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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References
Searle, J.R.: Speech Acts: An Essay in the Philosophy of Language. Cambridge University Press (1969)
Mintz, M., Bills, S., Snow, R., Jurafsky, D.: Distant supervision for relation extraction without labeled data. In: Proceedings of ACL-IJCNLP, vol. 2005 (2009)
Bracewell, D.B., Tomlinson, M.: The Social Actions of Successful Groups. In: IEEE Sixth International Conference on Semantic Computing (2012)
Core, M.G., Allen, J.F.: Coding Dialogs with the DAMSL Annotation Scheme. In: Traum, D. (ed.) AAAI Fall Symposium on Communicative Action in Humans and Machines, pp. 28–35. American Association for Artificial Intelligence (1997)
Stolcke, A., Shriberg, E., Bates, R., Coccaro, N., Jurafsky, D., Martin, R., Meteer, M., Ries, K., Taylor, P., Ess-Dykema, C.V.: Dialog Act Modeling for Conversational Speech. In: Applying Machine Learning to Discourse Processing, pp. 98–105. AAAI Press (1998)
Bunt, H.: The semantics of dialogue acts. In: International Conference on Computational Semantics, pp. 1–13 (2011)
Bender, E.M., Morgan, J.T., Oxley, M., Zachry, M., Hutchinson, B., Marin, A., Zhang, B., Ostendorf, M.: Annotating social acts: Authority claims and alignment moves in wikipedia talk pages. In: ACL HLT 2011, p. 48 (June 2011)
Hsu, C.-F., Khabiri, E., Caverlee, J.: Ranking Comments on the Social Web. In: 2009 International Conference on Computational Science and Engineering, pp. 90–97 (2009)
Khabiri, E., Hsu, C.F., Caverlee, J.: Analyzing and predicting community preference of socially generated metadata: A case study on comments in the digg community. In: AAAI Conference on Weblogs and Social Media (2009)
Cha, M., Mislove, A., Gummadi, K.P.: A measurement-driven analysis of information propagation in the flickr social network. In: Proceedings of the 18th International Conference on World Wide Web, WWW 2009, p. 721 (2009)
Szabo, G., Huberman, B.A.: Predicting the popularity of online content. Communications of the ACM 53(8), 80–88 (2010)
Kahneman, D., Tversky, A.: Kahneman & Tversky (1979) - Prospect Theory - An Analysis of Decision Under Risk.pdf. Econometrica 47(2), 263–291 (1979)
Heath, C., Larrick, R.P., Wu, G.: Goals as reference points. Cognitive Psychology 38(1), 79–109 (1999)
Albarracin, D., Hepler, J., Tannenbaum, M.: General Action and Inaction Goals: Their Behavioral, Cognitive, and Affective Origins and Influences. Current Directions in Psychological Science 20(2), 119–123 (2011)
Locke, E.A.: Toward a theory of task motivation and incentives. Organizational Behavior and Human Performance 3(2) (1968)
Finkelstein, S.R., Fishbach, A.: Tell Me What I Did Wrong: Experts Seek and Respond to Negative Feedback. Journal of Consumer Research 39(1), 22–38 (2012)
Ajzen, I.: The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes 50, 179–211 (1991)
Fang, A., Bunt, H., Cao, J.: Collaborative Annotation of Dialogue Acts: Application of a New ISO Standard to the Switchboard Corpus. In: EACL 2012, pp. 61–68 (2012)
Bracewell, D.B., Tomlinson, M.T., Brunson, M., Plymale, J., Bracewell, J., Boerger, D.: Annotation of Adversarial and Collegial Social Actions in Discourse. In: 6th Linguistic Annotation Workshop, pp. 184–192 (July 2012)
Davidov, D., Tsur, O., Rappaport, A.: Enhanced Sentiment Learning Using Twitter Hashtags and Smileys. In: Coling, pp. 241–249 (August 2010)
Roberts, K., Roach, M., Johnson, J., Gurthrie, J., Harabagiu, S.M.: Empatweet: Annotating and detecting emotions on Twitter. In: Proceedings of LREC 2012, pp. 3806–3813 (2012)
Petukhova, V., Bunt, H.: Incremental dialogue act understanding. In: IWCS 2011 Proceedings of the Ninth International Conference on Computational Semantics, pp. 235–244 (2011)
Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining. In: LREC, pp. 2200–2204 (2010)
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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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