1. Snap Judgments and Social Media: Forming Personality Perceptions on Twitter People form impressions of others quickly, and can do so with very little information. We examined how these impressions form online: Do people agree about Twitter users’ personalities? What do users’ network of friends and followers tell us about them? Questions 1. Do Twitter users’ profiles or social networks convey a coherent and interpretable impression to observers? 1. Do observers agree about their impressions across profiles and social networks? 1. What cues do observers use to form these impressions? People form impressions of others quickly, and can do so with very little information. We examined how these impressions form online: Do people agree about Twitter users’ personalities? What do users’ network of friends and followers tell us about them? Questions 1. Do Twitter users’ profiles or social networks convey a coherent and interpretable impression to observers? 1. Do observers agree about their impressions across profiles and social networks? 1. What cues do observers use to form these impressions? Introduction Participants 630 participants (age range: 18 – 76, 66% female) were recruited via the University of Oregon Human Subjects Pool (n = 307) and Amazon Mechanical Turk (n = 323). Targets 100 targets were selected from an initial pool of 10,000 randomly selected Twitter users. Targets were typical users with active accounts in a North American time zone. Procedure Participants saw 1 of 3 possible stimulus types: a Twitter user’s profile, a collage of 25 Twitter users who follow a particular user (“followers”) or a collage of 25 Twitter users a particular user follows (“friends”). Participants then evaluated the Twitter user’s Big 5 personality traits and other characteristics including liking, attractiveness, intelligence, and socioeconomic status. Each participant evaluated 5 Twitter users. Three independent coders recorded objective information (e.g., number of followers) and coded other characteristics from the profiles. Participants 630 participants (age range: 18 – 76, 66% female) were recruited via the University of Oregon Human Subjects Pool (n = 307) and Amazon Mechanical Turk (n = 323). Targets 100 targets were selected from an initial pool of 10,000 randomly selected Twitter users. Targets were typical users with active accounts in a North American time zone. Procedure Participants saw 1 of 3 possible stimulus types: a Twitter user’s profile, a collage of 25 Twitter users who follow a particular user (“followers”) or a collage of 25 Twitter users a particular user follows (“friends”). Participants then evaluated the Twitter user’s Big 5 personality traits and other characteristics including liking, attractiveness, intelligence, and socioeconomic status. Each participant evaluated 5 Twitter users. Three independent coders recorded objective information (e.g., number of followers) and coded other characteristics from the profiles. Method Stimuli Results: Consensus Results: Cues Consensus & Cues Independent observers formed consistent impressions of Twitter users, and their level of agreement was commiserate with what we see when strangers meet in face-to-face interactions for most traits. Participants also formed consistent impressions of the users when they saw different aspects of the user’s presence on Twitter. These impressions were based, at least in part, on cues in the users’ profiles: users who tweeted often were seen as more extraverted but less conscientious and intelligent, whereas users who wrote mostly positive tweets were well liked and were seen as conscientious, agreeable, emotionally stable and intelligent. Implications This can have significant real-life implications because viewers could include potential employers, business contacts, new friends and even potential romantic partners. Impressions that viewers make may influence decisions regarding whether or not to follow a particular user or invite them for an interview, even if the impressions are inaccurate. Consensus & Cues Independent observers formed consistent impressions of Twitter users, and their level of agreement was commiserate with what we see when strangers meet in face-to-face interactions for most traits. Participants also formed consistent impressions of the users when they saw different aspects of the user’s presence on Twitter. These impressions were based, at least in part, on cues in the users’ profiles: users who tweeted often were seen as more extraverted but less conscientious and intelligent, whereas users who wrote mostly positive tweets were well liked and were seen as conscientious, agreeable, emotionally stable and intelligent. Implications This can have significant real-life implications because viewers could include potential employers, business contacts, new friends and even potential romantic partners. Impressions that viewers make may influence decisions regarding whether or not to follow a particular user or invite them for an interview, even if the impressions are inaccurate. Friends Followers Profile Nicole M. Lawless DesJardins, Tad Falk & Sanjay Srivastava University of Oregon ConscAgreeExtraNeurOpenLikeAttractIntellSES # Tweets-0.29-0.120.300.10-0.17 0.05-0.33-0.20 # Friends-0.070.100.03-0.110.010.040.130.020.00 # Followers-0.08-0.070.21-0.10-0.05-0.080.17-0.150.05 English Quality0.280.30-0.210.020.040.290.130.370.31 Emotional Tone: Tweets0.420.570.12-0.420.210.410.230.310.34 Professional Appearance0.450.25-0.09-0.220.190.300.110.380.55 Emotional Tone: Profile Picture0.100.430.14-0.03-0.230.100.190.020.20 Note: N = 100; all r ≥.20 are significant, p <.05. For both emotional tone codes, higher values indicate more positive affect. Discussion % of variance in trait perceptions due to the target for each stimuli type similarity of trait perceptions across stimuli types correlations among trait perceptions and cues from Twitter profiles