Rumour Veracity Assessment in Social Media

Recent years have witnessed a growing proliferation of social media that serve as channels for rapid information dissemination. However, social media have also become a hotbed of viral hoaxes and online disinformation. It thus becomes increasingly important for journalists, emergency services and government agencies to be able to detect in near real time fast-spreading mis/disinformation. This has given rise to a growing body of research on automatic mis/disinformation detection, fact checking and content verification. Present accuracy levels, however, fall short of the accuracy required for practical adoption as training data is small and method performance tends to degrade significantly on unseen mis/disinformation. This research project will thus carry out novel research on supervised and unsupervised methods for veracity assessment by integrating information from multiple social sources and building a knowledge network that enables cross verification.

This high-level aim gives rise to the following objectives:
RO1: Develop an unsupervised feature extraction approach for identifying discriminative features for effective rumour veracity classification;
RO2: Develop supervised approaches built on graphical neural networks by incorporating evidence from external sources, through cross-media analysis and cross-linking;
RO3: Implement distributed probabilistic inference tools to analyse dynamic spreading-patterns of dis/misinformation in social media;
RO4: Validate the new methods in benchmarking datasets.