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Opportunities and risks of discovering personality traits from social media

Published: 26 April 2014 Publication History

Abstract

With the emergence of social media and the availability of big data, there has been much interest in mining the digital footprints left by users to predict personality traits (e.g., introvert and idealistic) and gain a deeper understanding of individuals. While such understanding will enable hyper-personalized computing, such as personality-based marketing, the use of this technology will have far-reaching social implications that could affect almost every aspect of our lives. For example, personality traits mined from social media could be used to guide hiring and promotion decisions or decide who is admitted into top academic programs. The risks of using derived personality traits are potentially high, particular due to factors such as the veracity of data collected from social media, imperfections in prediction algorithms, and a lack of control over how, when, and to whom anyone's personality traits might be exposed. We will use this panel to bring together experts from the fields of Psychology, Social Science, Computer Science, along with the CHI community, to discuss and debate the opportunities and risks of personality discovery from social media and the implications on technical communities and our society at large.

References

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Chen, J., Hshieh, G., Mahmud, J. and Nichols, J. Understanding Individuals' Personal Values with Social Media Word Uses, Proc. CSCW '14. To appear.
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Gilbert, G., and Karahalios, K. Predicting tie strength with social media. CHI 2009:211--220.
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Golbeck, J., Robles, C., Edmondson, M., and Turner, K. (2011). Predicting personality from twitter. In IEEE Social Computing, 149--156.
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Gou, L, Zhou, M. X., and Yang, H. KnowMe and ShareMe: Understanding Personality Traits Derived from Social Media and User Privacy Preferences. CHI 2014. Conditionally Accepted.
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Mahmud, J., Zhou, M. X., Megiddo, N., Nichols, J., and Drews, C. Recommending Targeted Strangers from Whom to Solicit Information on Social Media, Proc. IUI 2013, 37--48.
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Maslow, A. H. (1943). A theory of human motivation. Psychological Review, 50(4), 370--96.
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Pennacchiotti, M., Popescu, A. M. 2011. A Machine Learning Approach to Twitter User Classification, In Proc. ICWSM 2011.
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Schwartz, S. H. (2006). Basic human values: Theory, measurement, and applications. Revue française de sociologie.
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Tausczik, R. and Pennebaker, J. 2010. The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods, J. of Lang. and Social Psychology 29(1) 24--54.
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Yang, H. and Li, Y. Identifying User Needs from Social Media. IBM Tech Report. goo.gl/2XB7NY
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Yarkoni, Tal. 2010. Personality in 100,000 words: A largescale analysis of personality and word usage among bloggers. Journal of Research in Personality.
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http://venturebeat.com/2013/10/08/ibm-researcher-can-decipher-your-personality-in-200-tweets/
[14]
http://www.technologyreview.com/news/520671/ads-could-soon-know-if-youre-an-introvert-on-twitter/
[15]
http://www.dailymail.co.uk/news/article-2508757/Employers-use-personality-profiling-technology-analyse-tweets.html

Cited By

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  • (2024)Analysis of Software Developers' Programming Language Preferences and Community Behavior From Big5 Personality TraitsSoftware: Practice and Experience10.1002/spe.338155:3(473-490)Online publication date: 14-Oct-2024
  • (2018)Privacy Shield securing privacy in social networks2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)10.1109/ICCIC.2018.8782322(1-4)Online publication date: Dec-2018
  • (2016)PriGuardIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2016.258342528:10(2724-2737)Online publication date: 1-Oct-2016
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    cover image ACM Conferences
    CHI EA '14: CHI '14 Extended Abstracts on Human Factors in Computing Systems
    April 2014
    2620 pages
    ISBN:9781450324748
    DOI:10.1145/2559206
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 26 April 2014

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    Author Tags

    1. deep people understanding
    2. personality traits
    3. privacy
    4. psycholinguistic analytics
    5. social media

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    CHI '14
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    CHI '14: CHI Conference on Human Factors in Computing Systems
    April 26 - May 1, 2014
    Ontario, Toronto, Canada

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    CHI EA '14 Paper Acceptance Rate 1,000 of 3,200 submissions, 31%;
    Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

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    Cited By

    View all
    • (2024)Analysis of Software Developers' Programming Language Preferences and Community Behavior From Big5 Personality TraitsSoftware: Practice and Experience10.1002/spe.338155:3(473-490)Online publication date: 14-Oct-2024
    • (2018)Privacy Shield securing privacy in social networks2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)10.1109/ICCIC.2018.8782322(1-4)Online publication date: Dec-2018
    • (2016)PriGuardIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2016.258342528:10(2724-2737)Online publication date: 1-Oct-2016
    • (2016)Advancing Models and Theories for Digital Behavior Change InterventionsAmerican Journal of Preventive Medicine10.1016/j.amepre.2016.06.01351:5(825-832)Online publication date: Nov-2016

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