Seeing through Deception: A Computational Approach to Deceit Detection in Spanish Written Communication

Almela, Ángela; Valencia‐García, Rafael; Cantos, Pascual

doi:10.5195/lesli.2013.5

Cited by 31 publications

(33 citation statements)

References 14 publications

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“…The authors explored several strategies for identifying deceptive clues, such as utterance length, LIWC features, lemmas and part of speech patterns. (Almela et al, 2012) studied the deception detection in Spanish text by using SVM classifiers and linguistic categories, obtained from the Spanish version of the LIWC dictionary. A study on Chinese deception is presented in (Zhang et al, 2009), where the authors built a deceptive dataset using Internet news and performed machine learning experiments using a bag-of-words representation to train a classifier able to discriminate between deceptive and truthful cases.…”

Section: Related Workmentioning

confidence: 99%

Cross-cultural Deception Detection

Pérez-Rosas¹,

Mihalcea²

2014

Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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In this paper, we address the task of cross-cultural deception detection. Using crowdsourcing, we collect three deception datasets, two in English (one originating from United States and one from India), and one in Spanish obtained from speakers from Mexico. We run comparative experiments to evaluate the accuracies of deception classifiers built for each culture, and also to analyze classification differences within and across cultures. Our results show that we can leverage cross-cultural information, either through translation or equivalent semantic categories, and build deception classifiers with a performance ranging between 60-70%.

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Section: Related Workmentioning

confidence: 99%

Cross-cultural Deception Detection

Pérez-Rosas¹,

Mihalcea²

2014

Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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“…Previous work on deception detection [16,17,18] has relied on the use of the Linguistic Inquiry and Word Count (LIWC) 6 lexicon, which was originally designed to model psycholinguistic information from verbal communication based on specific word usages.…”

Section: Related Workmentioning

confidence: 99%

Detecting Deception in Political Debates Using Acoustic and Textual Features

Kopev

Ali²,

Koychev

et al. 2019

2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)

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We present work on deception detection, where, given a spoken claim, we aim to predict its factuality. While previous work in the speech community has relied on recordings from staged setups where people were asked to tell the truth or to lie and their statements were recorded, here we use real-world political debates. Thanks to the efforts of fact-checking organizations, it is possible to obtain annotations for statements in the context of a political discourse as true, half-true, or false. Starting with such data from the CLEF-2018 CheckThat! Lab, which was limited to text, we performed alignment to the corresponding videos, thus producing a multimodal dataset. We further developed a multimodal deep-learning architecture for the task of deception detection, which yielded sizable improvements over the state of the art for the CLEF-2018 Lab task 2. Our experiments show that the use of the acoustic signal consistently helped to improve the performance compared to using textual and metadata features only, based on several different evaluation measures. We release the new dataset to the research community, hoping to help advance the overall field of multimodal deception detection.

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“…Apart from the statistical models, there are linguistic models which represents texts written in natural language as the percentage of psychological-relevant words what convey what the text say, and how it says it. LIWC [20] is a tool widely used for conducting opinion mining that has been applied to different domains, such as the financial domain [21], or deceit detection [22]. However, linguistic models present the drawback that they are highly language-dependant so it is difficult to adapt them to other languages.…”

Section: Opinion Miningmentioning

confidence: 99%

Evaluating Information-Retrieval Models and Machine-Learning Classifiers for Measuring the Social Perception towards Infectious Diseases

et al. 2019

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Recent outbreaks of infectious diseases remind us the importance of early-detection systems improvement. Infodemiology is a novel research field that analyzes online information regarding public health that aims to complement traditional surveillance methods. However, the large volume of information requires the development of algorithms that handle natural language efficiently. In the bibliography, it is possible to find different techniques to carry out these infodemiology studies. However, as far as our knowledge, there are no comprehensive studies that compare the accuracy of these techniques. Consequently, we conducted an infodemiology-based study to extract positive or negative utterances related to infectious diseases so that future syndromic surveillance systems can be improved. The contribution of this paper is two-fold. On the one hand, we use Twitter to compile and label a balanced corpus of infectious diseases with 6164 utterances written in Spanish and collected from Central America. On the other hand, we compare two statistical-models: word-grams and char-grams. The experimentation involved the analysis of different gram sizes, different partitions of the corpus, and two machine-learning classifiers: Random-Forest and Sequential Minimal Optimization. The results reach a 90.80% of accuracy applying the char-grams model with five-char-gram sequences. As a final contribution, the compiled corpus is released.

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Seeing through Deception: A Computational Approach to Deceit Detection in Spanish Written Communication

Cited by 31 publications

References 14 publications

Cross-cultural Deception Detection

Cross-cultural Deception Detection

Detecting Deception in Political Debates Using Acoustic and Textual Features

Evaluating Information-Retrieval Models and Machine-Learning Classifiers for Measuring the Social Perception towards Infectious Diseases

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