![]() So far, scientists have used LIWC2015 for assessment in clinical psychology and psychotherapy (e.g., Wardecker et al., 2017 Huston et al., 2019), social psychology (Kwon et al., 2018 Klauke et al., 2019), personality psychology (e.g., Holtzman et al., 2019), education (e.g., Moore et al., 2019), health (e.g., Jordan et al., 2019), communication (Markowitz and Slovic, 2020), cultural psychology (e.g., Chen and Huang, 2019) or political science (Bond et al., 2017), to name a few application fields.īesides LIWC2015, there are other closed-vocabulary resources to which psychologists can appeal. In the following years, the ascending trend in using LIWC dictionaries for impactful research continued, peaking in 2020 with 124 papers indexed in Web of Science, after a similar number was reached in 2019 (i.e., 120). However, we could notice that in 2016, one year after the release of LIWC2015, a significant increase in interest in LIWC dictionaries occurred, considering the 65 papers published in that year, which is almost double the number for 2015 (i.e., 36) and the highest number until then. Therefore, estimating the number of papers in which LIWC2015 was a research tool would be impossible based on such a simplistic literature scan. Most authors opted for the generic term LIWC as a keyword. According to our search performed at the end of May 2021, at that time, the Web of Science contained 736 records that included LIWC * or Linguistic Inquiry and Word Count as keywords. LIWC2015 and its predecessors have probably been the most preferred solution for automatic content analysis in social science research. This result might further suggest that the knowledge gained with previous LIWC tools could still be relevant to some extent for studies conducted with LIWC2015, despite some major differences in the composition of the dictionaries. ( 2015), the correlations between the word frequencies counted with LIWC2015 and those obtained with LIWC2007 were very large-most of them were above 0.95-indicating that the new version tends to detect very similar linguistic patterns from one corpus to another as the old versions. However, for example, in the study of Pennebaker et al. The latest release, LIWC2015 (Pennebaker et al., 2015), was introduced more as a new instrument than an updated variant of the old versions since, after a rigorous process of several years, some new categories were developed, others disappeared, while others kept their names but received an improved word composition. Since then, two revised versions have been launched: LIWC2001 (Pennebaker et al., 2001) and LIWC2007 (Pennebaker et al., 2007). The history of Linguistic Inquiry and Word Count (LIWC) began in the early 1990s. The software scans the input text, makes a word-by-word comparison with the dictionary, and computes the percentage of words found in each category. Each word or word stem in the dictionary belongs to one or more pre-established categories with different meanings, most of them ensuing from psychological theories. More precisely, it consists of an internal dictionary and a piece of software designed for tokenization and word counting. Following a simplistic working principle, the tool provides any researcher with an automated, objective method for extracting insights about the attentional focus reflected through language (Boyd and Schwartz, 2021). Linguistic Inquiry and Word Count 2015 (LIWC2015 Pennebaker et al., 2015) is a closed-vocabulary approach tool well-suited for the needs of psychologists with no or limited background in data science (Kern et al., 2016). By extension, automatic content analysis refers to any transformation of such kind that is not performed manually by human raters but with specialized software or programming languages. Content analysis means any systematic transformation of a string of text into statistically manageable data representing the presence, intensity, or frequency of some relevant features (Shapiro and Markoff, 1997). As the repository of psychologically relevant written language expanded massively at an accelerating pace, opening new possibilities for social science research worldwide, a pressure to automatize content analysis also arose (e.g., Shayaa et al., 2018). ![]() Within a short period, the Internet of Things made online communication vital for our lives in society.
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