Tweeting negative emotion: An investigation of Twitter data in the aftermath of violence on college campuses.

Studying communities impacted by traumatic events is often costly, requires swift action to enter the field when disaster strikes, and may be invasive for some traumatized respondents. Typically, individuals are studied after the traumatic event with no baseline data against which to compare their postdisaster responses. Given these challenges, we used longitudinal Twitter data across 3 case studies to examine the impact of violence near or on college campuses in the communities of Isla Vista, CA, Flagstaff, AZ, and Roseburg, OR, compared with control communities, between 2014 and 2015. To identify users likely to live in each community, we sought Twitter accounts local to those communities and downloaded tweets of their respective followers. Tweets were then coded for the presence of event-related negative emotion words using a computerized text analysis method (Linguistic Inquiry and Word Count, LIWC). In Case Study 1, we observed an increase in postevent negative emotion expression among sampled followers after mass violence, and show how patterns of response appear differently based on the timeframe under scrutiny. In Case Study 2, we replicate the pattern of results among users in the control group from Case Study 1 after a campus shooting in that community killed 1 student. In Case Study 3, we replicate this pattern in another group of Twitter users likely to live in a community affected by a mass shooting. We discuss conducting trauma-related research using Twitter data and provide guidance to researchers interested in using Twitter to answer their own research questions in this domain. (PsycINFO Database Recor

DittoSupplemental_figure_1_copy – Supplemental material for At Least Bias Is Bipartisan: A Meta-Analytic Comparison of Partisan Bias in Liberals and Conservatives

<p>Supplemental material, DittoSupplemental_figure_1_copy for At Least Bias Is Bipartisan: A Meta-Analytic Comparison of Partisan Bias in Liberals and Conservatives by Peter H. Ditto, Brittany S. Liu, Cory J. Clark, Sean P. Wojcik, Eric E. Chen, Rebecca H. Grady, Jared B. Celniker, and Joanne F. Zinger in Perspectives on Psychological Science</p

At Least Bias Is Bipartisan: A Meta-Analytic Comparison of Partisan Bias in Liberals and Conservatives

Both liberals and conservatives accuse their political opponents of partisan bias, but is there empirical evidence that one side of the political aisle is indeed more biased than the other? To address this question, we meta-analyzed the results of 51 experimental studies, involving over 18,000 participants, that examined one form of partisan bias—the tendency to evaluate otherwise identical information more favorably when it supports one’s political beliefs or allegiances than when it challenges those beliefs or allegiances. Two hypotheses based on previous literature were tested: an asymmetry hypothesis (predicting greater partisan bias in conservatives than in liberals) and a symmetry hypothesis (predicting equal levels of partisan bias in liberals and conservatives). Mean overall partisan bias was robust (r = .245), and there was strong support for the symmetry hypothesis: Liberals (r = .235) and conservatives (r = .255) showed no difference in mean levels of bias across studies. Moderator analyses reveal this pattern to be consistent across a number of different methodological variations and political topics. Implications of the current findings for the ongoing ideological symmetry debate and the role of partisan bias in scientific discourse and political conflict are discussed

Reducing implicit racial preferences: I. A comparative investigation of 17 interventions.

Many methods for reducing implicit prejudice have been identified, but little is known about their relative effectiveness. We held a research contest to experimentally compare interventions for reducing the expression of implicit racial prejudice. Teams submitted 17 interventions that were tested an average of 3.70 times each in 4 studies (total N = 17,021), with rules for revising interventions between studies. Eight of 17 interventions were effective at reducing implicit preferences for Whites compared with Blacks, particularly ones that provided experience with counterstereotypical exemplars, used evaluative conditioning methods, and provided strategies to override biases. The other 9 interventions were ineffective, particularly ones that engaged participants with others' perspectives, asked participants to consider egalitarian values, or induced a positive emotion. The most potent interventions were ones that invoked high self-involvement or linked Black people with positivity and White people with negativity. No intervention consistently reduced explicit racial preferences. Furthermore, intervention effectiveness only weakly extended to implicit preferences for Asians and Hispanics