ABSTRACT
We present an intelligent virtual interviewer that engages with a user in a text-based conversation and automatically infers the user's psychological traits, such as personality. We investigate how the personality of a virtual interviewer influences a user's behavior from two perspectives: the user's willingness to confide in, and listen to, a virtual interviewer. We have developed two virtual interviewers with distinct personalities and deployed them in a real-world recruiting event. We present findings from completed interviews with 316 actual job applicants. Notably, users are more willing to confide in and listen to a virtual interviewer with a serious, assertive personality. Moreover, users' personality traits, inferred from their chat text, influence their perception of a virtual interviewer, and their willingness to confide in and listen to a virtual interviewer. Finally, we discuss the implications of our work on building hyper- personalized, intelligent agents based on user traits.
- Adali, S., & Golbeck, J. Predicting personality with social behavior. ASONAM'2012, 302--309. Google ScholarDigital Library
- Bickmore, T., Gruber, A., & Picard, R. Establishing the computer-patient working alliance in automated health behavior change interventions. Patient Education and Counseling, 2005, 59(1): 21--30.Google ScholarCross Ref
- Bickmore, T. & Cassell, J. Small talk and conversational storytelling in embodied interface agents. AAAI Fall Symposium on Narrative Intelligence, 1999.Google Scholar
- Bouchet, F. & Sansonnet, J. Intelligent Agents with Personality: from Adjectives to Behavioral Schemes. In E. M. Alkhalifa & K. Gaid (Eds.), Cognitively Informed Interfaces: System Design and Development, 2012, 177--200. IGI Global.Google Scholar
- Brown, P. & Levinson, S. Politeness: Some universals in language usage. Cambridge University Press, 1987.Google ScholarCross Ref
- Cialdini R. Influence: The Psychology of Persuasion. 2006. Harper Business.Google Scholar
- Cronbach L.Coefficient alpha and the internal structure of tests. Psychometrika, 1951, 16 (3): 297--334.Google ScholarCross Ref
- de Ayala, R. J. The Theory and Practice of Item Response Theory, Guilford Press. 2009.Google Scholar
- Dempster, A., Laird. N., & Rubin, D. Maximum likelihood from incomplete data via the EM algorithm. J. Royal Statistical Society, 39(1): 1--38, 1977.Google Scholar
- Digman, J. M. Personality structure: Emergence of the five-factor model. Annual Review of Psychology 1990, 41(1): 417--440.Google Scholar
- Gebhard, P., Baur, T., Damian, I., Mehlmann, G., Wagner, J. & André, E. Exploring interaction strategies for virtual characters to induce stress in simulated job interviews. AAMAS 2014: 661--668. Google ScholarDigital Library
- Gludice, M., Booth, T., & Irwing, P. The distance between mars and venus: Measuring global sex differences in personality. Pub. Library of Sci. 2012, 7(1).Google Scholar
- Grosz, B. & Sidner, C. Attention, intention, and the structure of discourse. Computational Linguistics, 12(3): 175--204. 1986. Google ScholarDigital Library
- Gou, L., Zhou, M.X., & Yang, H. KnowMe and ShareMe: Understanding automatically discovered personality traits from social media and user sharing preferences. CHI '14, 955--964. Google ScholarDigital Library
- Havens L. Making Contact: Uses of Language in Psychotherapy, Harvard University Press, 1986.Google Scholar
- Helgoe, L. Introvert Power: Why Your Inner Life Is Your Hidden Strength. Sourcebooks, 2013, 2nd Ed.Google Scholar
- Hopcroft, J., An nlogn algorithm for minimizing states in a finite automaton, Theory of machines and computations, Academic Press, 189--196, 1971.Google Scholar
- Hu, R., Pu, P.: Enhancing collaborative filtering systems with personality information. RecSys '2011 Google ScholarDigital Library
- Jackle, A., Lynn, P., Sinibaldi, J., & Tippping, S. The effect of interviewer experience, attitudes, personality, and skills on respondent cooperation with face-to-face surveys. Survey Research Methods, 2013, 7(1): 1--15.Google Scholar
- Jones, H., Sabouret, N., Damian, I., Baur, T., André, E., Porayska-Pomsta, K., & Rizzo, P. Interpreting social cues to generate credible affective reactions of virtual job interviewers. CoRR abs/1402.5039 (2014).Google Scholar
- Kern, M., Eichstaedt, J., Schwartz, A., Dziurzynski, L., Ungar, L., Stillwell D., Kosinski, M., Ramones, S., & Seligman M. The online social self: An open vocabulary approach to personality. J. of Assessment, 2013, 21(2),158--169.Google ScholarCross Ref
- Koda, T. & Maes, P. Agents with faces: The effects of personification of agents. Proc. HCI'96, 98--103.Google Scholar
- Kosinski, M., Stillwell, D., & Graepel, T. Private traits and attributes are predictable from digital records of human behavior. Proc. National Academy of Sciences of the U.S.A. 2013, 110(15), 5802--5805.Google ScholarCross Ref
- Laird, J.E. The Soar Cognitive Architecture, MIT Press, 2012. Google ScholarDigital Library
- Lee, K., Peng, W., Jin, S., & Yan, C. Can robots manifest personality? An empirical test of personality recognition, social responses, and social presence in human-robot interaction. J. Comm., 2006, 56, 754--772.Google ScholarCross Ref
- Lee, K., Mahmud, J., Chen, J., and Zhou, M.X. & Nichols, J. Who will retweet this? Automatically Identifying and Engaging Strangers on Twitter to Spread Information. IUI 2014: 247--256. Google ScholarDigital Library
- Lucas, G., Gratch, J., King, A., & Morency, L. It's only a computer: virtual humans increase willingness to disclose. Comp. in Human Behavior, 2014, 37: 94--100. Google ScholarDigital Library
- Luo, L., Wang, F., Zhou, M.X., Pan, Y. & Chen, H. Who have got answers? Growing the pool of answerers in a smart enterprise social QA system. IUI'2014, 7-- 16. Google ScholarDigital Library
- Mahmud, J., Zhou, M.X., Megiddo, N., Nichols, J. & Drews, C. Recommending targeted strangers from whom to solicit information on social media. IUI 2013: 37--48. Google ScholarDigital Library
- Mairesse, F., Walker, M. A., Mehl, M. R., & Moore, R. K. Using linguistic cues for the automatic recognition of personality in conversation and text. JAIR, 2007, 30: 457--500. Google ScholarDigital Library
- Mayer, R. C., Davis, J. H., & Schoorman, F. D. 1995. An integrative model of organizational trust. Academy of Management Review, 709--734.Google Scholar
- Mehlmann, G., Janowski, K., & André, E. Modeling Grounding for Interactive Social Companions. KI 2016, 30(1): 45--52.Google Scholar
- Okun, B. Effective Helping: Interviewing and Counseling Techniques. 7th Edition, Cengage Learning, 2007.Google Scholar
- Paulhus, D. L. Measurement and control of response bias. In J. P. Robinson & P. R. Shaver (Eds.), Measures of personality and social psychological attitudes 1991, vol. 1, 17--59. Academic Press.Google Scholar
- Pennebaker, J. & King, L. Linguistic styles: Language use as an individual difference. J. Personality Social Psychology, 1999, 77(6): 1296--1312.Google ScholarCross Ref
- Powers, A., Kiesler, S., & Goetz, J. Matching robot appearance and behavior to tasks to improve humanrobot cooperation. The 12th IEEE Intl. Workshop on Robot and Human Interactive Communication, 2003, vol., IXX, 55--60.Google Scholar
- Reeves, B. & Nass, C. The Media Equation. CSLI Publications, 2002.Google Scholar
- Sansonnet, J. & Bouchet, F. Managing personality influences in dialogical agents. In Proc. ICAART 2013, Vol. 1, 89--98.Google Scholar
- Sproull, L., Subramani, M., Kiesler, S., Walker, J., & Waters, K. When the interface is a face. J. Human-Computer Interaction, 1996, vol. 11, 97--124. Google ScholarDigital Library
- Tapus, A., Tapus, C., & Mataric, M. User-robot personality matching and assistive robot behavior adaptation for post-stroke rehabilitation therapy. Intelligent Service Robotics, 2008, 1:169--183.Google ScholarCross Ref
- Tausczik, Y. & Pennebaker, J. The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods. J. of Language and Social Psychology, 2010, 29(1): 24--54.Google Scholar
- Tintarev, N, Dennis, M., & Masthoff, J. Adapting Recommendation Diversity to Openness to Experience: A Study of Human Behavior. UMAP 2013: 190--202.Google Scholar
- Turk, M. Multimodal interaction: A review. Pattern Recognition Letters 36: 189--195 (2014). Google ScholarDigital Library
- Uziel, L. Impression management ("lie") scales are associated with interpersonally oriented self-control, not other deception. Journal of Personality, 2013, 82(3): 200--212.Google ScholarCross Ref
- Wallace, R., Tomabechi, H., & Aimless, D. Chatterbots Go Native: Considerations for an eco-system fostering the development of artificial life forms in a human world. http://www.pandorabots.com/pandora/pics/chatterbotsgonative.doc. 2007.Google Scholar
- Weisberg, Y., DeYoung, C., & Hirsh, J. Gender differences in personality across the ten aspects of Big Five. J. Front Psychology, 2011, 2:178.Google ScholarCross Ref
- Weizenbaum, J. ELIZA -- a computer program for the study of natural language communication between man and machine. Comm. of the ACM, 1966, 9:36--45. Google ScholarDigital Library
- Wexelblat, A. Don't make that face: a report on anthropomorphizing and interface. AAAI'98 173--179.Google Scholar
- Yarkoni, T. Personality in 100,000 words: A largescale analysis of personality and word usage among bloggers. J. Res. in Personality, 2010, 44(3): 363--373.Google Scholar
- Yu, R., Liu, Y., & Yang, M. Does interviewer personality matter for survey outcomes' Proc. 64th Annual Conference on World Association for Public Opinion Research, 2011.Google Scholar
- Ziegler, M., MacCann, C., & Roberts, R. (editors). New perspectives on faking in personality assessment. 2010, Oxford University Press.Google Scholar
Index Terms
- Confiding in and Listening to Virtual Agents: The Effect of Personality
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