基于多元分析的网络新闻流行度预测

2017 IEEE International Conference on Computer and Information Technology (CIT) Pub Date : 2017-08-01 DOI:10.1109/CIT.2017.36

Caiyun Liu, Wenjie Wang, Yuqing Zhang, Ying Dong, Fannv He, Chensi Wu

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引用次数: 16

摘要

越来越多的网络新闻引发了学术界对新闻流行度预测的广泛关注，新闻流行度受用户行为的影响，不容易预测。然而，现有的预测网络新闻发布后受欢迎程度的方法不够及时，发布前预测缺乏歧视性。本文探讨了影响新闻受欢迎程度的变量，提出了一种预测网络新闻发布前受欢迎程度的新方法。通过对新闻的观察，我们首先发现标题的语法结构会影响新闻的受欢迎程度，实验表明，这一特征可以使预测模型的R^2统计量提高6.62%。此外，我们改进了传统的类别和作者特征，首先对视图进行对数转换，然后计算这些特征的分数，而不是直接将它们填充到学习模型中。利用这些特征和另外两个特征，我们最终从两个方面预测新闻的受欢迎程度:新闻是否会受欢迎以及新闻最终吸引了多少浏览量。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Predicting the Popularity of Online News Based on Multivariate Analysis

An increasing number of online news triggers wide academic concern for the prediction of news popularity, which is affected by users' behaviors and not easy to predict. However, existing methods that predict the popularity of online news after publication are not timely enough, and predicting before publication lacks discriminatory features. This paper explores the variables which may affect news popularity and presents a novel methodology to predict the popularity of online news before publication. Through the observation of news, we first find that grammatical construction of titles can affect news popularity, and experiments show that this feature can improve R^2 statistics of the prediction model by 6.62% exactly. Besides, we improve traditional category and author features by using logarithmic conversion to views first and calculating a score of these features instead of stuffing them into learning models directly. Using these features and two other features, we finally predict news popularity in two aspects: whether the news will be popular and how many views the news ultimately attract.

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来源期刊

2017 IEEE International Conference on Computer and Information Technology (CIT)

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