  Given the huge impact that Online Social Networks (OSN) had in the way people
get informed and form their opinion, they became an attractive playground for
malicious entities that want to spread misinformation, and leverage their
effect. In fact, misinformation easily spreads on OSN and is a huge threat for
modern society, possibly influencing also the outcome of elections, or even
putting people's life at risk (e.g., spreading "anti-vaccines" misinformation).
Therefore, it is of paramount importance for our society to have some sort of
"validation" on information spreading through OSN. The need for a wide-scale
validation would greatly benefit from automatic tools.
  In this paper, we show that it is difficult to carry out an automatic
classification of misinformation considering only structural properties of
content propagation cascades. We focus on structural properties, because they
would be inherently difficult to be manipulated, with the the aim of
circumventing classification systems. To support our claim, we carry out an
extensive evaluation on Facebook posts belonging to conspiracy theories (as
representative of misinformation), and scientific news (representative of
fact-checked content). Our findings show that conspiracy content actually
reverberates in a way which is hard to distinguish from the one scientific
content does: for the classification mechanisms we investigated, classification
F1-score never exceeds 0.65 during content propagation stages, and is still
less than 0.7 even after propagation is complete.
