Психолінгвальні Аспекти Перекладу Словника LIWC

Zasiekin, Serhii; Bezuglova, Nataliia; Hapon, Anastasiia; Matiushenko, Vladyslav; Podolska, Olha; Zubchuk, Daria

doi:10.29038/eejpl.2018.5.1.zas

Cited by 5 publications

(3 citation statements)

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“…We audio-recorded and transcribed interviews. Transcriptions were analysed by the research team using the Linguistic Inquiry and Word Count (LIWC) (Tausczik & Pennebaker, 2010) in Ukrainian version (Zasiekin et al, 2018). The LIWC analyzes the words and administer them according with the pre-defined categories of positive and negative emotions, motivation, thinking styles and social relationships.…”

Section: Methodsmentioning

confidence: 99%

“…Evidence consistently suggests that individuals with physical and emotional pain focusing on themselves more frequently use first-person singular pronouns (Zasiekin et al, 2018). However, other research pointed out that individual selfreference is associated with positive view of themselves as active subjects (Zasiekina & Mahdysiuk, 2018).…”

Section: And Verbal Tense Categoriesmentioning

confidence: 99%

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Verbal Expression of Preparedness in Retirement Planning Interviews

Mahdysiuk

Tryhub

Дучимінська

et al. 2020

eejpl

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Conceptualization of retirement requires interdisciplinary research, which is represented by psycholinguistic approach in the present paper. The study takes a first step to explore conceptualization of retirement by individuals with different levels of retirement preparedness. The study applies questionnaire Psychological Preparedness for Retirement (Zasiekina & Mahdysiuk, 2018) to assess levels of preparedness; semi-structured interviews to focus primarily on concerns related to planning postretirement period; Linguistic Inquiry and Word Count (LIWC) (Tausczik & Pennebaker, 2010) to reveal psychological categories and explore conceptualization of retirement. By the end of the assessment, data had been collected from 117 workers who were at preretirement period. The sample was weighted by age and occupation in order to improve its representative of the total population 22 (18.8%) – university staff, teachers at colleges, 18 (15.4%), nurses in kindergartens, 8 (6.6%), healthcare staff, 18 (15.4%), government officials, 28 (23.9%), workers from private sector, 23 (19.7%). The final weighted sample includes 65.8% females, average age 54.52, (SD=6.21). The results indicate that 8.5% respondents have a low level of preparedness, 61.5% - a medium level of preparedness and 30% - a high level of preparedness. Interestingly, the highest percentage of categories of affect and positive emotions were observed in the group with a medium level of preparedness, whereas the highest percentage of categories cause, focus on present, and family were captured in the group with a high level of preparedness. Taken together, these results suggest that the high level of retirement preparedness is associated with active cognitive reappraisal of retirement as a period of family activities.

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Section: Methodsmentioning

confidence: 99%

Section: And Verbal Tense Categoriesmentioning

confidence: 99%

Verbal Expression of Preparedness in Retirement Planning Interviews

Mahdysiuk

Tryhub

Дучимінська

et al. 2020

eejpl

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show abstract

“…LIWC2015 is already available in multiple languages, including Dutch (van Wissen and Boot, 2017), German (Meier et al, 2018), Ukrainian (Zasiekin et al, 2018), Brazilian Portuguese (Carvalho et al, 2019), and Romanian (Dudau and Sava, 2020). The number of translations would probably increase in the following years, considering that the previous LIWC dictionaries have been developed in over 10 languages and that conducting automatic language analysis has become one of the state-of-theart approaches in psychology.…”

Section: Liwc2015 For Multilingual Analysismentioning

confidence: 99%

Performing Multilingual Analysis With Linguistic Inquiry and Word Count 2015 (LIWC2015). An Equivalence Study of Four Languages

Dudău

Sava

2021

Front. Psychol.

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Today, there is a range of computer-aided techniques to convert text into data. However, they convey not only strengths but also vulnerabilities compared to traditional content analysis. One of the challenges that have gained increasing attention is performing automatic language analysis to make sound inferences in a multilingual assessment setting. The current study is the first to test the equivalence of multiple versions of one of the most appealing and widely used lexicon-based tools worldwide, Linguistic Inquiry and Word Count 2015 (LIWC2015). For this purpose, we employed supervised learning in a classification problem and computed Pearson's correlations and intraclass correlation coefficients on a large corpus of parallel texts in English, Dutch, Brazilian Portuguese, and Romanian. Our findings suggested that LIWC2015 is a valuable tool for multilingual analysis, but within-language standardization is needed when the aim is to analyze texts sourced from different languages.

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Social identity correlates of social media engagement before and after the 2022 Russian invasion of Ukraine

Kyrychenko,

Brik,

van der Linden

et al. 2024

Nat Commun

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Despite the global presence of social media platforms, the reasons why people like and share content are still poorly understood. We investigate how group identity mentions and expressions of ingroup solidarity and outgroup hostility in posts correlate with engagement on Ukrainian social media (i.e., shares, likes, and other reactions) before and after the 2022 Russian invasion of Ukraine. We use a dataset of 1.6 million posts from Ukrainian news source pages on Facebook and Twitter (currently X) and a geolocated sample of 149 thousand Ukrainian tweets. Before the 2022 Russian invasion, we observe that outgroup mentions in posts from news source pages are generally more strongly associated with engagement than negative, positive, and moral-emotional language. After the invasion, social identity mentions become less strongly associated with engagement. Moreover, post-invasion ingroup solidarity posts are strongly related to engagement, whereas posts expressing outgroup hostility show smaller associations. This is the case for both news and non-news social media data. Our correlational results suggest that signaling solidarity with one’s ingroup online is associated with more engagement than negativity about outgroups during intense periods of intergroup conflicts, at least in the context of the Russian-Ukrainian war.

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Психолінгвальні Аспекти Перекладу Словника LIWC

Cited by 5 publications

References 0 publications

Verbal Expression of Preparedness in Retirement Planning Interviews

Verbal Expression of Preparedness in Retirement Planning Interviews

Performing Multilingual Analysis With Linguistic Inquiry and Word Count 2015 (LIWC2015). An Equivalence Study of Four Languages

Social identity correlates of social media engagement before and after the 2022 Russian invasion of Ukraine

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