Preventing profiling for ethical fake news detection

A news article's online audience provides useful insights about the article's identity. However, fake news classifiers using such information risk relying on profiling. In response to the rising demand for ethical AI, we present a profiling-avoiding algorithm that leverages Twitter users during model optimisation while excluding them when an article's veracity is evaluated. For this, we take inspiration from the social sciences and introduce two objective functions that max-imise correlation between the article and its spreaders, and among those spreaders. We applied our profiling-avoiding algorithm to three popular neural classifiers and obtained results on fake news data discussing a variety of news topics. The positive impact on prediction performance demonstrates the soundness of the proposed objective functions to integrate social context in text-based classifiers. Moreover, statistical visualisation and dimension reduction techniques show that the user-inspired classifiers better discriminate between unseen fake and true news in their latent spaces. Our study serves as a stepping stone to resolve the underexplored issue of profiling-dependent decision-making in user-informed fake news detection.

Automated summary

The online audience of a news article provides valuable insights about its identity, but using this information for fake news classification may result in reliance on profiling. To address the increasing demand for ethical AI, a profiling-avoiding algorithm is proposed that leverages Twitter users for model optimization while excluding them during the evaluation of article veracity. This algorithm incorporates objective functions inspired by the social sciences to maximize correlation between the article and its spreaders, as well as among the spreaders. Experimental results demonstrate the positive impact of this approach in improving prediction performance and discriminatory capability between fake and true news.