CHI 2014 Panel: Opportunities and Risks of Discovering Personality Traits from Social Media
•Download as PPTX, PDF•
2 likes•1,017 views
We will conduct a panel at CHI 2014 discussing the opportunities and implications of the coming wave of new analytics that allow individuals' intrinsic traits, such as personality and motivations, to be mined from their behaviors on social platforms. For more information, see the Facebook page for the panel here: https://www.facebook.com/events/633305060096913/
Recommended
More Related Content
Similar to CHI 2014 Panel: Opportunities and Risks of Discovering Personality Traits from Social Media
Similar to CHI 2014 Panel: Opportunities and Risks of Discovering Personality Traits from Social Media (20)
Recently uploaded
Recently uploaded (20)
CHI 2014 Panel: Opportunities and Risks of Discovering Personality Traits from Social Media
- 1. Interacting with Digital Individuals: Opportunities and Risks of Discovering Personality Traits from Social Media Moderators Michelle Zhou, Jeffrey Nichols Panelists Victoria Bellotti, Tom Dignan, Jennifer Golbeck, Jeff Hancock Facebook Page for Panel: https://t.co/2zSJ7f33F8
- 2. Michelle Zhou Jeffrey Nichols IBM Research – Almaden, IBM Watson Group
- 3. Outline • Introduction (30 min) • Digital individuals from Social Media (live demo) • Intro presentations from each Panelist • Q&A with audience (45 min) • Summary (5 min)
- 4. Analytics of Aggregates Monitoring and Reporting Analytics of Individuals Sentiment Listening Engagement Workflow Measurement Publishing Net Promoter Network Topology Intrinsic Traits What are people saying? How do people feel about my brand? Who is this individual? What motivates her? What is her taste and style? Next generation Early stages State of the art Social Genome
- 5. • Demographics • Birthday • Age • Home Location • Political Affiliation • Religion • Etc. • Intrinsic Traits • Personality (Big 5) • Basic Human Values (Motivations) • Fundamental Needs (Buying behaviors) • Emotional State • Etc. Information That Can Be Extracted From Social Media
- 6. IBM System U
- 9. Methodology: Personality Analytics “I love food, .., with … together we … in… very…happy.” Word category: Inclusive Agreeableness [Tausczik and Pennebaker ‘10, Yarkoni ‘10]
- 10. Automatically compute one’s personality traits Make hyper-personalized recommendations based on derived traits [Ford ‘05, O’Brien ‘96, Neuman ‘99, Gosling ‘03, Wholan ‘06] Do it for hundreds of millions of individuals Opportunities Individualization at Scale “Welcome to our store, would you like to take a personality test?”
- 11. Privacy invasion Veracity of social media Analytics imperfections Risks Individualization misfire “Your tweets tell us that you appear to have multiple personalities” “We do not hire vulnerable people like you”
- 12. Professor Communication, Information Science Cornell University Areas of Interest • Computer-mediated communication • Language and technology • Deception and its detection • Figurative language Jeff Hancock
- 13. Professor College of Information Studies University of Maryland Areas of Interest • Trust modeling • Personality and political preference from social media • Usable Security Jennifer Golbeck
- 14. Vice President, Head of Research Reputation.com Areas of Interest • Reputation scoring • Big data analytics and platforms for real-world systems Tom Dignan
- 15. Reputation.com is: The pioneering leader in the online reputation management & digital privacy space We monitor the online presence of individuals and businesses What shows up in your search results What people say about you on Facebook, Twitter, and other social media sites What PPI do you have exposed What photos or videos are tagged with your name What public records are exposed We assess and manage your digital footprint Is the content about you negative or positive (sentiment analysis) How much personal information do you have exposed Where is your online presence lacking What Does Reputation.com Do?
- 16. The Driving Factors for Reputation.com’s Business: As more and more of people’s lives are conducted online, there is a growing desire to enjoy the benefits of interacting on the internet whilst maintaining control of one’s online profile, presence and personal data. Philosophy: people and businesses have a right to control, protect their online reputations and privacy. Online reputation will only grow in importance: ample research demonstrates that consumers are actively searching online and trusting what they find – and businesses are materially impacted by social media/review feedback. What is Driving Reputation.com’s Business?
- 17. Research Fellow PARC Areas of Interest • Ethnography • Task and activity management • Context-aware computing • Sharing economy and collaborative consumption Victoria Bellotti
- 18. Context-Aware Computing: Activity Spotting Time No. of crowdsourced activity features spotted per interval Inferential threshold Writing a paper Not writing a paper
- 19. Ben Sutherland - Flickr Rethinking the TimeBanking Metaphor: Do humans need to be paid to be nice? Policies and Practice: Doing the Right Thing Wednesday 2:00-3:30pm Room: 801B Talk is scheduled to start at 3:10 pm
- 20. Q&A • How to interpret or measure the accuracy of personality traits derived from social media? • What factors (e.g., data sources and analytics methods) may affect accuracy? • What could the derived personality traits be used for individuals and businesses? • Who would benefit the most? • What are the risks of using such technologies, especially from an individual perspective? • Who is at risk? • How can we protect ourselves? • …
- 21. Summary • Hyper-Personalization • It is feasible now, but do we really want it? • If we do, what we want and don’t want • What kind of price will we be willing to pay for it? • Research • Do we really know a person’s inner most traits vs. we just know something about him/her that can help predict and influence the person’s behavior? • Society • Emergence of a new industry and economy: Data Banking and Exchanges
- 22. Interact with Us on Facebook https://t.co/2zSJ7f33F8
- 24. Validation How good are our results compared to standard psychometric studies? How well can our results be used to predict or influence one’s behavior? Yarkoni ’10, Adali ‘12, Chen ‘14, Gou ’14 … Mahmud ‘13, Lee ‘14
- 25. Results • RV-Coefficient correlation analysis of each type of trait • Over 80% of population, their correlation is statistically significant (80.8%, 98.21%, and 86.6% for Big 5 personality, basic values and needs) [Gou et al. CHI 2014]
- 26. References • Chen, J., Hsieh, G., Mahmud, J., and Nichols, J. Understanding individuals personal values from social media word use. In ACM Proc. CSCW ’2014. • Ford, J. K. Brands Laid Bare. John Wiley & Sons, 2005. • Gou, L., Zhou, M.X., and Yang, H. KnowMe and ShareMe: Understanding automatically discovered personality traits from social media and user sharing preferences. In ACM Proc. CHI 2014. • Lee, K., Mahmud, J., Chen, J., Zhou, M.X., and Nichols, J. Who will retweet this? Automatically identifying and engaging strangers on Twitter to spread information. In ACM Proc. IUI ‘2014. • Luo, L., Wang, F., Zhou, M.X., Pan, X., and Chen, H. Who’s got answers? Growing the pool of answerers in a smart enterprise Social Q&A system. In ACM Proc. IUI ‘2014. • Mahmud, J., Zhou, M.X., Megiddo, N., Nichols, J., and Drews, C. Recommending Targeted Strangers from Whom to Solicit Information in Twitter. In ACM Proc. IUI ‘2013. • Schwartz, S. H. Basic human values: Theory, measurement, and applications. Revue francaise de sociologie, 2006. • Tausczik, Y. R., and Pennebaker, J. W. The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology 29, 1 (2010), 24–54. • Yang, H., and Li, Y. Identifying user needs from social media. IBM Tech. Report (2013). • Yarkoni, T. Personality in 100,000 words: A large-scale analysis of personality and word use among bloggers. J. research in personality 44, 3 (2010), 363–373.
Editor's Notes
- Inferring Personality Won’t Be the Only ThingOne thing I want to describe that is relevant here is some work activity inferencing with Oliver Brdiczka at PARC. Oliver’s team is currently working on inferring personality from enterprise data such as someone’s email or work activities. This is fairly similar to what Michelle’s doing in the social media space, but inside the enterprise. Before this he and I collaborated on enterprise activity spotting. By this I mean activity defined in the human sense, like working on a patent or preparing for a sales meeting. What you see here is a depiction of our approach. We logged streams of input from the keyboard and mouseclicks and logged text in windows people opened and then looked at all the text in the log for specific word strings. These strings were words provided by people that they thought would be featuresof input when someone was working on a given activity. For example if the activity is a sales pitch, then you might expect to see the word product or customer a lot.By counting how many times any features suggested showed up in a given period we were able to assign a score that would go up and down. When it passed a certain threshold we would infer that someone was working on the activity that had those features. As far as we’re aware it’s the most accurate method of inferring enterprise work activity. So you can imagine the same sort of technology applied to predicting things about people from their behavior. For example someone could crowdsource the kind of information that would allow us to say; if anyone goes in here around 6:45 to 7pm and comes out an hour later any night of the week, you can infer that they are attending an AA meeting. So I am trying to set this issue of inferring personality in context with computers inferring lots of other things in combination with personality in the near future. Areother inferences more valuable and also more threatening?
- Peer-to-Peer Service ExchangeOK, so I didn’t just want to be a negative on all of this because some of the work I am doing now covers a great application domain for personality inferencing. We’re looking at peer-to-peer service exchange systems in which people offer and request services from other individuals in the community. Timebanking is the best-known non-profit form of this system and we are very interested in whatmotivates people to participate in both non-profit and for-profit systems of this nature. We’ll be talking about one facet of our research in a paper presentation tomorrow at 3 if you’re interested. But in the future, we plan to match service requestors to offerors based on their personality amongst other things. We would like to infer that from their behavior in social media such as Facebook, Twitter and Foursquare. We would do this to increase the chance of compatibility and the formation of a long-term social bond between parties to a transaction. For example, in Xerox we have a business in providing long-term care solutions, but in-home care is the most economical and preferred solution for most people. This is often possible with the support of a time bank in which mostly younger seniors help their less able elders and later on when they get older, they get help in return. Apart from practical physical challenges, loneliness is a big problem for seniors, so personality matching would be extremely valuable and we plan to try to do it in the coming year or two.