Information encountering: a conceptual framework for accidental information discovery
ISIC '96 Proceedings of an international conference on Information seeking in context
Sorting things out: classification and its consequences
Sorting things out: classification and its consequences
SALSA: the stochastic approach for link-structure analysis
ACM Transactions on Information Systems (TOIS)
How and why people Twitter: the role that micro-blogging plays in informal communication at work
Proceedings of the ACM 2009 international conference on Supporting group work
Identifying interesting assertions from the web
Proceedings of the 18th ACM conference on Information and knowledge management
Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter
HICSS '10 Proceedings of the 2010 43rd Hawaii International Conference on System Sciences
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
An empirical study on learning to rank of tweets
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Hip and trendy: Characterizing emerging trends on Twitter
Journal of the American Society for Information Science and Technology
Twitinfo: aggregating and visualizing microblogs for event exploration
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Do all birds tweet the same?: characterizing twitter around the world
Proceedings of the 20th ACM international conference on Information and knowledge management
User oriented tweet ranking: a filtering approach to microblogs
Proceedings of the 20th ACM international conference on Information and knowledge management
Classifying trending topics: a typology of conversation triggers on Twitter
Proceedings of the 20th ACM international conference on Information and knowledge management
Unfolding the event landscape on twitter: classification and exploration of user categories
Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work
Tweeting is believing?: understanding microblog credibility perceptions
Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work
Who gives a tweet?: evaluating microblog content value
Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work
Social media evolution of the Egyptian revolution
Communications of the ACM
Identification of useful user comments in social media: a case study on flickr commons
Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries
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Twitter has evolved into a significant communication nexus, coupling personal and highly contextual utterances with local news, memes, celebrity gossip, headlines, and other microblogging subgenres. If we take Twitter as a large and varied dynamic collection, how can we predict which tweets will be interesting to a broad audience in advance of lagging social indicators of interest such as retweets? The telegraphic form of tweets, coupled with the subjective notion of interestingness, makes it difficult for human judges to agree on which tweets are indeed interesting. In this paper, we address two questions: Can we develop a reliable strategy that results in high-quality labels for a collection of tweets, and can we use this labeled collection to predict a tweet's interestingness? To answer the first question, we performed a series of studies using crowdsourcing to reach a diverse set of workers who served as a proxy for an audience with variable interests and perspectives. This method allowed us to explore different labeling strategies, including varying the judges, the labels they applied, the datasets, and other aspects of the task. To address the second question, we used crowdsourcing to assemble a set of tweets rated as interesting or not; we scored these tweets using textual and contextual features; and we used these scores as inputs to a binary classifier. We were able to achieve moderate agreement (κ = 0.52) between the best classifier and the human assessments, a figure which reflects the challenges of the judgment task.