Social Dynamics: Coding Blame and Emotion in Social Media Multidisciplinary Team: Sara Levens (Psychology), Cherie Maestas (Political Science), Samira Shaikh (Psychology and Computer Science), James Walsh (Political Science), Wlodek Zadrozny (Computer Science)

Meanings of events are socially constructed Motivation: When, Why and How do events erupt into social movements, boycotts, protests, and backlash? Meanings of events are socially constructed Emotion and Emotion Contagion Blame Attribution and Blame Centering

Studying Emotion and Blame Dynamics are Critical to Industry and Economic Health Identify emerging critical events that affect branding and consumer confidence Identify ways to de-escalate boycotts or other brand-based protests on social media Identify social dynamics of blame cascades to improve management of critical events Identify emergent social movements or protests that affect commerce or economic activity

Scientific Challenge: Measure emotion and blame in social media in near real-time to understand and predict event related attitudes and behavior. Current State-of-the-Art is Insufficient: Most text analysis code positive or negative sentiment rather specific emotions that motivate social contagion. Current approaches do not incorporate measurement of blame attribution or event-specific symbols. Need to develop sophisticated approaches for detecting discrete emotion and blame attribution to utilize established psychology and political science theories to predict dynamics of social-emotional escalation and de-escalation of events.

Flash Surveys Train Algorithm Classify Social Text Objective Rapidly identify population expression of emotion and blame in response to novel or unexpected large-scale events Approach Conduct “flash” surveys to extract event- specific emotion and blame language Use survey data to train machine algorithm to detect event specific emotion and blame in social media text Apply algorithm to streams of social media text in near real time to track the dynamics of emotion and blame during life cycle of event Unexpected Event

Proposed Experiments Training Data Testing Data Flash Survey Text: Respondent’s self-coded emotion & blame reactions to event used as training data Machine Learning Algorithms (Supervised, Semi-Supervised, Active Learning...) Large Scale Validation on Social Media Streams coded for Anger, Disgust, Happiness, Fear, and Blame COMPARE validity and efficiency to: Traditional Approach: Human coded subset of testing data used as training data

Pilot Test: Inauguration Day Survey

Blame Joy Most of them voted for him simply because they hated Hillary Clinton. I am excited. It will be interesting to see if he can improve the country. Fear Anger I am a bit apprehensive to say the least. And I say that as a Republican who proudly vote for Bush twice, McCain, and Romney. It was insulting to hear over and over that we are "going to make America great again" implying that America isn't great.

It was insulting to hear over and over that we are "going to make America great again" implying that America isn't great. Insulting

2017-2018 Milestones & Deliverables Development of a benchmark testing datasets for multiple social media streams Development of survey research methods that provide the most reliable approach for extracting self-classified emotion and blame text Conduct comparative tests of the survey-based cross-corpus training to alternative approaches Conference papers and research articles detailing the methodology and results of our experiments

Summary of Benefits Currently, there are no developed methods for coding blame attribution or its spread through social media Our project offers a new method for businesses, policy-makers, and government officials to monitor the spread and centering of blame during crises events. Pairing blame coding with discrete emotion coding provides a mechanism for testing how emotion and blame combine to create cascades of public opinion and societal action (i.e. boycotts, protests, organized social movements)