  Despite the increasing use of social media platforms for information and news
gathering, its unmoderated nature often leads to the emergence and spread of
rumours, i.e. pieces of information that are unverified at the time of posting.
At the same time, the openness of social media platforms provides opportunities
to study how users share and discuss rumours, and to explore how natural
language processing and data mining techniques may be used to find ways of
determining their veracity. In this survey we introduce and discuss two types
of rumours that circulate on social media; long-standing rumours that circulate
for long periods of time, and newly-emerging rumours spawned during fast-paced
events such as breaking news, where reports are released piecemeal and often
with an unverified status in their early stages. We provide an overview of
research into social media rumours with the ultimate goal of developing a
rumour classification system that consists of four components: rumour
detection, rumour tracking, rumour stance classification and rumour veracity
classification. We delve into the approaches presented in the scientific
literature for the development of each of these four components. We summarise
the efforts and achievements so far towards the development of rumour
classification systems and conclude with suggestions for avenues for future
research in social media mining for detection and resolution of rumours.
