This paper explores how the portrayal of female characters in fanfiction evolved in response to the #MeToo movement and fourth-wave feminism, with the aim of assessing whether the impact of the awareness of the campaign was broad enough to visibly alter how the average author portrays women in narrative contexts. To analyze these trends, fanfiction data from Archive of Our Own (AO3) spanning 2015–2019 was parsed, and two Natural Language Processing (NLP) pipelines — Word2Vec and GloVe, and BERT — were developed. The study finds that bias scores, aggregated through formulas created to compare gendered associations, show a stronger stereotypization of women before 2017 compared to after. Furthermore, a similar trend is discovered in the representation of women in fanfiction. While the BERT pipeline proved most effective for capturing contextual nuances, it is significantly limited by its reliance on binary labels and computational intensity. This further indicates the need for more inclusive and sustainable methods, making the Word2Vec/GloVe models more appropriate for this task. The paper concludes with recommendations for future work, including broader representation, longer-term analysis, and enhanced detection of evolving language patterns.