A survey on predicting the popularity of web content

Tatar, Alexandru-Florin; Amorim, Marcelo Dias de; Fdida, Serge; Antoniadis, Panayotis

doi:10.1186/s13174-014-0008-y

Cited by 202 publications

(126 citation statements)

References 95 publications

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“…A variety of features are used in these studies to predict the popularity of tweets/news articles [23]. We can categorize these features into: content-based and temporal features.…”

Section: Related Workmentioning

confidence: 99%

Predicting the Popularity of News Articles

Keneshloo

Wang²,

Han³

et al. 2016

Proceedings of the 2016 SIAM International Conference on Data Mining

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Consuming news articles is an integral part of our daily lives and news agencies such as The Washington Post (WP) expend tremendous effort in providing high quality reading experiences for their readers. Journalists and editors are faced with the task of determining which articles will become popular so that they can efficiently allocate resources to support a better reading experience. The reasons behind the popularity of news articles are typically varied, and might involve contemporariness, writing quality, and other latent factors. In this paper, we cast the problem of popularity prediction problem as regression, engineer several classes of features (metadata, contextual or content-based, temporal, and social), and build models for forecasting popularity. The system presented here is deployed in a real setting at The Washington Post; we demonstrate that it is able to accurately predict article popularity with an R 2 ≈ 0.8 using features harvested within 30 minutes of publication time.

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“…A variety of features are used in these studies to predict the popularity of tweets/news articles [23]. We can categorize these features into: content-based and temporal features.…”

Section: Related Workmentioning

confidence: 99%

Predicting the Popularity of News Articles

Keneshloo

Wang²,

Han³

et al. 2016

Proceedings of the 2016 SIAM International Conference on Data Mining

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“…It is a rule method with a Weka extension. RIPPER builds a rule set by first adding new rules until all specific category instances are covered and then adding conditions to the rules until all other category instances are excluded [12]. It improves training data by reducing size.…”

Section: W-jripmentioning

confidence: 99%

Predicting the Popularity of Trending Arabic News on Twitter

Rsheed

Khan

2014

Proceedings of the 6th International Conference on Management of Emergent Digital EcoSystems

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Twitter is a popular form of social media that helps to spread breaking news. Mining textual documents and the number of posts can assist in predicting the popularity of news articles based on the content of news articles. The purpose of this study is to build a model that can predict the popularity of news articles on Twitter by classifying their features, providing comparisons using three algorithms for data mining: decision tree, NB, and rule based W-JRIP. Four approaches were used to compare the extracted features: light stemming, N-grams and light stemming, N-grams, and bag of words. The results of the experiment on the external features of articles indicate that the decision tree performed better (93.30%) than the rule-based and NB algorithms. The experiment on the internal features yielded no significant predictors of the popularity of news articles on Twitter. The results of the experiments on internal and external features of articles indicate that the decision tree algorithm with light stemming approaches performs best, achieving 85.60%. This study expands the limited literature on Arabic text classification and provides a predictive model that may help news organizations improve their online contents.

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“…Predicting the popularity of user generated content on the web has been studied extensively [36]. Many different settings have been considered; common content types include online videos [23], online news [4], social bookmarking sites [22], social networking services [39], and crowdfunding campaigns [9], among others.…”

Section: Popularity Prediction On the Webmentioning

confidence: 99%

Predicting the Success of Online Petitions Leveraging Multidimensional Time-Series

Proskurnia

Grabowicz

Kobayashi

et al. 2017

Proceedings of the 26th International Conference on World Wide Web

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Applying classical time-series analysis techniques to online content is challenging, as web data tends to have data quality issues and is often incomplete, noisy, or poorly aligned. In this paper, we tackle the problem of predicting the evolution of a time series of user activity on the web in a manner that is both accurate and interpretable, using related time series to produce a more accurate prediction. We test our methods in the context of predicting signatures for online petitions using data from thousands of petitions posted on The Petition Site-one of the largest platforms of its kind. We observe that the success of these petitions is driven by a number of factors, including promotion through social media channels and on the front page of the petitions platform. We propose an interpretable model that incorporates seasonality, aging effects, self-excitation, and external effects. The interpretability of the model is important for understanding the elements that drives the activity of an online content. We show through an extensive empirical evaluation that our model is significantly better at predicting the outcome of a petition than state-of-the-art techniques.

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A survey on predicting the popularity of web content

Cited by 202 publications

References 95 publications

Predicting the Popularity of News Articles

Predicting the Popularity of News Articles

Predicting the Popularity of Trending Arabic News on Twitter

Predicting the Success of Online Petitions Leveraging Multidimensional Time-Series

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