This article investigates the concept of "helpfulness" in online communities, particularly through the lens of consumer reviews on Amazon.com. It leverages a recognized framework for assessing data quality to explore what makes certain messages or reviews deemed helpful by community participants. By analyzing 22 textual, author reputation, and product characteristic metrics, the study identifies five key quality dimensions: reviewer reputation, review relevancy, understandability, believability, and objectivity. These dimensions are found to significantly influence the helpfulness ratings given by community members. Intriguingly, the research also highlights a notable correlation between the helpfulness scores and the chronological order of reviews, suggesting that newer reviews are often perceived as more helpful. This finding prompts both community members and platform designers to reconsider how this method of social navigation is utilized and interpreted.
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Otterbacher, Jahna. "'Helpfulness' in online communities: a measure of message quality." Proceedings of the SIGCHI conference on human factors in computing systems. 2009.
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