Voices of Her: Analyzing Gender Differences in the AI Publication World

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. 1 * Equal contribution. Equal supervision.1 Our code and data have been uploaded to the submission system, and will be open-sourced upon acceptance.2 https://mentorship.aclweb.org/ 2 Data Collection and Cleaning AI SCHOLAR.We use all the scholar information from the most recent collection of researchers in the field of AI, i.e., the AI SCHOLAR dataset (Jin et al., 2022), 3 which contains all the scholars in the field of AI with at least 100 citations according to Google Scholar.The data consists of 78K scholars with tags related to AI such as artificial intelligence (AI) and machine learning (ML), or subdomains of AI such as computer vision (CV) and natural language processing (NLP).It only includes scholars with at least 100 citations, an approximate cut-off for the long-tail since it is not feasible to include all scholar profiles.We discuss the limitations of using this dataset in Section 7. Throughout the paper, we use the term "AI researchers" to denote the set of scholars in the AI SCHOLAR dataset.For each AI researcher, the AI SCHOLAR dataset collects information such as the name, affiliation, up to five domain tags, total citations, citations by year, and all their papers with title, year, and the number of citations.Since the total number of papers is massive (2.8M papers for the 78K AI researchers), we use the random subset of papers provided by Jin et al. (2022).They collect 100K papers with detailed information such as abstracts and full names of all the coauthors.Among the 100K papers with detailed information, we further filter out papers with