The tube over time: characterizing popularity growth of youtube videos
Proceedings of the fourth ACM international conference on Web search and data mining
Analysis and forecasting of trending topics in online media streams
Proceedings of the 21st ACM international conference on Multimedia
| Hi-index | 0.00 |
Wikipedia is used every day by people all around the world, to satisfy a variety of information needs. We cross-correlate multiple Wikipedia traffic data sets to infer various behavioral features of its users: their usage patterns (e.g., as a reference or a source); their motivations (e.g., routine tasks such as student homework vs. information needs dictated by news events); their search strategies (how and to what extent accessing an article leads to further related readings inside or outside Wikipedia); and what determines their choice of Wikipedia as an information resource. We primarily study article hit counts to determine how the popularity of articles (and article categories) changes over time, and in response to news events in the English-speaking world. We further leverage logs of actual navigational patterns from a very large sample of Indiana University users over a period of one year, allowing us unprecedented ability to study how users traverse an online encyclopedia. This data allows us to make quantitative claims about how users choose links when navigating Wikipedia. From this same source of data we are further able to extract analogous navigation networks representing other large sites, including Facebook, to compare and contrast the use of these sites with Wikipedia. Finally we present a possible application of traffic analysis to page categorization.