An Interpretable Approach to Fake News Detection

Misinformation has long been a tool for political influence, but it has taken a new form in the information age: fake news. After exploding into public consciousness during the 2016 United States presidential election, fake news has become a reality of political life around the world, featuring heavily in the 2017 German election and the 2018 Brazilian election. Fake news poses a significant threat to civic society, and is too easily produced and quickly disseminated to be resolved by manual fact-checking. As such, fake news detection has received significant attention by machine learning and natural language processing researchers in the last years. Previous work in this field has overly relied on deep learning approaches suffering from the black-box problem, rendering them unable to articulate precisely what properties separate fake news from real news. This paper contributes to the limited work on interpretable fake news detection by engineering text-based features, applying statistical tests, and fitting and interpreting logistic regression models. The results of this paper support previous findings that fake and real news are best differentiated by metrics capturing complexity and style, that fake headlines communicate far more than real ones, and that text-based approaches can effectively discern between real and fake news

Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation

Consuming news from social media is becoming increasingly popular. However, social media also enables the widespread of fake news. Because of its detrimental effects brought by social media, fake news detection has attracted increasing attention. However, the performance of detecting fake news only from news content is generally limited as fake news pieces are written to mimic true news. In the real world, news pieces spread through propagation networks on social media. The news propagation networks usually involve multi-levels. In this paper, we study the challenging problem of investigating and exploiting news hierarchical propagation network on social media for fake news detection. In an attempt to understand the correlations between news propagation networks and fake news, first, we build a hierarchical propagation network from macro-level and micro-level of fake news and true news; second, we perform a comparative analysis of the propagation network features of linguistic, structural and temporal perspectives between fake and real news, which demonstrates the potential of utilizing these features to detect fake news; third, we show the effectiveness of these propagation network features for fake news detection. We further validate the effectiveness of these features from feature important analysis. Altogether, this work presents a data-driven view of hierarchical propagation network and fake news and paves the way towards a healthier online news ecosystem.Comment: 10 page

Picasso, Matisse, or a Fake? Automated Analysis of Drawings at the Stroke Level for Attribution and Authentication

This paper proposes a computational approach for analysis of strokes in line drawings by artists. We aim at developing an AI methodology that facilitates attribution of drawings of unknown authors in a way that is not easy to be deceived by forged art. The methodology used is based on quantifying the characteristics of individual strokes in drawings. We propose a novel algorithm for segmenting individual strokes. We designed and compared different hand-crafted and learned features for the task of quantifying stroke characteristics. We also propose and compare different classification methods at the drawing level. We experimented with a dataset of 300 digitized drawings with over 80 thousands strokes. The collection mainly consisted of drawings of Pablo Picasso, Henry Matisse, and Egon Schiele, besides a small number of representative works of other artists. The experiments shows that the proposed methodology can classify individual strokes with accuracy 70%-90%, and aggregate over drawings with accuracy above 80%, while being robust to be deceived by fakes (with accuracy 100% for detecting fakes in most settings)