  Social bots have recently gained attention in the context of public opinion
manipulation on social media platforms. While a lot of research effort has been
put into the classification and detection of such (semi-)automated programs, it
is still unclear how sophisticated those bots actually are, which platforms
they target, and where they originate from. To answer these questions, we
gathered repository data from open source collaboration platforms to identify
the status-quo as well as trends of publicly available bot code. Our findings
indicate that most of the code on collaboration platforms is of supportive
nature and provides modules of automation instead of fully fledged social bot
programs. Hence, the cost (in terms of additional programming effort) for
building social bots with the goal of topic-specific manipulation is higher
than assumed and that methods in context of machine- or deep-learning currently
only play a minor role. However, our approach can be applied as multifaceted
knowledge discovery framework to monitor trends in public bot code evolution to
detect new developments and streams.
