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<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" article-type="research-article" dtd-version="1.2" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">Russian Journal of Linguistics</journal-id><journal-title-group><journal-title xml:lang="en">Russian Journal of Linguistics</journal-title><trans-title-group xml:lang="ru"><trans-title>Russian Journal of Linguistics</trans-title></trans-title-group></journal-title-group><issn publication-format="print">2687-0088</issn><issn publication-format="electronic">2686-8024</issn><publisher><publisher-name xml:lang="en">Peoples’ Friendship University of Russia named after Patrice Lumumba (RUDN University)</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">15411</article-id><article-id pub-id-type="doi">10.22363/2312-9182-2017-21-1-183-202</article-id><article-categories><subj-group subj-group-type="toc-heading" xml:lang="en"><subject>Articles</subject></subj-group><subj-group subj-group-type="toc-heading" xml:lang="ru"><subject>Статьи</subject></subj-group><subj-group subj-group-type="toc-heading" xml:lang="zh"><subject>Articles</subject></subj-group><subj-group subj-group-type="article-type"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title xml:lang="en">Towards a New Linguistic Model for Detecting Political Lies</article-title><trans-title-group xml:lang="ru"><trans-title>На пути к новой лингвистической модели определения политической лжи</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>El-Zawawy</surname><given-names>Amr M</given-names></name><name xml:lang="ru"><surname>Эль-Завави</surname><given-names>Амр М</given-names></name></name-alternatives><email>amrzuave@yahoo.com</email><xref ref-type="aff" rid="aff1"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">Alexandria University</institution></aff><aff><institution xml:lang="ru">Университет Александрии</institution></aff></aff-alternatives><pub-date date-type="pub" iso-8601-date="2017-12-15" publication-format="electronic"><day>15</day><month>12</month><year>2017</year></pub-date><volume>21</volume><issue>1</issue><issue-title xml:lang="en">Discourse Analysis in the 21 st Century: Theory an d Practice  (II)</issue-title><issue-title xml:lang="ru">Дискурс-анализ в 21 веке: теория  и практика (II)</issue-title><fpage>183</fpage><lpage>202</lpage><history><date date-type="received" iso-8601-date="2017-02-25"><day>25</day><month>02</month><year>2017</year></date></history><permissions><copyright-statement xml:lang="en">Copyright ©; 2017, El-Zawawy A.M.</copyright-statement><copyright-statement xml:lang="ru">Copyright ©; 2017, Эль-Завави А.М.</copyright-statement><copyright-statement xml:lang="zh">Copyright ©; 2017, El-Zawawy A.</copyright-statement><copyright-year>2017</copyright-year><copyright-holder xml:lang="en">El-Zawawy A.M.</copyright-holder><copyright-holder xml:lang="ru">Эль-Завави А.М.</copyright-holder><copyright-holder xml:lang="zh">El-Zawawy A.</copyright-holder><ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/><license><ali:license_ref xmlns:ali="http://www.niso.org/schemas/ali/1.0/">https://creativecommons.org/licenses/by-nc/4.0</ali:license_ref></license></permissions><self-uri xlink:href="https://journals.rudn.ru/linguistics/article/view/15411">https://journals.rudn.ru/linguistics/article/view/15411</self-uri><abstract xml:lang="en">The present study addresses the problem of how the two US presidential candidates Donald Trump and Hillary Clinton use statements judged to be false by the Politifact site while delivering their campaign speeches. Two corpora of Clinton’s and Trump’s alleged lies were compiled. Each corpus contained 16 statements judged to be false or ridiculously untrue (‘pants on fire’) by the Pulitzer Prize Winner site Politifact. Some statements were accompanied by the video recordings where they appeared; others had no video recordings affiliated because they are either tweets or their events had not been recorded on Youtube or elsewhere. The present research made use of CBCA (Criteria-based Content Analysis) but as a stepping stone for building a new model of detecting lies in political discourse to suit the characteristics of campaign discourse. This furnished the qualitative dimension of the research. As for the quantitative dimension, data were analyzed using software, namely LIWC (Linguistic Inquiry &amp; Word Count), and also focused on the content analysis of the deception cues that can be matched with the results obtained from computerized findings. When VSA (Voice Stress Analysis) was required, Praat was used. Statistical analyses were occasionally applied to reach highly accurate results. The study concluded that the New Model (NM) is not context-sensitive, being a quantitative one, and is thus numerically oriented in its decisions. Moreover, when qualitative analysis intervenes, especially in examining Politifact rulings, context plays a crucial role in passing judgements on deceptive vs. non-deceptive discourse.</abstract><trans-abstract xml:lang="ru">Настоящее исследование рассматривает проблему того, каким образом предвыборная речь американских кандидатов в президенты, Дональда Трампа и Хиллари Клинтон, вводит в заблуждение избирателей. Были составлены два корпуса возможных лжевысказываний Клинтон и Трампа, каждый из которых содержал 16 утверждений, признанных сайтом Politifact (победитель Пулитцеровской премии) ложными или не соответствующими действительности. Некоторые заявления сопровождались видеозаписями, в то время как другие - нет, поскольку являлись либо твитами, либо событиями, которые не были выложены на YouTube или иной ресурс. В данном исследовании применялся метод контент-анализа в качестве трамплина для построения новой модели определения лжи в политическом дискурсе в соответствии с характеристиками дискурса кампании, что обеспечило качественный аспект исследования. Что касается количественных данных, то они были проанализированы с помощью программного обеспечения LIWC (Linguistic Inquiry and Word Count), а также ориентировались на анализ содержания обманных сигналов, которые могли быть сопоставлены с результатами, полученными из компьютеризированных данных. Для анализа стрессовых изменений голоса использовалась программа Praat. Для достижения высокой точности результатов в некоторых случаях нашел применение и статистический анализ. В исследовании делается вывод о том, что новая модель не является контекстно-зависимой, будучи количественной, и, таким образом, численно ориентированной в своих решениях. Вместе с тем, качественный анализ, особенно при изучении положений проекта Politifact, показывает, что контекст играет решающую роль в определении дискурса как вводящего или не вводящего в заблуждение.</trans-abstract><kwd-group xml:lang="en"><kwd>Clinton</kwd><kwd>Trump</kwd><kwd>LIW</kwd><kwd>Politifact</kwd><kwd>Lie detection</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>Клинтон</kwd><kwd>Трамп</kwd><kwd>программное обеспечение LIWC</kwd><kwd>проект Politifact</kwd><kwd>определение лжи</kwd></kwd-group></article-meta></front><body></body><back><ref-list><ref id="B1"><label>1.</label><mixed-citation>All False statements involving Hillary Clinton (Accessed on August 02, 2016). 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