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Comparative Analysis of Statistical Classifiers for Predicting News Popularity on Social Web

机译：统计分类器在社交网络上预测新闻流行度的比较分析

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摘要

In recent years, owing mainly to the ubiquity of the Internet, social media platforms are more popular than ever before. Facebook boasts of over 2 billion registered accounts and the number of individual users is said to be more than the population of most countries. It is clear that social media has our attention, and media houses are no strangers to this fact. A huge amount of time and resource is put into the research and development of strategies that will help news flash become more popular. One of the major driving factors of news popularity is the sentiment and emotion behind the news. Human emotions are the driving force of any microblog on social media today and in our research, we attempt to study some of these fields that affect the mood of people. These features include specific properties about the news, such as the sentiment and the topic of the news itself. They further include factors unrelated to the news articles that may affect the news reading behavior of readers, like the day of week or time of the day. Our research provides an approach to design a predictive model for the popularity of a news article on a particular social media platform, based on the input features.
机译：近年来，主要由于互联网的普及，社交媒体平台比以往任何时候都更受欢迎。 Facebook拥有超过20亿个注册帐户，据说个人用户数量超过了大多数国家/地区的人口。显然，社交媒体引起了我们的注意，媒体公司对此事实并不陌生。大量的时间和资源投入到策略的研究和开发中，这将有助于新闻速写变得更加流行。新闻流行的主要驱动因素之一是新闻背后的情感和情感。人类情感是当今社交媒体上任何微博的驱动力，在我们的研究中，我们尝试研究影响人们情绪的某些领域。这些功能包括有关新闻的特定属性，例如新闻本身的情感和主题。它们还包括与新闻无关的因素，这些因素可能会影响读者的新闻阅读行为，例如星期几或一天中的时间。我们的研究提供了一种基于输入功能来设计新闻模型在特定社交媒体平台上受欢迎程度的预测模型的方法。

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