1. Assessing Attractiveness in Online Dating Profiles Andrew T. Fiore Lindsay Shaw Taylor G.A. Mendelsohn Marti Hearst School of Information Department of Psychology University of California, Berkeley

2. In the U.S.: 63m know someone who has used a dating site 16m have used a dating site themselves 53m know someone who has gone on a date 7m have gone on a date themselves 64% of online dating users think the large pool helps people find a better date 47% of all online adults concur Source: Pew Internet and American Life Project

3. Perception and attraction, offline and online

6. Performing & perceiving self Performance of identity “giving” vs. “giving off” (Goffman 1959) Great capacity for control in online performance Signals convey information with varying degrees of certainty (Donath 1999) Conventional vs. assessment Social Information Processing, hyperpersonal comm. (Walther 1992, 1996)

7. What’s in a profile? Combination of fixed-choice categorical descriptors, free-text self-description, and photos Highly optimized self-presentations Carefully selected detail Unlimited time to craft Exaggerations? Lies? A lot of people mislead a little (Hancock et al. 2007) Do they reflect actual self? Ideal self?

8. PHilton81 Age: 27 Height: 5’8” Weight: 115 lbs Occupation: Heiress ABOUT ME “People say they envy my lifestyle, but I'm convinced that anyone with a little imagination can live ‘The Life.’” Sources: Wikipedia, “Confessions of an Heiress,” Reuters

9. Perceptions of profiles Substantial inferences from small cues — Walther’s SIP (Ellison et al. 2006) “Thin slices,” big inferences from bits of Facebook profiles (Stecher & Counts 2008) Fiore & Donath (2005) Messages received as proxy for attractiveness Different predictors for men and women Norton, Frost, & Ariely (2007) More information, less liking (better discrimination)

10. Norton, Frost, & Ariely (2007)

11. Methodology

12. Profiles (rating targets) 50 Yahoo! Personals profiles with photos 25 men, 25 women, 20 to 30 years old 5 of each from Atlanta, Boston, San Diego, Seattle, and St. Louis (geographic diversity) One profile randomly chosen from each of the first five pages of search results Random sample of recently active users

13. Fixed choice Free text Photo

14. Rating dimensions for profiles Attractive Genuine, trustworthy Masculine Feminine Warm, kind Self-esteem Extraverted Self-centered

19. Procedure Participants provide information about age, gender, sexual preference. We provided only profiles and pieces of the appropriate target gender. Rate randomly ordered profiles and pieces through the Web application for 50 min. Indicate own self-esteem and attractiveness on Likert-type scale. Debriefing, payment.

20. Participants (raters) Recruited through UC-Berkeley Xlab 41 women, 23 men, heterosexual 66% Asian Between 19 and 25 years old (median 21) Self-reported attractiveness: mean 2.8 on 0 to 4 scale Self-reported self-esteem: mean 2.7 on 0 to 4 scale

21. Results

22. Raw data and standardization Each profile and profile component rated by multiple participants: 29,120 total ratings “Ipsatization”: standardize by each participant, for each dimension Scales are arbitrary — what is “high” or “low” for a given participant, for a given dimension? Averaged ratings of each profile and profile piece on each dimension Necessary because data are sparse — few participants rated every piece of every profile

23. Checking for repetition effects Participants rated more than one piece from each profile — is this a problem? They never rated the exact same piece twice. Whole profiles generally presented after pieces. No systematic differences in ratings upon first exposure to a piece of a given profile and subsequent ratings of other pieces. Bottom line: We can safely use all the ratings for our analysis.

24. Attractiveness of whole profiles

25. Dimensions of whole profiles

26. Whole profiles and pieces Men’s whole-profile attractiveness Women’s whole-profile attractiveness Photo attractiveness.88.87 Free-text attractiveness.71.27 Fixed-choice attractiveness.47.23 *** *

27. Whole profiles and pieces

28. Attractiveness of profile pieces

29. Attractiveness of photos

30. Attractiveness of free text

31. Putting it together

32. The big picture: Modeling whole-profile attractiveness Men’s profiles + Photo attractive + Free-text attractive + Masculine – Warm and kind in photo + Genuine/trustworthy in photo + Photo attractive x fixed-choice attractive x free-text attractive Women’s profiles + Photo attractive + Free-text attractive – Masculine + Extraverted + Self-esteem in photo + Feminine in photo

33. Men’s whole profile attractiveness

34. What wasn’t associated with attractiveness Attractiveness of fixed-choice components (after adjusting for other component effects) Self-rated self-esteem or attractiveness of participants Length of text in free-text piece Use of positive or negative emotion words or self-references in profile text (measured with LIWCS)

35. Limitations Purely associational data, not causal Representativeness of participant sample Asians overrepresented among raters — problematic for studying attractiveness What is good is beautiful; what is beautiful is good (Dion et al. 1972) — a halo effect? But not all desirable dimensions were associated with attractiveness What do averages mean for dyadic phenomena?

36. What’s next? Systematically combine attractive and unattractive components — what dominates? Examine deal-makers and deal-breakers What role do the categorical pieces play in the process of identifying potential dates? Identify pairs of users about to meet — how do their perceptions based on profiles change when the meet face to face?

37. Thank you! Any questions? Andrew T. Fiore Lindsay Shaw Taylor G.A. Mendelsohn Marti Hearst For more information: http://www.ischool.berkeley.edu/~atf/ atf@ischool.berkeley.edu Thanks to the National Science Foundation and Microsoft Research for sponsoring this work.