Assessing Attractiveness in Online Dating Profiles.ppt

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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,In the U.S.:63m know someone who has used a dating site16m have used a dating site themselves53m know

2、someone who has gone on a date7m have gone on a date themselves64% of online dating users think the large pool helps people find a better date47% of all online adults concurSource: Pew Internet and American Life Project,Perception and attraction, offline and online,Performing & perceiving self,Perfo

3、rmance 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),Whats in a profi

4、le?,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?,P

5、Hilton81Age: 27 Height: 58” Weight: 115 lbs Occupation: HeiressABOUT ME “People say they envy my lifestyle, but Im convinced that anyone with a little imagination can live The Life.”,Sources: Wikipedia, “Confessions of an Heiress,” Reuters,Perceptions of profiles,Substantial inferences from small cu

6、es Walthers 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 d

7、iscrimination),Norton, Frost, & Ariely (2007),Methodology,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 firs

8、t five pages of search results Random sample of recently active users,Rating dimensions for profiles,Attractive Genuine, trustworthy Masculine Feminine Warm, kind Self-esteem Extraverted Self-centered,Procedure,Participants provide information about age, gender, sexual preference. We provided only p

9、rofiles 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.,Participants (raters),Recruited through UC-Berkeley Xlab 41 women, 23 men, het

10、erosexual 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,Results,Raw data and standardization,Each profile and profile component rated by multiple participants: 29,120 total ratings “Ipsatiz

11、ation”: 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

12、profile,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

13、 subsequent ratings of other pieces. Bottom line: We can safely use all the ratings for our analysis.,Attractiveness of whole profiles,Dimensions of whole profiles,Whole profiles and pieces,*,*,*,*,Whole profiles and pieces,Attractiveness of profile pieces,Attractiveness of photos,Attractiveness of

14、free text,Putting it together,The big picture: Modeling whole-profile attractiveness,Mens 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,Womens profiles + Photo at

15、tractive + Free-text attractive Masculine + Extraverted + Self-esteem in photo + Feminine in photo,Mens whole profile attractiveness,What wasnt associated with attractiveness,Attractiveness of fixed-choice components (after adjusting for other component effects) Self-rated self-esteem or attractiven

16、ess 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),Limitations,Purely associational data, not causal Representativeness of participant sample Asians overrepresented among raters problematic for stud

17、ying 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?,Whats next?,Systematically combine attractive and unattractive components what d

18、ominates? 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?,Thank you! Any questions?,Andrew T. Fiore Lindsay Shaw Taylor G.A. Mendelsohn Marti HearstFor more information: http:/www.ischool.berkeley.edu/atf/ atfischool.berkeley.eduThanks to the National Science Foundation and Microsoft Research for sponsoring this work.,