Evidence-based workplace harassment guidance system: transforming #MeToo narratives into actionable knowledge through machine learning

Abstract

Workplace harassment remains a pervasive global issue, yet victims often lack ac- cess to evidence about what actually happens when people in similar situations

take action. Traditional resources offer generic procedural advice without outcome

data, leaving individuals to make consequential decisions with incomplete informa- tion. This study presents an evidence-based workplace harassment guidance system

that transforms 15,835 #MeToo narratives into personalized, actionable guidance grounded in documented outcomes from comparable cases. The system addresses a fundamental information asymmetry: while organizations accumulate knowledge about harassment cases, individual victims rarely know what outcomes others in similar situations experienced. By analyzing patterns across

thousands of documented experiences, the system identifies a user’s specific vulner- ability profile, retrieves semantically similar historical cases, and presents evidence- based guidance including proven successful action sequences, outcome statistics, and

high-impact actions that correlate with positive results. The guidance generation

pipeline employs SimCSE-BERT embeddings for semantic similarity, multi-label vul- nerability detection leveraging these embeddings to identify seven co-occurring risk

factors, and outcome pattern analysis across fifteen outcome categories—supported

by BERT sentiment analysis (98.1% accuracy) and HDBSCAN clustering for eval- uation.

The empirical analysis reveals sobering realities: negative outcomes predominate across all vulnerability types, institutional inaction occurs in 16.9% of cases, and

harasser accountability remains rare at 3.6%. Rather than offering false reassur- ance, the system presents these evidence-based statistics to enable informed decision- making. Professional evaluation with eleven practitioners from HR, legal, mental

health, and other sectors—91% with direct harassment case experience—validates

that the guidance meets practical standards for appropriateness (M=4.09/5), use- fulness (M=4.09/5), actionability (M=3.91/5), and safety (M=3.73/5), with 91%

endorsement and 73% rating the approach superior to typical harassment resources. This research demonstrates that machine learning can provide meaningful support

for sensitive domains when developed with rigorous professional validation and com- mitment to user safety.