Context Map Analysis of Fake News in Social Media: A Contextualized Visualization Approach

Abstract

Visualization tools in text analytics are typically based on content analysis, using $n$-gram frequencies or topic models which output commonly used words, phrases, or topics in a text corpus. However, the interpretation of these visual output and summary labels can be incomplete or misleading when words or phrases are taken out of context. We use a novel Context Map approach to create a connected network of $n$-grams by considering the frequency in which they are used together in the same context. We combine network optimization techniques with embedded representation models to generate an visualization interface with clearer and more accurate interpretation potential. In this paper, we apply our Context Map method to analyze fake news in social media. We compare news article veracity (true versus false news) with orientation (left, mainstream, or right). Our approach provides a rich context analysis of the language used in true versus fake news.