This is the first book of its kind to provide a practical and student-friendly guide to corpus linguistics that explains the nature of electronic data and how it can be collected and analyzed. * ...Designed to equip readers with the technical skills necessary to analyze and interpret language data, both written and (orthographically) transcribed * Introduces a number of easy-to-use, yet powerful, free analysis resources consisting of standalone programs and web interfaces for use with Windows, Mac OS X, and Linux * Each section includes practical exercises, a list of sources and further reading, and illustrated step-by-step introductions to analysis tools * Requires only a basic knowledge of computer concepts in order to develop the specific linguistic analysis skills required for understanding/analyzing corpus data
Children tend to produce words earlier when they are connected to a variety of other words along the phonological and semantic dimensions. Though these semantic and phonological connectivity effects ...have been extensively documented, little is known about their underlying developmental mechanism. One possibility is that learning is driven by lexical network growth where highly connected words in the child's early lexicon enable learning of similar words. Another possibility is that learning is driven by highly connected words in the external learning environment, instead of highly connected words in the early internal lexicon. The present study tests both scenarios systematically in both the phonological and semantic domains across 10 languages. We show that phonological and semantic connectivity in the learning environment drives growth in both production‐ and comprehension‐based vocabularies, even controlling for word frequency and length. This pattern of findings suggests a word learning process where children harness their statistical learning abilities to detect and learn highly connected words in the learning environment.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
► We examine zero-acquaintance personality judgment in the context of microblogs. ► Personality traits are associated with specific linguistic cues in microblogs. ► Agreeableness and neuroticism can ...be accurately judged by unknown others.
Microblogging services such as Twitter have become increasingly popular in recent years. However, little is known about how personality is manifested and perceived in microblogs. In this study, we measured the Big Five personality traits of 142 participants and collected their tweets over a 1-month period. Extraversion, agreeableness, openness, and neuroticism were associated with specific linguistic markers, suggesting that personality manifests in microblogs. Meanwhile, eight observers rated the participants’ personality on the basis of their tweets. Results showed that observers relied on specific linguistic cues when making judgments, and could only judge agreeableness and neuroticism accurately. This study provides new empirical evidence of personality expression in naturalistic settings, and points to the potential of utilizing social media for personality research.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
This paper examines Twitter use by product and service companies in the healthcare sector. This four company study aims to identify the type of content posted in Twitter that drives engagement in ...terms of likes, retweets and comments. A sample of 838 tweets were thematically coded as to the perceived tweet function. The tweets were analyzed to determine whether the function was significantly associated with greater or lesser engagement. Linguistic content of the tweets was then analyzed using LIWC to determine the type of content associated with greater engagement. Results suggest that company type (product vs. service) and tweet function influence the degree of engagement. Engagement also differed significantly based on the linguistic content of messages, such that word categories associated with greater engagement were identified. Thus, to drive greater engagement with a wider network, the business marketer should consider the nature of the company as well as the function and linguistic content of messages posted to Twitter.
•Service companies' tweets obtained more likes and comments than product companies.•Retweets had the highest level of behavioral engagement.•Specific types of tweets differ in behavioral engagement i.e., likes and comments for services and products.•Specific tweet types for services and products differed linguistically in eliciting behavioral engagement i.e., likes.•Behavioral engagement i.e. comments and retweets were differentially evoked by certain linguistic characteristics.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Abstract
The language and speech of individuals with psychosis reflect their impairments in cognition and motor processes. These language disturbances can be used to identify individuals with and at ...high risk for psychosis, as well as help track and predict symptom progression, allowing for early intervention and improved outcomes. However, current methods of language assessment—manual annotations and/or clinical rating scales—are time intensive, expensive, subject to bias, and difficult to administer on a wide scale, limiting this area from reaching its full potential. Computational methods that can automatically perform linguistic analysis have started to be applied to this problem and could drastically improve our ability to use linguistic information clinically. In this article, we first review how these automated, computational methods work and how they have been applied to the field of psychosis. We show that across domains, these methods have captured differences between individuals with psychosis and healthy controls and can classify individuals with high accuracies, demonstrating the promise of these methods. We then consider the obstacles that need to be overcome before these methods can play a significant role in the clinical process and provide suggestions for how the field should address them. In particular, while much of the work thus far has focused on demonstrating the successes of these methods, we argue that a better understanding of when and why these models fail will be crucial toward ensuring these methods reach their potential in the field of psychosis.
Work is thought to be more enjoyable and beneficial to individuals and society when there is congruence between one’s personality and one’s occupation. We provide large-scale evidence that ...occupations have distinctive psychological profiles, which can successfully be predicted from linguistic information unobtrusively collected through social media. Based on 128,279 Twitter users representing 3,513 occupations, we automatically assess user personalities and visually map the personality profiles of different professions. Similar occupations cluster together, pointing to specific sets of jobs that one might be well suited for. Observations that contradict existing classifications may point to emerging occupations relevant to the 21st century workplace. Findings illustrate how social media can be used to match people to their ideal occupation.
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BFBNIB, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
The most critical attribute of human language is its unbounded combinatorial nature: smaller elements can be combined into larger structures on the basis of a grammatical system, resulting in a ...hierarchy of linguistic units, such as words, phrases and sentences. Mentally parsing and representing such structures, however, poses challenges for speech comprehension. In speech, hierarchical linguistic structures do not have boundaries that are clearly defined by acoustic cues and must therefore be internally and incrementally constructed during comprehension. We found that, during listening to connected speech, cortical activity of different timescales concurrently tracked the time course of abstract linguistic structures at different hierarchical levels, such as words, phrases and sentences. Notably, the neural tracking of hierarchical linguistic structures was dissociated from the encoding of acoustic cues and from the predictability of incoming words. Our results indicate that a hierarchy of neural processing timescales underlies grammar-based internal construction of hierarchical linguistic structure.
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