'Helpfulness' in online communities: a measure of message quality Export

@inproceedings{citeulike:4670526, abstract = {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.}, address = {New York, NY, USA}, author = {Otterbacher, Jahna}, booktitle = {CHI '09: Proceedings of the 27th international conference on Human factors in computing systems}, citeulike-article-id = {4670526}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1518701.1518848}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1518701.1518848}, doi = {10.1145/1518701.1518848}, isbn = {978-1-60558-246-7}, keywords = {redundant, review}, location = {Boston, MA, USA}, pages = {955--964}, posted-at = {2009-05-28 22:48:54}, priority = {2}, publisher = {ACM}, title = {'Helpfulness' in online communities: a measure of message quality}, url = {http://dx.doi.org/10.1145/1518701.1518848}, year = {2009} }

TY - CONF ID - citeulike:4670526 L3 - citeulike-article-id:4670526 N2 - 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. CY - Boston, MA, USA SN - 978-1-60558-246-7 AD - New York, NY, USA EP - 964 TI - 'Helpfulness' in online communities: a measure of message quality PB - ACM T2 - CHI '09: Proceedings of the 27th international conference on Human factors in computing systems SP - 955 KW - redundant KW - review AU - Otterbacher, Jahna PY - 2009/// UR - http://dx.doi.org/10.1145/1518701.1518848 ER -