CAMIO: A Corpus for OCR in Multiple Languages

Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022), pages 109–118 Marseille, 20-25 June 2022 © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0 109 RU-ADEPT: Russian Anonymized Dataset with Eight Personality Traits C. Anton Rytting∗, Valerie Novak∗, James R. Hull∗, Victor M. Frank∗, Paul R. Rodrigues†, Jarrett G.W. Lee∗, Laurel G. Miller-Sims∗ ∗University of Maryland College Park, MD, U.S.A. {crytting, vnovak, jhull1, vmfrank, jgwl, lmillers}@umd.edu †Accenture Washington, D.C., U.S.A paul.rodrigues@accenture.com Abstract Social media has provided a platform for many individuals to easily express themselves naturally and publicly, and researchers have had the opportunity to utilize large quantities of this data to improve author trait analysis techniques and to improve author trait profiling systems. The majority of the work in this area, however, has been narrowly spent on English and other Western European languages, and generally focuses on a single social network at a time, despite the large quantity of data now available across languages and differences that have been found across platforms. This paper introduces RU-ADEPT, a dataset of Russian authors’ personality trait scores—Big Five and Dark Triad, demographic information (e.g. age, gender), with associated corpus of the authors’ cross-contributions to (up to) four different social media platforms—VKontakte (VK), LiveJournal, Blogger, and Moi Mir. We believe this to be the first publicly-available dataset associating demographic and personality trait data with Russian-language social media content, the first paper to describe the collection of Dark Triad scores with texts across multiple Russian-language social media platforms, and to a limited extent, the first publicly-available dataset of personality traits to author content across several different social media sites. Keywords: personality, Big Five, Dark Triad, Russian language, author profiling 1. Introduction There has been longstanding interest in under- standing the manner in which people’s personal- ity traits may be manifested in what they say or write. With the advent of widespread online written communication through the Internet, in- cluding social media, it is now feasible to col- lect user-generated text paired with information on personality traits and demographical informa- tion at much larger scales than a decade ago. It is thus not surprising that many researchers have examined social media text to study how personal- ity traits are manifest in one’s expression of their language. Many—probably most—of these early studies looked exclusively at English (Golbeck et al., 2011; Farnadi et al., 2016, inter alia) though a few datasets exist in other (Western European) languages as well (Rangel Pardo et al., 2015). While numerous ways of conceptualizing and mea- suring personality differences have been proposed, two commonly studied frameworks for studying personality in online media are the Five Factor Model (McCrae and John, 1992) and the “Dark Triad” (Paulhus and Williams, 2002). The former posits general traits of application to many aspects of life; the latter focuses on traits encompassing a propensity for malevolence or antisocial behavior. Each is briefly described below. 1.1. The Big Five Inventory-2 (BFI-2) The Five Factor Model or “Big Five” utilizes five broad factors of personality: Extraversion, …

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