Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
Think different: increasing online community participation using uniqueness and group dissimilarity
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Centroid-based summarization of multiple documents
Information Processing and Management: an International Journal
How oversight improves member-maintained communities
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Opinion observer: analyzing and comparing opinions on the Web
WWW '05 Proceedings of the 14th international conference on World Wide Web
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
SuggestBot: using intelligent task routing to help people find work in wikipedia
Proceedings of the 12th international conference on Intelligent user interfaces
Talk amongst yourselves: inviting users to participate in online conversations
Proceedings of the 12th international conference on Intelligent user interfaces
Understanding user behavior in online feedback reporting
Proceedings of the 8th ACM conference on Electronic commerce
Show me the money!: deriving the pricing power of product features by mining consumer reviews
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
'Helpfulness' in online communities: a measure of message quality
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
What's mine is mine: territoriality in collaborative authoring
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Get out the vote: determining support or opposition from congressional floor-debate transcripts
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Detecting product review spammers using rating behaviors
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Towards quality discourse in online news comments
Proceedings of the ACM 2011 conference on Computer supported cooperative work
Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work
Learning the lingo?: gender, prestige and linguistic adaptation in review communities
Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work
An analysis of structured data on the web
Proceedings of the VLDB Endowment
Analyzing Online Review Helpfulness Using a Regressional ReliefF-Enhanced Text Mining Method
ACM Transactions on Management Information Systems (TMIS)
On the institutional archiving of social media
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
Widespread underprovision on Reddit
Proceedings of the 2013 conference on Computer supported cooperative work
Analyzing the quality of information solicited from targeted strangers on social media
Proceedings of the 2013 conference on Computer supported cooperative work
Simultaneously detecting fake reviews and review spammers using factor graph model
Proceedings of the 5th Annual ACM Web Science Conference
Are user-contributed reviews community property?: exploring the beliefs and practices of reviewers
Proceedings of the 5th Annual ACM Web Science Conference
Sharing Knowledge and Expertise: The CSCW View of Knowledge Management
Computer Supported Cooperative Work
Demographics, weather and online reviews: a study of restaurant recommendations
Proceedings of the 23rd international conference on World wide web
Hi-index | 0.00 |
People who review products on the web invest considerable time and energy in what they write. So why would someone write a review that restates earlier reviews? Our work looks to answer this question. In this paper, we present a mixed-method study of deja reviewers, latecomers who echo what other people said. We analyze nearly 100,000 Amazon.com reviews for signs of repetition and find that roughly 10-15% of reviews substantially resemble previous ones. Using these algorithmically-identified reviews as centerpieces for discussion, we interviewed reviewers to understand their motives. An overwhelming number of reviews partially explains deja reviews, but deeper factors revolving around an individual's status in the community are also at work. The paper concludes by introducing a new idea inspired by our findings: a self-aware community that nudges members toward community-wide goals.