Abstract
An individual‘s ability to produce quality work is a function of their current motivation, their control over the results of their work, and the social influences of other individuals. All of these factors can be identified in the language that individuals use to discuss their work with their peers. Previous approaches to modeling motivation have relied on social-network and time-series analysis to predict the popularity of a contribution to user-generated content site. In contrast, we show how an individual’s use of language can reflect their level of motivation and can be used to predict their future performance. We compare our results to an analysis of motivation based on utility theory. We show that an understanding of the language contained in comments on user generated content sites provides significant insight into an author’s level of motivation and the potential quality of their future work.
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Tomlinson, M.T., Bracewell, D.B., Krug, W., Hinote, D. (2014). #impressme: The Language of Motivation in User Generated Content. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2014. Lecture Notes in Computer Science, vol 8404. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54903-8_15
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DOI: https://doi.org/10.1007/978-3-642-54903-8_15
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