The Rise and Fall of Fake News sites: A Traffic Analysis

被引：4

作者：

Chalkiadakis, Manolis ^{[1
]}

Kornilakis, Alexandros ^{[1
]}

Papadopoulos, Panagiotis ^{[2
]}

Markatos, Evangelos P. ^{[1
]}

Kourtellis, Nicolas ^{[2
]}

机构：

[1] Univ Crete, FORTH, Iraklion, Greece

[2] Telefonica Res, Madrid, Spain

来源：

PROCEEDINGS OF THE 13TH ACM WEB SCIENCE CONFERENCE, WEBSCI 2021 | 2020年

关键词：

D O I：

10.1145/3447535.3462510

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Over the past decade, we have witnessed the rise of misinformation on the Internet, with online users constantly falling victims of fake news. A multitude of past studies have analyzed fake news diffusion mechanics and detection and mitigation techniques. However, there are still open questions about their operational behavior such as: How old are fake news websites? Do they typically stay online for long periods of time? Do such websites synchronize with each other their up and down time? Do they share similar content through time? Which third-parties support their operations? How much user traffic do they attract, in comparison to mainstream or real news websites? In this paper, we perform a first of its kind investigation to answer such questions regarding the online presence of fake news websites and characterize their behavior in comparison to real news websites. Based on our findings, we build a content-agnostic ML classifier for automatic detection of fake news websites (i.e., F1 score up to 0.942 and AUC of ROC up to 0.976) that are not yet included in manually curated blacklists.

引用

页码：168 / 177

页数：10