Representation and Knowledge Transfer for Health-related Rumour Detection

he breakthrough of social media has boosted to an increase in the spread of misleading information, with a serious impact on society especially when related to health knowledge. Recently, researchers have been developing AI-based automatic systems to detect rumours in social microblogs. Nevertheless rumours detection at the level of single post, also referred to as micro-level, is still a major challenge since most of the efforts have been directed toward the macro-level, which means that the system considers as rumours news carried by a set of aggregated microblog posts. In this work, we provide two contributions: first, we compare two state-of-the-art representations to figure out which one better catches hidden information in the data. Second, we explore whether it is possible to exploit knowledge extracted on a topic to automatically recognise micro-level rumour in a different one. To this end, we experimentally investigate three transfer learning methods on two health-related datasets. The comparison with a baseline that does not use any knowledge transfer from the source and target domains reveals that negative transfer occurs.