Difference between revisions of "Roja Bandari et. al. ICWSM 2012"
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| − | + | R Bandari, S Asur, BA Huberman | |
| − | + | The Pulse of News in Social Media: Forecasting Popularity, ICWSM 2012 | |
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| + | |||
| + | == Summary == | ||
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| + | In this paper, the author address the following problem: predict the popularity of news prior to their release. They extract features from article based on its content, using two methods to predict their popularity: regression and classification, and evaluate with the actual popularity from social media, like Twitter. | ||
| + | |||
| + | == Datasets == | ||
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| + | They collected all news article, from August 8th to 16th using API of a news aggregator called FeedZilla. Each article include a title, short summary, url, and a timestamp, and a category. The total number of data after cleaning is over 42,000. | ||
| + | |||
| + | They then using a service called Topsy, to collect the times being posted and retweeted on Twiiter for each new article. | ||
| + | |||
| + | == Features == | ||
Revision as of 20:55, 26 September 2012
Contents
Citation
R Bandari, S Asur, BA Huberman The Pulse of News in Social Media: Forecasting Popularity, ICWSM 2012
Summary
In this paper, the author address the following problem: predict the popularity of news prior to their release. They extract features from article based on its content, using two methods to predict their popularity: regression and classification, and evaluate with the actual popularity from social media, like Twitter.
Datasets
They collected all news article, from August 8th to 16th using API of a news aggregator called FeedZilla. Each article include a title, short summary, url, and a timestamp, and a category. The total number of data after cleaning is over 42,000.
They then using a service called Topsy, to collect the times being posted and retweeted on Twiiter for each new article.
