1. The Role of Optimal Distinctiveness and Homophily in Online Dating
Daniel M. Romero School of Information University of Michigan
In collaboration with Danaja Maldeniya, Arun Varghese and Toby Stuart

2. Self-presentation and success in online dating
What affects the odds that a potential romantic partner responds to a message on a dating site?
Homophily across numerous sociodemographic dimensions [Fiore and Donath 2005, Hitsch et al. 2010, Anderson et al. 2014]
Network of communication on dating site [Xia et al. 2014].

3. In This Talk message Bob Alice Bob’s competition
Will Bob get a response?
Does the probability that Bob gets a response depend on:
The similarity between Bob and Alice (Homophily)?
The similarity between Bob and his competition?

4. Optimal Distinctiveness Theory
Individuals in groups feel the need to balance:
The need to assimilate and belong to the group.
The desire to stand out and be unique. [Brewer 1991]
Hypothesis: In a dating site, a user competing for the attention of another needs to balance:
Being similar enough to a potential romantic partner (compatibility).
Standing out from the competition.

5. Online Dating Data
3 months of anonymized user activity from a popular US dating site:
10 metropolitan regions
~410K active users
25M messages, 286M clicks and 864M ratings
User Data
Profile information: Demographics & Free-text descriptions of themselves.
Viewing profile history
Ratings

6. Sexual Orientation by Gender
Gender Dynamics
Straight
Gay
Bisexual
Total
Male
54.0
0.3
0.9
55.2
Female
39.5
0.7
4.6
44.8
93.5
1.0
5.5
100
Sexual Orientation by Gender

7. Sexual Orientation by Gender
Gender Dynamics
Straight
Gay
Bisexual
Total
Male
54.0
0.3
0.9
55.2
Female
39.5
0.7
4.6
44.8
93.5
1.0
5.5
100
Sexual Orientation by Gender

8. Gender Dynamics Sexual Orientation by Gender
Straight
Gay
Bisexual
Total
Male
54.0
0.3
0.9
55.2
Female
39.5
0.7
4.6
44.8
93.5
1.0
5.5
100
Sexual Orientation by Gender
Receiver
Male
Female
Total
Sender
4.8
57.7
62.5
35.2
2.3
37.5 40 60
100
Message Sending and Receiving by Gender

9. Gender Dynamics Sexual Orientation by Gender
Straight
Gay
Bisexual
Total
Male
54.0
0.3
0.9
55.2
Female
39.5
0.7
4.6
44.8
93.5
1.0
5.5
100
Sexual Orientation by Gender
Receiver
Male
Female
Total
Sender
4.8
57.7
62.5
35.2
2.3
37.5 40 60
100
Message Sending and Receiving by Gender

10. Age and Gender Distribution of Users
Majority of the users are young (45%+ in their 20s).
There are slightly more males than females across all ages.

11. Message Sent by Age and Gender
Males are over-represented in message volume in every age group.

12. Initiations by Age and Gender
Males initiate interactions far more often than females at every age group.

13. Fraction of initiations by Females
Fraction of initiations by females increases with age.

14. Gender Dynamics 93% of the messages involve male-female dyads
Males account for:
55% of the users
62% of all messages
86% of the contact initiations in male-female dyads
We focus our study on male-female messages and male initiated communication.

15. Market-level Competition Network
Connect any two males who messaged at least one female in common.

16. Female-choice Competition Network
Connect male to other males who messaged same female in the past t0

17. Profile View Competition Network
Connect male to other males whose profile was viewed by female. t0

18. Dyad and Competition Text Similarity
Dyad Similarity: Similarity between a male and a female he messaged.
Competition Similarity: average similarity between a male and his competition.
Bob
Alice

19. Profile Text Similarity
For each user, find TF-IDF vectors using bag-of-words from profile.
TF-IDF vector is a weighted representation of the words in a profile. Words are weighted by importance based on frequency in the focal profile and all others.
For users u and v ,with profile TF-IDF vectors Vu and Vu, text similarity, 𝑆 𝑢,𝑣 is the cosine similarity of the two vectors

20. Reply probability vs. Dyad Similarity
Homophily plays a role

21. Reply Probability vs. Competition Similarity
Market Level Competition
Female Choice Competition
Distinctiveness associated with better odds for the male

22. Reply Probability vs. Competition Similarity
A male has better odds with a female if his profile is either very different or very similar to the profiles of males she views.

23. Linear Probability Model
Will She Reply?
Text Similarity to Female
Text Similarity to the Competition +
Dyad profile feature similarities (12 variables)
Female’s response rate
Female’s age Dyad co-location
Attractiveness of male compared to competition
Attractiveness of male compared to female
Control Variables

24. Regression Results Distinctiveness Better ratings than competition
Better ratings than female
Homphily

25. Effect of Dyad Similarity vs. Female’s Age
Tendency for homophily remains stable as female grows older

26. Effect of Competition Similarity vs. Female’s Age
As age increases uniqueness of the male becomes less important

27. Effect of Dyad Attarctiveness vs. Female’s Age
Female becomes less interested in the the male’s attractiveness relative to her as she ages

28. Effect of Competition Attractiveness vs. Female’s Age
Female becomes less interested in the the male’s attractiveness relative to the competition as she ages

29. Interaction Between Attractiveness and Distinctiveness
Variable
MLC
FCC
PVC
MLC Text Similarity -
FCC Text Similarity
PVC Text Similarity
MLC Attractiveness
0.0967
FCC Attractiveness
0.1237
PVC Attractiveness
0.0911
MLC (Text Similarity x Attractiveness)
0.0206
0.0127
0.0299 ….
Being attractive diminishes the need for a male to be distinctive in receiving a response

30. Discussion Main Findings
Homophily: Dyad similarity increases likelihood of response.
Distinctiveness: Being different from the competition increases likelihood of response.
Implications
From a strategy perspective: males on dating sites need to balance exhibiting common interests with females while standing out from the competition.
Insights for improving user recommendations: link female to a diverse set of males who are different from each other but similar to her.
Future Work
How does similarity between users and their own sociodemographic groups affect their success on the dating site?