Pain in a Safe Space: Mapping Emotions and Discourse in theWomenintech Subreddit

Women in Information Technology (WIT) continue to face systemic challenges, including career stagnation, lack of recognition, workplace bias, and professional isolation. In response, external support networks such as the r/womenintech subreddit have emerged, offering a dedicated and inclusive space for women in Software Engineering (SE) to exchange experiences, seek advice, and build community. This study investigates the discursive landscape of this subreddit by analyzing 2,367 posts published between April 2024 and April 2025. We applied a set of natural language processing (NLP) techniques, using the Twitter-based RoBERTa model, to conduct a multi-dimensional analysis that includes emotion detection, sentiment analysis, hate speech classification, irony detection, and offensive content identification. Our findings show that 99.9% of the posts are free of hate speech, reinforcing the subreddit’s role as a safe space for women in tech to share experiences. However, the prevalence of the emotion Sadness (45%) reveal that the experiences reported are often distressing and unpleasant. In addition, we explore temporal trends in politically charged contexts of DEIA law. Our study highlights the importance of better understanding the underlying structural and cultural factors that contribute to these emotions and open new directions for further analysis.