Project Grant 2210137

EAGER: DCL: SATC: ENABLING INTERDISCIPLINARY COLLABORATION: COMBATTING DISINFORMATION AND RACIAL BIAS: A DEEP-LEARNING-ASSISTED INVESTIGATION OF TEMPORAL DYNAMICS OF DISINFORMATION -THIS PROJECT EXPLORES THE DIFFUSION OF RACIAL DISINFORMATION ONLINE AND ITS SOCIAL IMPACTS, PARTICULARLY FOCUSING ON ASIAN AMERICANS. WHILE THE HATRED AND BIAS AGAINST ASIAN AMERICANS HAVE BECOME NOTABLE AMID THE COVID-19 PANDEMIC, ASIAN-TARGETING DISINFORMATION HAS YET BEEN FULLY EXPLORED. THE PROJECT'S NOVELTIES ARE IN UNIQUE MULTIDISCIPLINARY APPROACHES TO (1) DETECT ASIAN-TARGETING DISINFORMATION AND ITS COUNTERMEASURE MESSAGES, AND UNDERSTAND HOW THEY ARE SPREAD ON THE WEB, (2) EXAMINE HOW THE SPREAD OF DISINFORMATION AND COUNTERMEASURE MESSAGES ON THE WEB IS ASSOCIATED WITH THE INTENSITY OF THE BIAS AND HATE CRIMES AGAINST ASIAN AMERICANS, AND (3) DEVELOP VARIOUS DATA-DRIVEN COMPUTATIONAL MODELS TO HELP UNDERSTANDING THE DISINFORMATION DYNAMICS. THE PROJECT'S BROADER SIGNIFICANCE AND IMPORTANCE ARE TO INFORM CIVIL SOCIETY, INCLUDING ADVOCACY ORGANIZATIONS AND THE GENERAL PUBLIC, ABOUT HOW TO STRATEGIZE COMMUNICATION EFFORTS IN BATTLING RACIAL DISINFORMATION, AND TO MAKE THE DEVELOPED TOOLS AND OUTCOMES PUBLICLY AVAILABLE FOR BROADER USES. THE PROJECT TAKES THREE-STAGED APPROACHES. THE MAIN OBJECTIVE OF THE FIRST PHASE IS TO DEVELOP COMPUTATIONAL TOOLS FOR THE DETECTION AND ANALYSIS OF THE TEMPORAL DYNAMICS BETWEEN ASIAN-TARGETED DISINFORMATION AND COUNTERMEASURES ON THE WEB. A SPECIFIC FOCUS IS ON DEVELOPING AUTOMATED IDENTIFICATION TOOLS AND DEEP-LEARNING CLASSIFICATION MODELS BY FEATURE-ENGINEERING UNIQUE CHARACTERISTICS OF DISINFORMATION. THE OBJECTIVE OF THE SECOND PHASE IS TO UNDERSTAND TO WHAT EXTENT THE SPREAD OF DISINFORMATION AND COUNTERMEASURES ONLINE IS ASSOCIATED WITH THE SOCIETAL TREND OF IMPLICIT BIAS AND HATE CRIME OCCURRENCES AGAINST ASIAN AMERICANS IN THE REAL-WORLD, WHICH CAN BE ACHIEVED VIA DEVELOPING DEEP-LEARNING CAUSALITY MODELS. THE OBJECTIVE OF THE THIRD PHASE IS TO DESIGN SCALABLE DATA-DRIVEN DEEP-LEARNING MODELS OF DISINFORMATION DYNAMICS IN MACRO AND MICRO LEVELS, IDENTIFYING UNKNOWN DYNAMICS FROM THE REAL-WORLD MEASUREMENTS, WHICH ALSO ENABLES SIMULATIONS OF THE LEARNED DYNAMICS. THIS AWARD REFLECTS NSF'S STATUTORY MISSION AND HAS BEEN DEEMED WORTHY OF SUPPORT THROUGH EVALUATION USING THE FOUNDATION'S INTELLECTUAL MERIT AND BROADER IMPACTS REVIEW CRITERIA.