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Online communities displaying textual postings require measures to combat information overload. One popular approach is to ask participants whether or not messages are helpful in order to then guide others to interesting content. Adopting a well-established framework for assessing data quality, we examine the nature of "helpfulness."We study consumer reviews at Amazon.com, deriving 22 measures quantifying their textual properties, authors' reputations and product characteristics. Confirmatory factor analysis reveals five underlying quality dimensions representing reviewers' reputations in the community, the topical relevancy of the reviews, the ease of understanding them, their believability and objectivity. A correlation and regression analysis confirms that these dimensions are related to the helpfulness scores assigned by community participants. However, it also uncovers a strong relationship between the chronological ordering of reviews and helpfulness, which both community participants and designers should keep in mind when using this method of social navigation.
Jahna Otterbacher (Sat,) studied this question.
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