Studying Computer-Mediated Communication via Online Personals

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Studying Computer-Mediated Communication via
Online Personals

• Andrew Fiore, Marti Hearst, SIMS
• Lindsay Shaw, Jerry Mendelsohn, Psychology

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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

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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?

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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)

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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.
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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

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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

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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?

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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 wed expect by
   chance?

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• 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

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Widowed Separated Divorced Married In
relationship Never married (Invalid) No answer
Women
Men
Married In relationship
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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.

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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.

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Longer-Term Plans

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