence kernels, Markov chains and author unmasking: an investigation. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 482–491. Association for Computational Linguistics (2006)
21.
Solorio, T., Hasan, R., Mizan, M.: A case study of sockpuppet detection in wikipedia. In: Workshop on Language Analysis in Social Media (LASM) at NAACL HLT. pp. 59–68 (2013)
22.
Qian, T., Liu, B.: Identifying multiple userids of the same author. In: EMNLP, pp. 1124–1135 (2013)
23.
Jindal, N., Liu, B.: Opinion spam and analysis. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 219–230. ACM (2008)
24.
25.
Xie, S., Wang, G., Lin, S., Yu, P.S.: Review spam detection via temporal pattern discovery. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 823–831. ACM (2012)
26.
Gokhman, S., Hancock, J., Prabhu, P., Ott, M., Cardie, C.: In search of a gold standard in studies of deception. In: Proceedings of the Workshop on Computational Approaches to Deception Detection, pp. 23–30. Association for Computational Linguistics (2012)
27.
Li, J., Ott, M., Cardie, C., Hovy, E.H.: Towards a general rule for identifying deceptive opinion spam. In: ACL (1), Citeseer, pp. 1566–1576 (2014)
28.
Li, J., Ott, M., Cardie, C.: Identifying manipulated offerings on review portals. In: EMNLP, pp. 1933–1942 (2013)
29.
Banerjee, R., Feng, S., Kang, J.S., Choi, Y.: Keystroke patterns as prosody in digital writings: a case study with deceptive reviews and essays. In: Empirical Methods on Natural Language Processing (EMNLP) (2014)
30.
Mukherjee, A., Venkataraman, V., Liu, B., Glance, N.S.: What yelp fake review filter might be doing? In: ICWSM (2013)
31.
32.
33.
34.
Feng, S., Banerjee, R., Choi, Y.: Characterizing stylistic elements in syntactic structure. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 1522–1533. Association for Computational Linguistics (2012)
35.
Weinberger, K.Q., Blitzer, J., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. In: Advances in Neural Information Processing Systems, pp. 1473–1480 (2005)
36.
Xu, X., Li, W., Xu, D., Tsang, I.: Co-labeling for multi-view weakly labeled learning. IEEE Trans. Pattern Anal. Mach. Intell. 38(6), 1113–1125 (2015)CrossRef
37.
Solorio, T., Hasan, R., Mizan, M.: Sockpuppet detection in wikipedia: a corpus of real-world deceptive writing for linking identities. In: Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC 2014), Reykjavik, Iceland, European Language Resources Association (ELRA) (2014)
Metadaten
Titel
Detecting Sockpuppets in Deceptive Opinion Spam
verfasst von
Marjan Hosseinia
Arjun Mukherjee
Copyright-Jahr
2018
Buch
Computational Linguistics and Intelligent Text Processing

Print ISBN: 978-3-319-77115-1

Electronic ISBN: 978-3-319-77116-8

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-77116-8_19