  Recommendation algorithms have been pointed out as one of the major culprits
of misinformation spreading in the digital sphere. However, it is still unclear
how these algorithms really propagate misinformation, e.g., it has not been
shown which particular recommendation approaches are more prone to suggest
misinforming items, or which internal parameters of the algorithms could be
influencing more on their misinformation propagation capacity.
  Motivated by this fact, in this paper we present an analysis of the effect of
some of the most popular recommendation algorithms on the spread of
misinformation in Twitter. A set of guidelines on how to adapt these algorithms
is provided based on such analysis and a comprehensive review of the research
literature. A dataset is also generated and released to the scientific
community to stimulate discussions on the future design and development of
recommendation algorithms to counter misinformation. The dataset includes
editorially labelled news items and claims regarding their misinformation
nature.
