Sorry (Not Sorry): Decoding #MeToo Defenses

Abstract

This Article examines the text of over two hundred public statements issued by people accused of work-related sexual harassment and misconduct as a part of the #MeToo movement. Using both computational and manual text analytics approaches, the project constructs a typology of the statements' substantive content, including admissions, denials, defenses, and apologies; their emotional content, including anger, anxiety, and sadness; and their cognitive content, including authenticity and certainty. The project also tracks specific themes throughout the statements, including attacks on the accusers, references to changing workplace norms, addiction and mental health stories, and concerns about due process. Building on this descriptive picture, the Article uses the statements to assess the #MeToo movement's progress in holding individual perpetrators to account, and in achieving structural change.

Keywords: metoo, defenses, sexual misconduct, sexual harassment, text analytics, text analysis, sentiment analysis