Galli Antonio, Masciari Elio, Moscato Vincenzo, Sperlí Giancarlo
Department of Electrical and Information Technology (DIETI), University of Naples, Federico II via Claudio 21, 80125 Naples, Italy.
J Intell Inf Syst. 2022;59(1):237-261. doi: 10.1007/s10844-021-00646-9. Epub 2022 Mar 21.
Nowadays, really huge volumes of fake news are continuously posted by malicious users with fraudulent goals thus leading to very negative social effects on individuals and society and causing continuous threats to democracy, justice, and public trust. This is particularly relevant in social media platforms (e.g., Facebook, Twitter, Snapchat), due to their intrinsic uncontrolled publishing mechanisms. This problem has significantly driven the effort of both academia and industries for developing more accurate fake news detection strategies: early detection of fake news is crucial. Unfortunately, the availability of information about news propagation is limited. In this paper, we provided a benchmark framework in order to analyze and discuss the most widely used and promising machine/deep learning techniques for fake news detection, also exploiting different features combinations w.r.t. the ones proposed in the literature. Experiments conducted on well-known and widely used real-world datasets show advantages and drawbacks in terms of accuracy and efficiency for the considered approaches, even in the case of limited content information.
如今，怀有欺诈目的的恶意用户持续发布大量虚假新闻，从而对个人和社会产生非常负面的社会影响，并对民主、正义和公众信任造成持续威胁。由于其固有的不受控制的发布机制，这在社交媒体平台（如脸书、推特、阅后即焚）中尤为突出。这个问题极大地推动了学术界和产业界开发更准确的假新闻检测策略的努力：假新闻的早期检测至关重要。不幸的是，关于新闻传播的信息可用性有限。在本文中，我们提供了一个基准框架，以便分析和讨论用于假新闻检测的最广泛使用且最有前景的机器学习/深度学习技术，同时还利用了与文献中提出的不同的特征组合。在知名且广泛使用的真实世界数据集上进行的实验表明，即使在内容信息有限的情况下，所考虑的方法在准确性和效率方面也各有优缺点。
