Prototype takes aim at ‘fake news’

The detection of ‘fake news’ in social media is the goal of a research project by a Charles Sturt PhD student, whose prototype may assist in identifying and mitigating misinformation.

PhD student, Sarah Condran, has developed the Model-agnostic Aggregation Prediction eXplanation (MAPX) tool, which uses AI-driven techniques to identify and flag false narratives, ensuring that governments, media, and the public have access to reliable information. Her work integrates machine learning models with human expertise, creating a dynamic approach to combating digital misinformation.

“Existing models often use content or context features in isolation, limiting their effectiveness, because the dynamic and temporal nature of social media content is often overlooked, and the quality of document features and their impact on prediction trustworthiness is not adequately considered,” Sarah said.

Extensive experiments on benchmarked ‘fake news’ datasets demonstrate that MAPX consistently outperformed state-of-the-art models. Sarah and her co-authors also received a best paper award at the 2024 International Web Information Systems Engineering (WISE) conference.