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Measuring language exposure during social media interaction and its application as a reliable metric of individual differences during comprehension

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Poster E114 in Poster Session E, Thursday, October 26, 10:15 am - 12:00 pm CEST, Espace Vieux-Port
This poster is part of the Sandbox Series.

Shannon McKnight¹; ¹Fort Lewis College

It is estimated that nearly two-thirds of the population interacts with social media with nearly 90% of college-aged students interacting at least once per day (Pew Research Center, 2021). While many social media platforms support different forms of posting including photo, text, and video; users will encounter more or less verbal material depending on the platform. For example, Twitter users are more likely to spend time scrolling through text than users of TikTok or Instagram. However, there is not yet a consistent method for measuring language exposure online. As a field, there is a growing need for a reliable way to measure how social media based language interactions (including subtitles, video content, text posts, etc.) contribute to our overall experience with and mastery of language and how that shapes comprehension. Author recognition tests (ARTs) combine popular author names with reasonable foils (Stanovich & West, 1989; Acheson et al 2008) as an indirect measure of print exposure and reliably correlate with vocabulary size (West and Stanovich, 1991; Martin-Chang & Gould, 2008) and reading comprehension (Mol & Bus, 2011). Language experience measured at least in part through ARTs have also been shown to predict the P600 event-related potential (ERP) response to syntactic errors (Pakulak & Neville, 2010; McKnight et al., 2016) as well as eye-movements during comprehension (Moore & Gordon, 2015). While they continue to reflect vocabulary and word knowledge in predominantly WEIRD samples, they need to be periodically updated to accurately reflect knowledge from this sample (see Acheson et al 2008). Furthermore, they can be specialized to better capture individual differences among unique populations, including for readers of English in the United Kingdom (Masterson & Hayes, 2007), Canada (Chateau & Jared, 200), and children (eg. Ricketts et al., 2007). However, social media algorithms and the sheer volume of online creators poses a problem for a social media specialized ART. Nevertheless, questionnaires can be administered and psychometrically evaluated to capture language exposure on social media. Based on the original Reading and Media Habits Questionnaire (Stanovich & West, 1989) and Reading Habits Questionnaire (Acheson et al, 2008), the Social Media Interactions and Language Exposure scale (SMILE) includes measurements of how often (in hours) individuals spend on social media, the platforms they prefer to use, “activeness” on social media (passively scrolling versus actively commenting on posts), as well as how often they view subtitles on social media posts, while watching TV or movies, and in online classroom settings. The goal for this work in progress is twofold: the first stage involves the development of a reliable SMILE scale and appropriate psychometric testing. The second stage is to determine the relatedness of SMILE to standard individual differences measures (such as ARTs, vocabulary, comprehension tests, and author naming tasks) as well as during error monitoring tasks. Overall, this Sandbox Series presentation will present psychometric data for the SMILE scale and preliminary data to address the scale’s utility in capturing a realistic snapshot of an individual’s language experience.

Topic Areas: Development of Resources, Software, Educational Materials, etc., Reading