Studying Computer-Mediated Communication via Online Personals

Andrew Fiore, Marti Hearst, SIMSLindsay Shaw, Jerry Mendelsohn, Psychology

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

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?

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)

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.

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

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

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?

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?

Characteristic Exp. % same Actual % same t stat.

Marital status 31.6 56.0 (1.77x) 76.00

Wants children 25.1 40.5 (1.61x) 48.55

Num. of children 27.8 38.6 (1.39x) 34.35

Physical build 19.2 25.6 (1.33x) 22.44

Smoking 40.5 54.0 (1.33x) 41.98

Phys. appear. 37.6 49.2 (1.31x) 35.89

Educational level 23.6 29.3 (1.24x) 19.36

Religion 42.4 52.6 (1.24x) 31.59

Race 71.1 85.9 (1.21x) 65.81

Drinking habits 61.2 73.4 (1.20x) 42.69

Pet preferences 34.7 39.9 (1.15x) 16.43

Pets owned 21.8 24.0 (1.10x) 8.04

Widowed

Separated

Divorced

Married

In relationship

Never married

(Invalid)

No answer

Women

Men

Married

In relationship

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.

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.

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