The rise of online misinformation is posing a threat to the functioning of democratic processes. The ability to algorithmically spread false information through online social networks together with the data-driven ability to profile and micro-target individual users has made it possible to create customized false content that has the potential to influence decision making processes. Fortunately, similar data-driven and algorithmic methods can also be used to detect misinformation and to control its spread. Automatically estimating the reliability and trustworthiness of information is, however, a complex problem and it is today addressed by heavily relying on human experts known as fact-checkers. In this paper, we present the challenges and opportunities of combining automatic and manual fact-checking approaches to combat the spread on online misinformation also highlighting open research questions that the data engineering community should address.