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

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].

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?

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.

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

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

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

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

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

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.

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

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

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

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.

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

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

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

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

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

Reply probability vs. Dyad Similarity Homophily plays a role

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

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.

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

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

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

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

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

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

Interaction Between Attractiveness and Distinctiveness Variable MLC FCC PVC MLC Text Similarity -0.0279 - FCC Text Similarity -0.0220 PVC Text Similarity -0.0005 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

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?