In this work, we ask two questions: 1. Can we predict the type of community interested in a news article using only features from the article content? and 2. How well do these models generalize over time? To answer these questions, we compute well-studied content-based features on over 60K news articles from 4 communities on reddit.com. We train and test models over three different time periods between 2015 and 2017 to demonstrate which features degrade in performance the most due to concept drift. Our models can classify news articles into communities with high accuracy, ranging from 0.81 ROC AUC to 1.0 ROC AUC. However, while we can predict the community-specific popularity of news articles with high accuracy, practitioners should approach these models carefully. Predictions are both community-pair dependent and feature group dependent. Moreover, these feature groups generalize over time differently, with some only degrading slightly over time, but others degrading greatly. Therefore, we recommend that community-interest predictions are done in a hierarchical structure, where multiple binary classifiers can be used to separate community pairs, rather than a traditional multi-class model. Second, these models should be retrained over time based on accuracy goals and the availability of training data.
There are many prior works on general news popularity. The majority of these works use and develop content-based features to capture popularity, such as headline features @cite_11 @cite_22 or body content @cite_10 . However, others have based predictions on other signals such as users comments @cite_3 and early popularity @cite_0 @cite_5 @cite_13 . Even more general, there is a large body of work on content popularity prediction, not focused on news articles. These works include predicting the popularity of comments on Reddit @cite_1 @cite_25 , predicting the popularity of tweets or hashtags on Twitter @cite_30 @cite_26 , and predicting the popularity of videos @cite_20 @cite_13 .
@cite_13 @cite_30 @cite_26 @cite_22 @cite_1 @cite_3 @cite_0 @cite_5 @cite_10 @cite_25 @cite_20 @cite_11
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News articles are extremely time sensitive by nature. There is also intense competition among news items to propagate as widely as possible. Hence, the task of predicting the popularity of news items on the social web is both interesting and challenging. Prior research has dealt with predicting eventual online popularity based on early popularity. It is most desirable, however, to predict the popularity of items prior to their release, fostering the possibility of appropriate decision making to modify an article and the manner of its publication. In this paper, we construct a multi-dimensional feature space derived from properties of an article and evaluate the efficacy of these features to serve as predictors of online popularity. We examine both regression and classification algorithms and demonstrate that despite randomness in human behavior, it is possible to predict ranges of popularity on twitter with an overall 84 accuracy. Our study also serves to illustrate the differences between traditionally prominent sources and those immensely popular on the social web. News articles are extremely time sensitive by nature. There is also intense competition among news items to propagate as widely as possible. Hence, the task of predicting the popularity of news items on the social web is both interesting and challenging. Prior research has dealt with predicting eventual online popularity based on early popularity. It is most desirable, however, to predict the popularity of items prior to their release, fostering the possibility of appropriate decision making to modify an article and the manner of its publication. In this paper, we construct a multi-dimensional feature space derived from properties of an article and evaluate the efficacy of these features to serve as predictors of online popularity. We examine both regression and classification algorithms and demonstrate that despite randomness in human behavior, it is possible to predict ranges of popularity on twitter with an overall 84 accuracy. Our study also serves to illustrate the differences between traditionally prominent sources and those immensely popular on the social web. News articles are a captivating type of online content that capture a significant amount of Internet users' interest. They are particularly consumed by mobile users and extremely diffused through online social platforms. As a result, there is an increased interest in promptly identifying the articles that will receive a significant amount of user attention. This task falls under the broad scope of content popularity prediction and has direct implications in various contexts such as caching strategies or online advertisement policies. In this paper we address the problem of predicting the popularity of news articles based on user comments. We formulate the prediction task into a ranking problem where the goal is not to infer the precise attention that a content will receive but to accurately rank articles based on their predicted popularity. To this end, we analyze the ranking performance of three prediction models using a dataset of articles covering a four-year period and published by 20minutes.fr, an important French online news platform. Our results indicate that prediction methods improve the ranking performance and we observed that for our dataset a simple linear prediction method outperforms more dedicated prediction methods. The popularity of content in social media is unequally distributed, with some items receiving a disproportionate share of attention from users. Predicting which newly-submitted items will become popular is critically important for both the hosts of social media content and its consumers. Accurate and timely prediction would enable hosts to maximize revenue through differential pricing for access to content or ad placement. Prediction would also give consumers an important tool for filtering the content. Predicting the popularity of content in social media is challenging due to the complex interactions between content quality and how the social media site highlights its content. Moreover, most social media sites selectively present content that has been highly rated by similar users, whose similarity is indicated implicitly by their behavior or explicitly by links in a social network. While these factors make it difficult to predict popularity a priori, stochastic models of user behavior on these sites can allow predicting popularity based on early user reactions to new content. By incorporating the various mechanisms through which web sites display content, such models improve on predictions that are based on simply extrapolating from the early votes. Specifically, for one such site, the news aggregator Digg, we show how a stochastic model distinguishes the effect of the increased visibility due to the network from how interested users are in the content. We find a wide range of interest, distinguishing stories primarily of interest to users in the network (“niche interests”) from those of more general interest to the user community. This distinction is useful for predicting a story’s eventual popularity from users’ early reactions to the story.