Hagan, Courtney, Jordan Carpenter, Lyle Ungar, and Daniel Preoţiuc-Pietro. "Personality Profiles of Users Sharing Animal-related Content on Social Media." Anthrozoos (2017). AbstractDraft

Animal preferences are thought to be linked with more salient psychological traits of people and most research examining owner personality as a differentiating factor has obtained mixed results. The rise in usage of social networks offers users a new medium in which users broadcast their preferences and activities, including about animals. In two studies, the first on Facebook status updates and the second on images shared on Twitter, we revisited the link between user Big Five personality traits and animal preference, specifically focusing on cats and dogs. We used automatic content analysis of text and images to unobtrusively measure preference for animals online using large data sets. Results from Study 1 indicated that those who mentioned ownership of a cat (by using the phrase ‘my cat’) in their status updates were more open to experience, introverted, neurotic and less conscientious when compared to the general population, while users mentioning ownership of a dog (by using ‘my dog’) were only less conscientious compared to the rest of the population. Study 2 foundfinds that users who featured either cat or dog images in their tweets are more neurotic, less conscientious and less agreeable than those who do not. In addition, posting images containing cats was specific to users higher in openness, while posting images featuring dogs was associated with users higher in extraversion. These findings taken together align with some previous findings on the relationship between owner personality and animal preference, additionally highlighting some social media specific behaviors.

Carpenter, Jordan, Daniel Preoţiuc-Pietro, Lucie Flekova, Salvatore Giorgi, Courtney Hagan, Margaret Kern, Anneke Buffone, Lyle Ungar, and Martin Seligman. "Real Men don’t say 'cute': Using Automatic Language Analysis to Isolate Inaccurate Aspects of Stereotypes." Social Psychological and Personality Science (2016). AbstractDraftSupplemental MaterialsWebsite

People associate certain behaviors with certain social groups. These stereotypical beliefs consist of both accurate and inaccurate associations. Using large-scale, data driven methods with social media as a context, we isolate stereotypes by using verbal expression. Across four social categories - gender, age, education level, and political orientation - we identify words and phrases that lead people to incorrectly guess the social category of the writer. Although raters often correctly categorize authors, they overestimate the importance of some stereotype-congruent signal. Findings suggest that data-driven approaches might be a valuable and ecologically valid tool for identifying even subtle aspects of stereotypes and highlighting the facets that are exaggerated or misapplied.