1. DOES LANGUAGE CHOICE PREDICT MOTIVATIONS FOR RELATIONSHIP INITIATION?: USING LIWC TO ANALYZE LINGUISTIC MARKERS OF INTENT IN ONLINE DATING PROFILES LIESEL SHARABI DAVID J. ROACHÉ

2. RATIONALE  Language as a window into culture (Chung & Pennebaker, 2012)  Advantages of using Linguistic Inquiry and Word Count (LIWC; Pennebaker, Booth, & Francis, 2007)  Large scale language analysis over a variety of platforms  Twitter: Arab spring (Elson et al., 2012), stock market (Grossman, 2010), Germany’s 2009 federal elections (Tumasjan, Sprenger, Sandner, & Welpe, 2010)  Facebook: Gross national happiness index (Kramer, 2010)  Online dating: Deception (Toma & Hancock, 2012; Hancock & Toma, 2009; Toma & Hancock, 2009)  Analyze Words detects personality characteristics

3. DATASET  Free and popular  Unique set of motivations  Intent (e.g., hang out, intimate encounter)  Looking for (e.g., wants relationship, looking for someone to marry)  No violation of Terms of Service

4. RESEARCH QUESTIONS RQ1: Do linguistic markers predict relational intent on POF? RQ2: Which linguistic markers will be the best predictors of relational intent on POF?

5. METHOD  Online Dating Profiles (N = 120)  Equal numbers of males and females  Equal numbers of profiles from six categories of intent  Looking for a commitment (66.7%; n = 80)  Not looking for a commitment (33.3%; n = 40)  Demographics  Ages ranged from 18 to 61 (M = 33.63; SD = 10.32)  Mostly Caucasian (69.2%; n = 83), followed by Black (17.5%; n = 21), Hispanic (8.3%; n = 10), Mixed Race (1.7%; n = 2), Other (1.7%; n = 2), Asian (.8%; n = 1), and Native American (.8%; n = 1)  Data Analysis  Linguistic Inquiry and Word Count (LIWC) (Pennebaker, Booth, & Francis, 2007)  Stepwise logistic regression with forward selection

6. RESULTS

7. THANK YOU