  According to the classical definition, propaganda is the management of
collective attitudes by manipulation of significant symbols. However this
definition has changed to computational propaganda, the way manipulation takes
place in digital medium. Computational propaganda is the use of algorithms,
automation and human curation to purposefully distribute misleading information
over social media networks to manipulate public opinion, for political
polarization etc. Digital media platforms have introduced new modalities of
propaganda such as the use of social bots and state-organized 'troll armies'
for social astroturfing to simulate public support or opposition towards a
particular topic. Along with this digital media has blurred the line between
different forms of propaganda. Hence existing conceptual and epistemological
frameworks in propaganda studies need a revision. One of the methods to detect
the computational propaganda is to identify automation and bots. Many
supervised machine learning based frameworks have been proposed for bot
detection but these systems can only identify single accounts, not the
coordinated activities of botnets and also these systems depend on the data
structure provided by the social media platforms. Similarly, current systems
have not included the image features in their detection system. Most of the
systems are mainly built for Twitter while there are still uncharted areas of
research in other social media platforms. Therefore, there are many unexplored
research questions and methods in bot detection systems.
