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 Social Justice and Verbal Behavior: Evaluating Effects of Empathy Training and Exploring Connections Between Anti-Islamic Incidents and Verbal Behavior |
| Sunday, May 29, 2022 |
| 6:00 PM–6:50 PM |
| Meeting Level 1; Room 156A |
| Area: CSS/VBC; Domain: Applied Research |
| Chair: Natalie M. Driscoll (Seven Hills Foundation & Endicott College) |
| Discussant: Noor Younus Syed (SUNY Empire State College; Anderson Center International; Endicott College) |
| CE Instructor: Noor Younus Syed, M.Ed. |
| Abstract: This presentation will focus on evaluating the effects of behavioral procedures to relational training and multiple exemplar training on empathic responding of individuals who display racial bias. The behavioral procedures include the presentation of relational frames of coordination and distinction between the participants’ values and the values of a person belonging to a group for which a bias existed altered empathic responses toward people belonging to such groups. This presentation will demonstrate findings to support that relational training resulted in altered patterns of empathic responses toward people belonging to different racial groups for which a bias previously existed. This presentation will also focus on the use of data science to identifying connections between anti-Islamic incidents and verbal behavior on Twitter. The relationship between online and offline activity was explored using Natural Language Processing (NLP). This presentation will demonstrate the use of data science tools to explore these connections. Behavior analysts can combine data science techniques with operant and respondent analyses of verbal behavior to predict events related to anti-discrimination, social justice efforts. |
| Instruction Level: Advanced |
| Keyword(s): data science, empathy, social justice, verbal behavior |
| Target Audience: Target audience for this event should have more than 5 years experience as masters level behavior analysts. |
| Learning Objectives: At the conclusion of the presentation, participants will be able to: 1. Describe key differences in the effects of presenting relational frames of coordination and relational frames of distinction on empathic responses. 2. Identify important features of relational frames of coordination and relational frames of distinction. 3. Discuss elements of measuring empathic responses 4. Describe how data science tools can be used to analyze large amounts of verbal behavior 5. Explain relationships between online and offline hate incidents from an operant-respondent paradigm. |
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| An Evaluation of the Effects of Empathy Training on Racial Bias |
| VICTORIA DANIELA CASTILLO (Endicott College) |
| Abstract: The purpose of the current study was to evaluate the effects of behavioral procedures, including
relational training and multiple exemplar training on empathic responding of individuals who
display racial bias. More specifically, this study used a multielement design with five adult
participants to evaluate whether the presentation of relational frames of coordination and
distinction between the participants’ values and the values of a person belonging to a group for
which a bias existed altered empathic responses toward people belonging to such groups. The
results showed empathic responding was higher when relational frames of coordination were
presented and was lower when relational frames of distinction were presented. Therefore, this
study demonstrated that relational training resulted in altered patterns of empathic responses
toward people belonging to different racial groups for which a bias previously existed. |
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| Islamophobia: Using Data Science to Explore Connections Between Anti-Islamic Incidents and Verbal Behavior on Twitter |
| ASIM JAVED (Endicott College) |
| Abstract: Over the past two decades, the Muslim community has been the target of an increasing number of anti-Islamic incidents. In a 2020 FBI report, anti-Islamic incidents were the second highest anti-religious crimes (e.g., verbal threats, intimidatory statements, or mosque vandalism). Previous studies have used algorithms to classify tweets as hateful or non-hateful and have explored associations between online and offline activity. However, these studies have only analyzed associations within very specific temporal windows. The purpose of this study was to examine the relationship between online and offline activity within various temporal windows. We did this by scraping 400,000 Tweets and using Natural Language Processing techniques to identify the content and sentiment of each tweet. These tweets were then compared temporally to a list of hate crimes published by the Council on American-Islamic Relations. We found noticeable differences in the strength of relationships depending on the temporal window used for analysis. Overall, this study demonstrates how data science tools allow us to explore the connections between online verbal behavior and offline events. Behavior analysts can combine data science techniques with operant and respondent analyses of verbal behavior to predict events related to anti-discrimination, social justice efforts. |
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