"Ruspersonality": A Russian corpus for authorship profiling and deception detection

Litvinova, Tatiana; Litvinlova, Olga; Zagorovskaya, Olga V.; Seredin, P. V.; Sboev, Alexander; Romanchenko, Olga

doi:10.1109/fruct.2016.7584767

Cited by 22 publications

(12 citation statements)

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“…2, 2017, pp. 243-252 doi: 10.13053/CyS-21-2-2721 risks of autoaggressive behavior using the corpus RusPersonality [19,20].…”

Section: Discussionmentioning

confidence: 99%

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Identification of Suicidal Tendencies of Individuals based on the Quantitative Analysis of their Internet Texts

Litvinova¹,

Seredin²,

Litvinova³

et al. 2017

CyS

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Even though suicide is one of the top three causes of young people's deaths, no reliable methods of identifying suicidal behavior have been developed. One of the promising directions of research is quantitative analysis of speech. It is nowadays common to process texts by suicidal individuals (mostly suicidal notes or literary texts by famous people, e.g., poets, writes, etc.) and texts by individuals from a control group using software (mostly LIWC) and to design models for classifying texts as those by suicidal individuals or not. This kind of analysis has been mainly performed for English texts that generally have a number of restrictions due to their linguistic nature. The authors are the first to attempt to design a mathematical model to classify texts as those by suicidal or nonsuicidal individuals using numerical values of linguistic parameters as features. Texts (blogs by young people who committed suicides, similar in both genre and topic, to those by individuals of an age-corresponding control group) were processed using the Russian version of LIWC with users' dictionaries. Unlike current studies, in designing the model we mostly made use of features that are not significantly dependent on the content. This is because not all individuals who committed suicides are known to deal with the topic in their texts. The resulting model was shown to be 71.5% accurate, which is comparable with the state-of-the-art for English texts.

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“…2, 2017, pp. 243-252 doi: 10.13053/CyS-21-2-2721 risks of autoaggressive behavior using the corpus RusPersonality [19,20].…”

Section: Discussionmentioning

confidence: 99%

“…Samples of natural written speech from 1,000 students of various Russian universities, all a part of RusPersonality [19] (the first corpus of Russian texts containing wide metadata with the authors' personal information), were used. The average text length was 200 words with a total word count of 198045 words (further on NSUIC).…”

Section: Methodsmentioning

confidence: 99%

Identification of Suicidal Tendencies of Individuals based on the Quantitative Analysis of their Internet Texts

Litvinova¹,

Seredin²,

Litvinova³

et al. 2017

CyS

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“…Not many have been the research works that addressed author profiling in Russian. A corpus for authorship profiling in Russian was created by [30]. Gender identification in Russian texts has been addressed in [31] and in social media in [32].…”

Section: Cross-genre Gender Identification In Russianmentioning

confidence: 99%

“…This dataset is composed of 185 authors per gender, one or two texts per author (in case of two texts they were merged together and considered as one text). The texts were taken randomly from manually collected RusPersonality corpus [30]. RusPersonality is the first Russian-language corpus of written texts labeled with data on their authors.…”

Section: Essay Datasetmentioning

confidence: 99%

Author Profiling Tracks at FIRE

Rosso

Rangel

2020

SN COMPUT. SCI.

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Benchmarking activities are vital for fostering research and addressing new challenging problems. During the last 10 years of the FIRE initiative, we have been involved in the organization of more than ten tracks, with the aim of the creation of new resources in several languages that were made available to the research community. This allowed to compare the new several approaches on the same datasets. In this chapter, we will focus on the description of three author profiling tracks, on their data creation as well as the result analysis.

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“…It was launched in 2014 as part of a text corpus called RusPersonality (Litvinova et al, 2016). Deception Bank currently contains truthful and deceptive narratives (average text length is 221 words, SD = 15.2) of the same individuals on the same topic (How I spent yesterday) (see example in Table 1).…”

Section: Data and Settingsmentioning

confidence: 99%

Deception detection in Russian texts

Litvinova¹,

Seredin²,

Litvinova³

et al. 2017

Proceedings of the Student Research Workshop at the 15th Conference Of the European Chapter of the Association for Co

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Psychology studies show that people detect deception no more accurately than by chance, and it is therefore important to develop tools to enable the detection of deception. The problem of deception detection has been studied for a significant amount of time, however in the last 10-15 years we have seen methods of computational linguistics being employed with greater frequency. Texts are processed using different NLP tools and then classified as deceptive/truthful using modern machine learning methods. While most of this research has been performed for the English language, Slavic languages have never been the focus of detection deception studies. This paper deals with deception detection in Russian narratives related to the theme "How I Spent Yesterday". It employs a specially designed corpus of truthful and deceptive texts on the same topic from each respondent, such that N = 113. The texts were processed using Linguistic Inquiry and Word Count software that is used in most studies of text-based deception detection. The average amount of parameters, a majority of which were related to Part-of-Speech, lexical-semantic group, and other frequencies. Using standard statistical analysis, statistically significant differences between false and truthful Russian texts was uncovered. On the basis of the chosen parameters our classifier reached an accuracy of 68.3%. The accuracy of the model was found to depend on the author's gender.

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"Ruspersonality": A Russian corpus for authorship profiling and deception detection

Cited by 22 publications

References 2 publications

Identification of Suicidal Tendencies of Individuals based on the Quantitative Analysis of their Internet Texts

Identification of Suicidal Tendencies of Individuals based on the Quantitative Analysis of their Internet Texts

Author Profiling Tracks at FIRE

Deception detection in Russian texts

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