Modelling and predicting news popularity

Hensinger, Elena; Flaounas, Ilias; Cristianini, Nello

doi:10.1007/s10044-012-0314-6

Cited by 26 publications

(16 citation statements)

References 31 publications

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“…For example existing work in pattern analysis that uses natural language processing and statistics based machine learning to identify news popularity [45] could be adopted to evaluate app descriptions. Research has also been conducted to investigate the use of Latent Dirichlet Allocation (LDA) to evaluate app description against app behavior [46].…”

Section: Addressing Packaging Requirementsmentioning

confidence: 99%

Investigating Country Differences in Mobile App User Behavior and Challenges for Software Engineering

Lim

Bentley

Kanakam

et al. 2015

IIEEE Trans. Software Eng.

156

130

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Mobile applications (apps) are software developed for use on mobile devices and made available through app stores. App stores are highly competitive markets where developers need to cater to a large number of users spanning multiple countries. This work hypothesizes that there exist country differences in mobile app user behavior and conducts one of the largest surveys to date of app users across the world, in order to identify the precise nature of those differences. The survey investigated user adoption of the app store concept, app needs, and rationale for selecting or abandoning an app. We collected data from more than 15 countries,

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Section: Addressing Packaging Requirementsmentioning

confidence: 99%

Investigating Country Differences in Mobile App User Behavior and Challenges for Software Engineering

Lim

Bentley

Kanakam

et al. 2015

IIEEE Trans. Software Eng.

156

130

View full text Add to dashboard Cite

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“…We can also see that New York Times and NPR readers share very similar interests, favouring content related to science, technology, health and education research. We can see a general trend, also reported in [9], that readers avoid news content about public affairs, as represented here by terms such as Obama, Afghanistan, Iran and Pakistan appearing prominently in the negative features.…”

Section: Resultsmentioning

confidence: 77%

“…To solve this problem, our prior results indicate that one needs to solve a preference learning task that consists of classifying ordered pairs of articles [9]. We call this classifier a ranker.…”

Section: Learning To Rank Preferencesmentioning

confidence: 99%

“…separating popular from non-popular articles) [9] . This is due to the popularity of an article being a relative quantity that is affected by the other articles that are published on the same day.…”

Section: Learning To Rank Preferencesmentioning

confidence: 99%

“…From previous work, we know that the probability of a news article becoming popular does depend on its content, and that it is possible to identify which words increase the probability of a news article being clicked [9]. However, while the content of an article influences its appeal, the actual set of popular articles is defined by the ones with the most appeal: in other words, an article needs to be more appealing than its competitors in order to be chosen.…”

Section: Introductionmentioning

confidence: 98%

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Scalable Preference Learning from Data Streams

Dzogang

Lansdall-Welfare

Sudhahar

et al. 2015

Proceedings of the 24th International Conference on World Wide Web

Self Cite

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We study the task of learning the preferences of online readers of news, based on their past choices. Previous work has shown that it is possible to model this situation as a competition between articles, where the most appealing articles of the day are those selected by the most users. The appeal of an article can be computed from its textual content, and the evaluation function can be learned from training data. In this paper, we show how this task can benefit from an efficient algorithm, based on hashing representations, which enables it to be deployed on high intensity data streams. We demonstrate the effectiveness of this approach on four real world news streams, compare it with standard approaches, and describe a new online demonstration based on this technology.

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