Predicting the Popularity of Online News from Content Metadata
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Date
2016-10-28
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
Popularity prediction of online news aims to predict
the future popularity of news article prior to its publication
estimating the number of shares, likes, and comments. Yet,
popularity prediction is a challenging task due to various issues including difficulty to measure the quality of content and
relevance of content to users; prediction difficulty of complex
online interactions and information cascades; inaccessibility of
context outside the web; local and geographic conditions; social
network properties. This paper focuses on popularity prediction
of online news by predicting whether users share an article or not,
and how many users share the news adopting before publication
approach. This paper proposes the gradient boosting machine
for popularity prediction using features that are known before
publication of articles. The proposed model shows around 1.8%
improvement over previously applied techniques on a benchmark
dataset. This model also indicates that features extracted from
articles keywords, publication day, and the data channel are
highly influential for popularity prediction.
Description
Keywords
Social Media Contents, Popularity Prediction, Before Publication Approach, Machine Learning, Text Mining
Citation
IIUC-ICISET2016-ID-110