View source for Roja Bandari et. al. ICWSM 2012

== 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.

== Features ==

 - The category of the article, 
like technology and entertainment. They define a measure called ''t-density'', which means the average tweets of link in a particular category, to define the popularity of such category.
 - Subjectivity of the article. They use an existing NLP 
toolkit to assign a binary score to an article, whether the article is subjective or objective.
 - Name Entity. They use ''t-density'' to measure the popularity of all the name entities. 
Then for a given article, they calculate score, according to name entity extracted from the article.
 - The Source of a news.  The '''t-density'' is also used in measuring the popularity of a news source, like weblogs.

== Prediction ==

The author use to method to predict the popularity of a given article:

 - Regression. They perform 3 kinds of regression: linear regression, SVM regression, and kNN regression, to predict the exact number of tweets, given a news article. The results turns out to be not so satisfied, the R^{2} value is typically below 0.5. 
 - Classification. They define the popularity of a news into 3 classes: A(1-20 tweets), B(20-100 tweets),C(more than 100 tweets). They use serval classifying algorithm to perform classification, the best result is about 84%.