@inproceedings{ding-etal-2025-voices,
    title = "Voices of Her: Analyzing Gender Differences in the {AI} Publication World",
    author = {Ding, Yiwen  and
      Liu, Jiarui  and
      Lyu, Zhiheng  and
      Zhang, Kun  and
      Sch{\"o}lkopf, Bernhard  and
      Jin, Zhijing  and
      Mihalcea, Rada},
    editor = "Atwell, Katherine  and
      Biester, Laura  and
      Borah, Angana  and
      Dementieva, Daryna  and
      Ignat, Oana  and
      Kotonya, Neema  and
      Liu, Ziyi  and
      Wan, Ruyuan  and
      Wilson, Steven  and
      Zhao, Jieyu",
    booktitle = "Proceedings of the Fourth Workshop on NLP for Positive Impact (NLP4PI)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.nlp4pi-1.17/",
    pages = "196--214",
    ISBN = "978-1-959429-19-7",
    abstract = "While several previous studies have analyzed gender bias in research, we are still missing a comprehensive analysis of gender differences in the AI community, covering diverse topics and different development trends. Using the AI Scholar dataset of 78K researchers in the field of AI, we identify several gender differences: (1) Although female researchers tend to have fewer overall citations than males, this citation difference does not hold for all academic-age groups; (2) There exist large gender homophily in co-authorship on AI papers; (3) Female first-authored papers show distinct linguistic styles, such as longer text, more positive emotion words, and more catchy titles than male first-authored papers. Our analysis provides a window into the current demographic trends in our AI community, and encourages more gender equality and diversity in the future."
}