Event threading within news topics
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Tracking news stories across different sources
Proceedings of the 13th annual ACM international conference on Multimedia
Proceedings of the 20th international conference on World wide web
Out of sight, not out of mind: on the effect of social and physical detachment on information need
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Finding trendsetters in information networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Finding news curators in twitter
Proceedings of the 22nd international conference on World Wide Web companion
Says who?: automatic text-based content analysis of television news
Proceedings of the 2013 international workshop on Mining unstructured big data using natural language processing
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We examine biases in online news sources and social media communities around them. To that end, we introduce unsupervised methods considering three types of biases: selection or ``gatekeeping'' bias, coverage bias, and statement bias, characterizing each one through a series of metrics. Our results, obtained by analyzing 80 international news sources during a two-week period, show that biases are subtle but observable, and follow geographical boundaries more closely than political ones. We also demonstrate how these biases are to some extent amplified by social media.