Character N-Grams for Detecting Deceptive Controversial Opinions

[EN] Controversial topics are present in the everyday life, and opinions about them can be either truthful or deceptive. Deceptive opinions are emitted to mislead other people in order to gain some advantage. In the most of the cases humans cannot detect whether the opinion is deceptive or truthful, however, computational approaches have been used successfully for this purpose. In this work, we evaluate a representation based on character n-grams features for detecting deceptive opinions. We consider opinions on the following: abortion, death penalty and personal feelings about the best friend; three domains studied in the state of the art. We found character n-grams effective for detecting deception in these controversial domains, even more than using psycholinguistic features. Our results indicate that this representation is able to capture relevant information about style and content useful for this task. This fact allows us to conclude that the proposed one is a competitive text representation with a good trade-off between simplicity and performance.We would like to thank CONACyT for partially supporting this work under grants 613411, CB-2015-01-257383, and FC-2016/2410. The work of the last author was partially funded by the Spanish MINECO under the research project SomEMBED (TIN2015-71147-C2-1-P).Sánchez-Junquera, JJ.; Luis Villaseñor Pineda; Montes Gomez, M.; Rosso, P. (2018). Character N-Grams for Detecting Deceptive Controversial Opinions. Lecture Notes in Computer Science. 11018:135-140. https://doi.org/10.1007/978-3-319-98932-7_13S13514011018Aritsugi, M., et al.: Combining word and character n-grams for detecting deceptive opinions, vol. 1, pp. 828–833. IEEE (2017)Buller, D.B., Burgoon, J.K.: Interpersonal deception theory. Commun. Theory 6(3), 203–242 (1996)Cagnina, L.C., Rosso, P.: Detecting deceptive opinions: intra and cross-domain classification using an efficient representation. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 25(Suppl. 2), 151–174 (2017)Feng, S., Banerjee, R., Choi, Y.: Syntactic stylometry for deception detection, pp. 171–175. Association for Computational Linguistics (2012)Fusilier, D.H., Montes-y-Gómez, M., Rosso, P., Cabrera, R.G.: Detection of opinion spam with character n-grams. In: Gelbukh, A. (ed.) CICLing 2015. LNCS, vol. 9042, pp. 285–294. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18117-2_21Hernández-Castañeda, Á., Calvo, H., Gelbukh, A., Flores, J.J.G.: Cross-domain deception detection using support vector networks. Soft Comput. 21(3), 1–11 (2016)Mihalcea, R., Strapparava, C.: The lie detector: explorations in the automatic recognition of deceptive language. In: Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, pp. 309–312. Association for Computational Linguistics (2009)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-Volume 1, pp. 309–319. Association for Computational Linguistics (2011)Pérez-Rosas, V., Mihalcea, R.: Cross-cultural deception detection. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), vol. 2, pp. 440–445 (2014)Sapkota, U., Solorio, T., Montes-y-Gómez, M., Bethard, S.: Not all character n-grams are created equal: a study in authorship attribution. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 93–102 (2015)Vrij, A.: Detecting Lies and Deceit: Pitfalls and Opportunities. Wiley, Hoboken (2008

Knowledge Base Population using Semantic Label Propagation

A crucial aspect of a knowledge base population system that extracts new facts from text corpora, is the generation of training data for its relation extractors. In this paper, we present a method that maximizes the effectiveness of newly trained relation extractors at a minimal annotation cost. Manual labeling can be significantly reduced by Distant Supervision, which is a method to construct training data automatically by aligning a large text corpus with an existing knowledge base of known facts. For example, all sentences mentioning both 'Barack Obama' and 'US' may serve as positive training instances for the relation born_in(subject,object). However, distant supervision typically results in a highly noisy training set: many training sentences do not really express the intended relation. We propose to combine distant supervision with minimal manual supervision in a technique called feature labeling, to eliminate noise from the large and noisy initial training set, resulting in a significant increase of precision. We further improve on this approach by introducing the Semantic Label Propagation method, which uses the similarity between low-dimensional representations of candidate training instances, to extend the training set in order to increase recall while maintaining high precision. Our proposed strategy for generating training data is studied and evaluated on an established test collection designed for knowledge base population tasks. The experimental results show that the Semantic Label Propagation strategy leads to substantial performance gains when compared to existing approaches, while requiring an almost negligible manual annotation effort.Comment: Submitted to Knowledge Based Systems, special issue on Knowledge Bases for Natural Language Processin