Triggers and barriers to customizing software
CHI '91 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
The sciences of the artificial (3rd ed.)
The sciences of the artificial (3rd ed.)
User customization of a word processor
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
User population and user contributions to virtual publics: a systems model
GROUP '99 Proceedings of the international ACM SIGGROUP conference on Supporting group work
Information Systems Research
HICSS '02 Proceedings of the 35th Annual Hawaii International Conference on System Sciences (HICSS'02)-Volume 4 - Volume 4
I-DIAG: from community discussion to knowledge distillation
Communities and technologies
A comparison of static, adaptive, and adaptable menus
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Slash(dot) and burn: distributed moderation in a large online conversation space
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Studying cooperation and conflict between authors with history flow visualizations
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Mining interesting knowledge from weblogs: a survey
Data & Knowledge Engineering
Follow the (slash) dot: effects of feedback on new members in an online community
GROUP '05 Proceedings of the 2005 international ACM SIGGROUP conference on Supporting group work
Crafting the initial user experience to achieve community goals
Proceedings of the 2008 ACM conference on Recommender systems
Three recommender approaches to interface controls reduction
Proceedings of the 2008 ACM conference on Recommender systems
'Helpfulness' in online communities: a measure of message quality
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
backchan.nl: integrating backchannels in physical space
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Signed networks in social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
OOLAM: an opinion oriented link analysis model for influence persona discovery
Proceedings of the fourth ACM international conference on Web search and data mining
Towards quality discourse in online news comments
Proceedings of the ACM 2011 conference on Computer supported cooperative work
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A spectral algorithm for computing social balance
WAW'11 Proceedings of the 8th international conference on Algorithms and models for the web graph
Semi-supervised correction of biased comment ratings
Proceedings of the 21st international conference on World Wide Web
Reciprocal and heterogeneous link prediction in social networks
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
"Welcome!": social and psychological predictors of volunteer socializers in online communities
Proceedings of the 2013 conference on Computer supported cooperative work
Predicting community preference of comments on the Social Web
Web Intelligence and Agent Systems
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Large-scale online communities need to manage the tension between critical mass and information overload. Slashdot is a news and discussion site that has used comment rating to allow massive participation while providing a mechanism for users to filter content. By default, comments with low ratings are hidden. Of users who changed the defaults, more than three times as many chose to use ratings for filtering or sorting as chose to suppress the use of comment ratings. Nearly half of registered users, however, never strayed from the default filtering settings, suggesting that the costs of exploring and selecting custom filter settings exceeds the expected benefit for many users. We recommend leveraging the efforts of the users that actively choose filter settings to reduce the cost of changing settings for all other users. One strategy is to create static schemas that capture the filtering preferences of different groups of readers. Another strategy is to dynamically set filtering thresholds for each conversation thread, based in part on the choices of previous readers. For predicting later readers' choices, the choices of previous readers are far more useful than content features such as the number of comments or the ratings of those comments.