The QWERTY Effect on the Web: How Typing Shapes the Meaning of Words in Online Human-Computer Interaction

ABSTRACT

The QWERTY effect postulates that the keyboard layout influences word meanings by linking positivity to the use of the right hand and negativity to the use of the left hand. For example, previous research has established that words with more right hand letters are rated more positively than words with more left hand letters by human subjects in small scale experiments. In this paper, we perform large scale investigations of the QWERTY effect on the web. Using data from eleven web platforms related to products, movies, books, and videos, we conduct observational tests whether a hand-meaning relationship can be found in text interpretations by web users. Furthermore, we investigate whether writing text on the web exhibits the QWERTY effect as well, by analyzing the relationship between the text of online reviews and their star ratings in four additional datasets. Overall, we find robust evidence for the QWERTY effect both at the point of text interpretation (decoding) and at the point of text creation (encoding). We also find under which conditions the effect might not hold. Our findings have implications for any algorithmic method aiming to evaluate the meaning of words on the web, including for example semantic or sentiment analysis, and show the existence of "dactilar onomatopoeias" that shape the dynamics of word-meaning associations. To the best of our knowledge, this is the first work to reveal the extent to which the QWERTY effect exists in large scale human-computer interaction on the web.

References

1. Abisheva, A., Garimella, V. R. K., Garcia, D., and Weber, I. Who watches (and shares) what on youtube? and when? using twitter to understand youtube viewership. In Proceedings of the International Conference on Web Search and Data Mining (2014), pp. 593--602.
2. Alvarez, R., Garcia, D., Moreno, Y., and Schweitzer, F. Sentiment cascades in the 15m movement. EPJ Data Science 4, 6 (2015).
3. Bollen, J., Gonçalves, B., Ruan, G., and Mao, H. Happiness is assortative in online social networks. Artificial life 17, 3 (2011), 237--251.
4. Bollen, J., Mao, H., and Zeng, X. Twitter mood predicts the stock market. Journal of Computational Science 2, 1 (2011), 1--8.
5. Bryden, J., Funk, S., and Jansen, V. A. Word usage mirrors community structure in the online social network twitter. EPJ Data Science 2, 1 (2013), 1--9.
6. Casasanto, D., Jasmin, K., Brookshire, G., and Gijssels, T. The qwerty effect: How typing shapes word meanings and baby names. In Proceedings of the Conference of the Cognitive Science Society (2014).
7. Casasanto, D., Román, A., Santiago, J., et al. Can culture influence body-specific associations between space and valence? Cognitive science (2014).
8. Danescu-Niculescu-Mizil, C., West, R., Jurafsky, D., Leskovec, J., and Potts, C. No country for old members: User lifecycle and linguistic change in online communities. In Proceedings of the international conference on World Wide Web (2013), pp. 307--318.
9. Danilak, M. M. Language detection library ported from google's language-detection, 2015.
10. Davison, A. C., and Hinkley, D. V. Bootstrap methods and their application. Cambridge university press, 1997.
11. De Saussure, F. Course in general linguistics. Columbia University Press, 1966.
12. DiMaggio, P., Hargittai, E., Neuman, W. R., and Robinson, J. P. Social implications of the internet. Annual review of sociology (2001), 307--336.
13. Dodds, P. S., Clark, E. M., Desu, S., Frank, M. R., Reagan, A. J., Williams, J. R., Mitchell, L., Harris, K. D., Kloumann, I. M., Bagrow, J. P., et al. Human language reveals a universal positivity bias. Proceedings of the National Academy of Sciences 112, 8 (2015), 2389--2394.
14. Fritz, B. Ombd snapshot, Aug 2015.
15. Garas, A., Garcia, D., Skowron, M., and Schweitzer, F. Emotional persistence in online chatting communities. Scientific Reports 2 (2012).
16. Garcia, D., Garas, A., and Schweitzer, F. Positive words carry less information than negative words. EPJ Data Science 1 (2012), 3.
17. Garcia, D., and Schweitzer, F. Social signals and algorithmic trading of bitcoin. Royal Society Open Science 2, 9 (2015), 150288.
18. Garcia, D., Weber, I., and Garimella, R. V. K. Gender asymmetries in reality and fiction : The bechdel test of social media. In International Conference on Weblogs and Social Media (2014), pp. 131--140.
19. Gindrat, A.-D., Chytiris, M., Balerna, M., Rouiller, E. M., and Ghosh, A. Use-dependent cortical processing from fingertips in touchscreen phone users. Current Biology 25, 1 (2015), 109--116.
20. Golder, S. A., and Macy, M. W. Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science 333, 6051 (2011), 1878--1881.
21. Golder, S. A., and Macy, M. W. Digital footprints: opportunities and challenges for online social research. Sociology 40, 1 (2014), 129.
22. González-Bailón, S., Banchs, R. E., and Kaltenbrunner, A. Emotions, public opinion, and us presidential approval rates: A 5-year analysis of online political discussions. Human Communication Research 38, 2 (2012), 121--143.
23. Good, P. I. Permutation, parametric, and bootstrap tests of hypotheses. Springer Science & Business Media, 2006.
24. Jakubıcek, M., Kilgarriff, A., Kovár, V., Rychlỳ, P., Suchomel, V., et al. The tenten corpus family. In International Corpus Linguistics Conference CL (2013).
25. Jasmin, K., and Casasanto, D. The qwerty effect: How typing shapes the meanings of words. Psychonomic bulletin & review 19, 3 (2012), 499--504.
26. Kappas, A. Social regulation of emotion: messy layers. Frontiers in psychology 4 (2013).
27. Kramer, A. D., Guillory, J. E., and Hancock, J. T. Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences 111, 24 (2014), 8788--8790.
28. Lin, Y., Michel, J.-B., Aiden, E. L., Orwant, J., Brockman, W., and Petrov, S. Syntactic annotations for the google books ngram corpus. In Proceedings of the ACL system demonstrations (2012), pp. 169--174.
29. McAuley, J., Pandey, R., and Leskovec, J. Inferring networks of substitutable and complementary products. In Proceedings of the International Conference on Knowledge Discovery and Data Mining (2015), pp. 785--794.
30. McAuley, J., Targett, C., Shi, Q., and van den Hengel, A. Image-based recommendations on styles and substitutes. In Proceedings of the International Conference on Research and Development in Information Retrieval (2015), pp. 43--52.
31. McAuley, J. J., and Leskovec, J. From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews. In Proceedings of the international conference on World Wide Web (2013), pp. 897--908.
32. Osgood, C. E., Suci, G. J., and Tannenbaum, P. H. The measurement of meaning. University of Illinois Press, 1964.
33. Russell, J. A., and Mehrabian, A. Evidence for a three-factor theory of emotions. Journal of research in Personality 11, 3 (1977), 273--294.
34. Sassenberg, K., and Boos, M. Attitude change in computer-mediated communication: Effects of anonymity and category norms. Group Processes & Intergroup Relations 6, 4 (2003), 405--422.
35. Scherer, K. R. What are emotions? and how can they be measured? Social science information 44, 4 (2005), 695--729.
36. Schweitzer, F., and Garcia, D. An agent-based model of collective emotions in online communities. The European Physical Journal B 77, 4 (October 2010), 533--545.
37. Smith, A. Americans and text messaging, 2011. Pew Research Center Report.
38. Tanase, D., Garcia, D., Garas, A., and Schweitzer, F. Emotions and activity profiles of influential users in product reviews communities. Frontiers in Physics 3, 87 (2015).
39. The Radicati Group, I. Email statistics report, 2015.
40. Thogmartin, W. E. The qwerty effect does not extend to birth names. Names 61, 1 (2013), 47--52.
41. Vallortigara, G., Regolin, L., Bortolomiol, G., and Tommasi, L. Lateral asymmetries due to preferences in eye use during visual discrimination learning in chicks. Behavioural brain research 74, 1 (1996), 135--143.
42. Yohai, V. J. High breakdown-point and high efficiency robust estimates for regression. The Annals of Statistics (1987), 642--656.
43. Ziegler, C.-N., McNee, S. M., Konstan, J. A., and Lausen, G. Improving recommendation lists through topic diversification. In Proceedings of the international conference on World Wide Web (2005), pp. 22--32.

Index Terms

• Human-centered computing

  • Human computer interaction (HCI)

    • Empirical studies in HCI