Towards explainable fake news detection and automated content credibility assessment: Polish internet and digital media use-case

Detecting fake news and disinformation is a challenging and complex task. With the proliferation of the transformer architectures, researchers and practitioners have started actively using them to combat this phenomenon. In that regard, different approaches have been proposed by the community. Unfortunately, a significant number of solutions have started to treat misinformation in a similar way as other natural language processing challenges (e.g., sentiment classification). However, the reality is more complex, and the fake news detection problem often require additional context that may be essential to provide a complete assessment. Another pitfall in combating fake news is treating it as a binary classification challenge (e.g., fake vs. legitimate news). Combining such a simplified response with a deep black-box neural system effectively impacts the interpretability and eventual trustworthiness of the detection system. In this paper, the phenomenon of fake news is approached from a multi-factor perspective. Therefore, the proposed innovative approach does not limit news content rating to a one-dimensional binary response. In particular, the authors utilize a vast spectrum of processing techniques, including text embedding, machine translation, and various users’ preferences to provide content assessment. The results are presented using a Polish digital media use-case. • Novel approach to content assessment. • Combination of text embedding, machine translation and users’ preferences. • Innovative, complex approach to annotation. • New dataset of content in Polish language. • New pipeline including information about the annotator.