论文标题
将机器学习与知识工程相结合以检测社交网络中的虚假新闻 - A调查
Combining Machine Learning with Knowledge Engineering to detect Fake News in Social Networks-a survey
论文作者
论文摘要
由于虚假新闻在社交和新闻媒体上广泛传播，因此现在成为了一个新兴的研究主题。在新闻媒体和社交媒体中，信息传播高速，但没有准确性，因此检测机制应该能够足够快地预测新闻，以解决对假新闻的传播。它有可能对个人和社会产生负面影响。因此，在社交媒体上检测假新闻很重要，并且这些天在技术上具有挑战性的问题。我们知道，机器学习有助于基于隐性知识建立人工智能系统，因为它可以帮助我们通过真实的单词数据解决复杂的问题。另一方面，我们知道知识工程有助于代表专家知识，人们知道这些知识的知识。因此，我们建议机器学习和知识工程的整合有助于检测假新闻。在本文中，我们介绍了假新闻的重要性，虚假新闻对不同领域的总体影响，在社交媒体上检测假新闻的不同方法，现有的检测算法，这些算法可以帮助我们克服问题，相似的应用领域，最后我们提出了数据驱动和工程的结合，以打击假新闻。我们研究并比较了三个不同的模块文本分类器，立场检测应用程序以及事实检查现有的技术，这些技术可以帮助检测假新闻。此外，我们研究了假新闻对社会的影响。对公开可用数据集的实验评估以及我们提出的假新闻检测组合可以更好地检测假新闻。
Due to extensive spread of fake news on social and news media it became an emerging research topic now a days that gained attention. In the news media and social media the information is spread highspeed but without accuracy and hence detection mechanism should be able to predict news fast enough to tackle the dissemination of fake news. It has the potential for negative impacts on individuals and society. Therefore, detecting fake news on social media is important and also a technically challenging problem these days. We knew that Machine learning is helpful for building Artificial intelligence systems based on tacit knowledge because it can help us to solve complex problems due to real word data. On the other side we knew that Knowledge engineering is helpful for representing experts knowledge which people aware of that knowledge. Due to this we proposed that integration of Machine learning and knowledge engineering can be helpful in detection of fake news. In this paper we present what is fake news, importance of fake news, overall impact of fake news on different areas, different ways to detect fake news on social media, existing detections algorithms that can help us to overcome the issue, similar application areas and at the end we proposed combination of data driven and engineered knowledge to combat fake news. We studied and compared three different modules text classifiers, stance detection applications and fact checking existing techniques that can help to detect fake news. Furthermore, we investigated the impact of fake news on society. Experimental evaluation of publically available datasets and our proposed fake news detection combination can serve better in detection of fake news.
