期刊
KNOWLEDGE AND INFORMATION SYSTEMS
卷 35, 期 3, 页码 645-664出版社
SPRINGER LONDON LTD
DOI: 10.1007/s10115-012-0548-z
关键词
Gender; Information filtering; Social voting; Text classification
Online review forums provide consumers with essential information about goods and services by facilitating word-of-mouth communication. Despite that preferences are correlated to demographic characteristics, reviewer gender is not often provided on user profiles. We consider the case of the internet movie database (IMDb), where users exchange views on movies. Like many forums, IMDb employs collaborative filtering such that by default, reviews are ranked by perceived utility. IMDb also provides a unique gender filter that displays an equal number of reviews authored by men and women. Using logistic classification, we compare reviews with respect to writing style, content and metadata features. We find salient differences in stylistic features and content between reviews written by men and women, as predicted by sociolinguistic theory. However, utility is the best predictor of gender, with women's reviews perceived as being much less useful than those written by men. While we cannot observe who votes at IMDb, we do find that highly rated female-authored reviews exhibit male characteristics. Our results have implications for which contributions are likely to be seen, and to what extent participants get a balanced view as to what others think about an item.
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