Hugo Liu & Rada Mihalcea (2007). Of men, women, and computers: data-driven gender modeling for improved user interfaces. Proceedings of the International Conference on Weblogs and Social Media (ICWSM), Boulder, CO.
Liu, H., & Mihalcea, R. (2007). Of men, women, and computers: data-driven gender modeling for improved user interfaces. In Proceedings of the International Conference on Weblogs and Social Media (ICWSM), Boulder, CO.
@inproceedings{liu2007of,
author = {Hugo Liu and Rada Mihalcea},
title = {Of men, women, and computers: data-driven gender modeling for improved user interfaces},
booktitle = {Proceedings of the International Conference on Weblogs and Social Media (ICWSM), Boulder, CO},
url = {https://starheartsong.com/papers/pdf/ICWSM2007-GenderLens.pdf},
year = {2007}
}From roughly 300,000 Blogspot entries gathered on two days in 2006, this paper formed a balanced corpus of 75,000 male-labeled and 75,000 female-labeled posts. A unigram classifier reached 71 percent author-gender accuracy; further analyses explored time, food, color, size, pronouns, social reference, and affect. GenderLens then reranked Google News with the 14,000 most discriminating words. Thirty readers compared hidden male- and female-ranked columns; reported preferences aligned in four of five categories, while entertainment did not. The limits matter as much as the result: binary self-report, one platform, a two-day window, thirty participants, correlational and stereotype-adjacent interpretation, and one category accepted at a weaker significance threshold. These are patterns in particular corpora and participants, not fixed properties of women and men.