Predicting the popularity of online articles based on user comments

Tatar, Alexandru; Leguay, Jérémie; Antoniadis, Panayotis; Limbourg, Arnaud; Amorim, Marcelo Dias de; Fdida, Serge

doi:10.1145/1988688.1988766

Cited by 89 publications

(70 citation statements)

References 12 publications

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“…There are two works most closely aligned to our proposal, but they also have shortcomings. In [33], due to lack of ground truth, they used comments as the popularity metric, which we show differs from actual view count in Section 5.2.1. In Jamali et al's work [17] on Digg, they transformed the prediction problem into one of classification tasks, using Digg-score as the popularity index, rather than the views.…”

Section: Mining User Commentsmentioning

confidence: 97%

Predicting the popularity of web 2.0 items based on user comments

Gao

Kan

et al. 2014

Proceedings of the 37th International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

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In the current Web 2.0 era, the popularity of Web resources fluctuates ephemerally, based on trends and social interest. As a result, content-based relevance signals are insufficient to meet users' constantly evolving information needs in searching for Web 2.0 items. Incorporating future popularity into ranking is one way to counter this. However, predicting popularity as a third party (as in the case of general search engines) is difficult in practice, due to their limited access to item view histories.To enable popularity prediction externally without excessive crawling, we propose an alternative solution by leveraging user comments, which are more accessible than view counts. Due to the sparsity of comments, traditional solutions that are solely based on view histories do not perform well. To deal with this sparsity, we mine comments to recover additional signal, such as social influence. By modeling comments as a time-aware bipartite graph, we propose a regularization-based ranking algorithm that accounts for temporal, social influence and current popularity factors to predict the future popularity of items. Experimental results on three realworld datasets -crawled from YouTube, Flickr and Last.fmshow that our method consistently outperforms competitive baselines in several evaluation tasks.

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Section: Mining User Commentsmentioning

confidence: 97%

Predicting the popularity of web 2.0 items based on user comments

Gao

Kan

et al. 2014

Proceedings of the 37th International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

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“…Tatar et al use a simple linear regression based on the early number of comments to predict the final number of comments for news articles [74]. The authors observe that there is no significant improvement when using specialized prediction models as a function of the category and the publication hour of an article.…”

Section: After Publicationmentioning

confidence: 99%

A survey on predicting the popularity of web content

Tatar

Amorim

Fdida

et al. 2014

J Internet Serv Appl

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202

125

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Social media platforms have democratized the process of web content creation allowing mere consumers to become creators and distributors of content. But this has also contributed to an explosive growth of information and has intensified the online competition for users attention, since only a small number of items become popular while the rest remain unknown. Understanding what makes one item more popular than another, observing its popularity dynamics, and being able to predict its popularity has thus attracted a lot of interest in the past few years. Predicting the popularity of web content is useful in many areas such as network dimensioning (e.g., caching and replication), online marketing (e.g., recommendation systems and media advertising), or real-world outcome prediction (e.g., economical trends). In this survey, we review the current findings on web content popularity prediction. We describe the different popularity prediction models, present the features that have shown good predictive capabilities, and reveal factors known to influence web content popularity.

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“…In addition, we demonstrate how standard engagement metrics and content-based sentiment features can be leveraged to quantify the level of interaction between the user and the news content. Previous studies have mostly examined the relationship between information propagation and user supplied comments [45,40,46] or web content properties [4,6,8,50,53,55].…”

Section: Introductionmentioning

confidence: 99%

“…So far, comments have been examined for the most part in terms of volume [40,46] or ratings (e.g., likes, votes), which would be one way of measuring engagement. For example, the number of comments posted in an article is considered as a measure of popularity or user participation.…”

Section: Introductionmentioning

confidence: 99%

User engagement in online News: Under the scope of sentiment, interest, affect, and gaze

Arapakis

Lalmas

Cambazoğlu

et al. 2014

Asso for Info Science & Tech

102

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Online content providers, like news portals and social media platforms, constantly seek new ways to attract large shares of online attention by keeping their users engaged. A common challenge is to identify which aspects of online interaction influence user engagement the most. In this article, through an analysis of a news article collection obtained from Yahoo! News, we demonstrate that news articles exhibit considerable variation in terms of the sentimentality and polarity of their content, depending on factors like news provider and genre. Moreover, through a laboratory study, we observe the effect of sentimentality and polarity of news and comments on a set of subjective and objective measures of engagement. In particular, we show that attention, affect, and gaze differ across news of varying interestingness. As part of our study, we also explore methods that exploit the sentiments expressed in user comments to reorder the lists of comments displayed in news pages. Our results indicate that user engagement can be anticipated if we account for the sentimentality and polarity of the content, as well as other factors that drive attention and inspire human curiosity.

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Predicting the popularity of online articles based on user comments

Cited by 89 publications

References 12 publications

Predicting the popularity of web 2.0 items based on user comments

Predicting the popularity of web 2.0 items based on user comments

A survey on predicting the popularity of web content

User engagement in online News: Under the scope of sentiment, interest, affect, and gaze

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