1. Studying Computer-Mediated Communication via Online Personals Andrew Fiore, Marti Hearst, SIMS Lindsay Shaw, Jerry Mendelsohn, Psychology

2. Computer-Mediated Communication  People now work and play together at a distance Students get degrees via distance courses International teams write software and design products together Groups write position papers and organize political activities People provide advice and other services

3. Computer-Mediated Communication  How does online communication differ from face-to-face? People are more likable? Less? Communication is better? Worse? Different?  How to design CMC systems to best promote positive relationships?

4. How is this studied?  To date, mainly by small controlled studies  Example: Walther et al. 96, 01 Studied online workgroups Pairs of students working on class projects 2x2 design (short- or long-term interaction, presence/absence of photos)

5. Walther et al. on CMC and Affinity Found that users experienced affection and social attraction: 1. Most of all in long-term online groups without photographs. 2. Less so in long-term online groups with photographs and short-term online groups with photographs. 3. Least of all in short-term online groups without photographs. Hyperpersonal Personal (offline norm) Impersonal Hyperpersonal interaction: accelerated affinity via wishful thinking in the absence of strong social cues.

6. Scaling Up the Studies  Controlled studies are very useful, but they are necessarily small  Millions of people are interacting online, so how can we leverage this massive- scale interaction for study?  Idea: Study online personals

7. Online Personals  A HUGE socio-technical phenomenon US has ~80 Million single adults In 2003, ~40 Million UNIQUE visitors to online personals websites A virtually untapped data source for studying technology-mediated interactions  Virtually untapped

8. Example (Fiore & Donath ’05): Data from an online personals site  Anonymized eight-month snapshot June 2002 to February 2003 153,942 completed user profiles  Messaging: who contacted whom, when, how much, and who replied. 29,687 users sent 236,930 messages  51,348 distinct recipients 110,722 distinct contacts  One or more msgs sent between two users  Only 21.8 percent were reciprocated  Question: Does Homophily hold? How similar are people to those whom they contact, and on which features?

9. Method of analysis 1. Calculate percent of dyads we would expect to be the same on a given dimension if they consisted of randomly selected men and women. 2. Calculate actual percent of dyads the same on that dimension from dating site data. 3. Compare actual and expected percentages. Is actual similarity greater than we’d expect by chance?

10. CharacteristicExp. % sameActual % samet stat. Marital status31.656.0 (1.77x)76.00 Wants children25.140.5 (1.61x)48.55 Num. of children27.838.6 (1.39x)34.35 Physical build19.225.6 (1.33x)22.44 Smoking40.554.0 (1.33x)41.98 Phys. appear.37.649.2 (1.31x)35.89 Educational level23.629.3 (1.24x)19.36 Religion42.452.6 (1.24x)31.59 Race71.185.9 (1.21x)65.81 Drinking habits61.273.4 (1.20x)42.69 Pet preferences34.739.9 (1.15x)16.43 Pets owned21.824.0 (1.10x)8.04

11. Widowed Separated Divorced Married In relationship Never married (Invalid) No answer WomenMen Married In relationship

12. Studying Hyperpersonal Interaction in the Online Personals Context  Issue: people disappointed with face-to-face meetings based on profiles and earlier interactions Are people lying on their profiles? Or … are people experiencing inflated expectations caused by the CMC?  Hyperpersonal interactions:  Accelerated affinity via wishful thinking in the absence of strong social cues.

13. Near-Term Plans  Test the inflated expectations theory Conduct a survey to determine expectations before and after F2F Analyze results with respect to a wide range of factors Use analysis to determine how to better align expectations.

14. Longer-Term Plans  Use social psych research to Understand problems with current CMC systems  People are poor at self-description  -> How to improve descriptions? Understand what makes for good matches  Complementarity vs. Compatibility  Translate this into CMC representation  Design better systems