In recent years, social media has become a common means for sharing and consuming news. However, the spread of misinformation and fake news on these platforms has posed a major challenge to the well-being of individuals and societies. Therefore, it is imperative that we develop robust and automated solutions for early detection of fake news on social media. Traditional approaches rely purely on the news content (using natural language processing) to mark information as real or fake. However, the social context in which the news is published and shared can provide additional insights into the nature of fake news on social media and improve the predictive capabilities of fake news detection tools.

Data models and query languages are admittedly somewhat dry topics for people who are not in the inner circle of connoisseurs. Although graph data models and query languages are no exception to that rule, we've tried to keep track of developments in that area, for one main reason. Graph is the fastest growing area in the biggest segment in enterprise software -- databases. Case in point: A series of recent funding rounds, culminating in Neo4j's $325 million Series F funding round, brought its valuation to over $2 billion. Neo4j is among the graph database vendors who have been around the longest, and it now is the best-funded one, too.