Psychology Bias in Predicted Behavior: Affective Forecasting in Response to Online Dehumanization of Immigrants Reyhaneh Bagherian Shamir * , Tonya M. Buchanan, PhD Project Mentor(s): Tonya M. Buchanan, PhD Over 5 billion people use social media as one of their main communication tools (Petrosyan, 2025). Although social media platforms connect individuals with few time and location limitations, users are often exposed to harmful content such as discriminatory language (e.g., dehumanization). Previous research showed that individuals are not accurate in predicting their behavior and emotions (often overpredicting the magnitude emotional/behavioral responses), including in response to witnessing in- person discriminatory behavior, with participants predicting their responses expecting to feel more negative affect and be more likely to reject the racist confederate more than those who actually witnessed the event (Kawakami, 2009). The current research examines how accurately people predict their responses to dehumanizing content in online interactions using a 2x2 between-subjects design with participants (experiencers vs. forecasters) responding to online comments (dehumanizing vs. non- dehumanizing) from X users to an article that reports an increase in the number of U.S immigrants. After reading the article and comments, participants completed the PANAS to assess current (or forecasted) affect and rated their likelihood of engagement with the content. Data collection is in progress (current N = 237), with completion expected prior to the conference date. We expect that forecasters will expect to feel more negative affect and report a higher likelihood of reporting content than experiencers actually feel and report. We expect these differences to be larger in the dehumanizing (vs non- dehumanizing) language condition. These results would suggest that affective forecasting errors also Introductory Classroom Support Impacting Academic Self-Efficacy: Expanding the Applicability of SEOI Assessments Ryan Brookman * Project Mentor(s): Mary Radeke, PhD For many years, institutions of higher education have faced a persistent challenge: retaining students and ensuring they successfully complete their programs. Decades of studies have been aimed towards this endeavor, with many identifying the development of belongingness and self-efficacy as being linked to higher retention rates. These can be developed through a student’s perception of being properly supported through their academic and personal pursuits. This study focuses on how that development can occur within the controlled environment of the classroom by identifying students’ perceived institutional and instructional support. To minimize changes in current procedures at CWU, this study will be testing to see if an archival analysis of the current Student Evaluation of Instruction (SEOI) assessment tool used by CWU can be further utilized to identify correlations between values associated with perceived support and values associated with predicted academic outcomes (predicted grades). This study analyzes 15 years of SEOI data from identical introductory courses by the same professor to assess student-perceived support, aiming to inform interventions that boost self-efficacy and retention at CWU. Presentation Type: Poster Presentation (May 21, 9:30am–3:00pm) Keywords : Retention, Self-efficacy, Support, Perception SOURCE Form ID: 121 apply to responses to more subtle forms of prejudice on social media. Presentation Type: Oral Presentation (May 20, 9:30am–5:00pm) Keywords: Affective forecasting, Dehumanization, Social media, Immigrants SOURCE Form ID: 54
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