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所在平台: Udemy |
课程主页: https://www.udemy.com/course/ahol-dl4nlp10/
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课程名称：深度学习与自然语言处理 - 第10部分 概述： 假新闻被视为对民主、新闻业和言论自由的重大威胁，尤其在2016年美国总统竞选期间，假新闻的传播令人瞩目。在这一周期，前20个讨论频率最高的假选举故事在Facebook上共获得了870万次分享、反应和评论，而来自19个主要新闻网站的前20个最受讨论的真实选举故事仅获得740万次。研究显示，相较于真实新闻，假新闻在Twitter上的转发数量通常要高得多，尤其是在政治新闻中，这使假新闻传播得极其迅速。此外，假新闻还影响了经济市场，例如，2017年关于奥巴马总统受伤的假新闻导致1300亿美元的股票价值蒸发。这些现象促使学者们对假新闻进行研究，并引发了有关假新闻的广泛讨论。 本课程将提供关于假新闻的全面介绍和深入分析，分为三个部分展开。第一部分介绍假新闻检测，包括“什么是假新闻及相关领域”、“如何手动识别假新闻”、“为什么需要检测假新闻”以及各组织在打击假新闻方面的努力。第二部分将重点讲解四种假新闻检测方法：基于知识的检测、基于风格的检测、基于传播的检测和基于可信度的检测。最后，第三部分将讨论与假新闻检测相关的其他视角和主题，包括假新闻检测的数据集、可解释的假新闻检测、关于假新闻检测的关注点及研究机会。 希望您能够享受本课程，并找到对您工作有用的理念。
Fake news is now viewed as one of the greatest threats to democracy, journalism, and freedom of expression. The reach of fake news was best highlighted during the critical months of the 2016 U.S. presidential election campaign. During that period, the top twenty frequently-discussed fake election stories generated 8.7M shares, reactions, and comments on Facebook, ironically, more than the 7.4M for the top twenty most-discussed election stories posted by 19 major news websites. Research has shown that compared to the truth, fake news on Twitter is typically retweeted by many more users and spreads far more rapidly, especially for political news. Our economies are not immune to the spread of fake news either, with fake news being connected to stock market fluctuations and large trades. For example, fake news claiming that Barack Obama, the 44th President of the United States, was injured in an explosion wiped out $130 billion in stock value in 2017. These events and losses have motivated fake news research and sparked the discussion around fake news, as observed by skyrocketing usage of terms such as "post-truth" - selected as the international word of the year by Oxford Dictionaries in 2016. The many perspectives on what fake news is, what characteristics and nature fake news or those who disseminate it share, and how fake news can be detected motivate the need for a comprehensive introduction and in-depth analysis, which this course aims to develop. This course is divided into three sections.In the first section, I will introduce fake new detection, and discuss topics like "what is fake news and related areas", "how to manually identify fake news", "why detect fake news" and "efforts by various organisations towards fighting fake news". In the second section we will focus on various types of fake news detection methods. Specifically I will talk about four different fake news detection methods which are knowledge based fake news detection, style based fake news detection, propagation based fake news detection and credibility based fake news detection. Lastly in the third section I'll talk about other perspectives and topics related to fake news detection including fake news detection datasets, explainable fake news detection, concerns around fake news detection and research opportunities. Hope you will enjoy this course and find the ideas useful for your work.
