  Participation on social media platforms has many benefits but also poses
substantial threats. Users often face an unintended loss of privacy, are
bombarded with mis-/disinformation, or are trapped in filter bubbles due to
over-personalized content. These threats are further exacerbated by the rise of
hidden AI-driven algorithms working behind the scenes to shape users' thoughts,
attitudes, and behavior. We investigate how multimedia researchers can help
tackle these problems to level the playing field for social media users. We
perform a comprehensive survey of algorithmic threats on social media and use
it as a lens to set a challenging but important research agenda for effective
and real-time user nudging. We further implement a conceptual prototype and
evaluate it with experts to supplement our research agenda. This paper calls
for solutions that combat the algorithmic threats on social media by utilizing
machine learning and multimedia content analysis techniques but in a
transparent manner and for the benefit of the users.
