  Large quantities of data flow on the internet. When a user decides to help
the spread of a piece of information (by retweeting, liking, posting content),
most research works assumes she does so according to information's content,
publication date, the user's position in the network, the platform used, etc.
However, there is another aspect that has received little attention in the
literature: the information interaction. The idea is that a user's choice is
partly conditioned by the previous pieces of information she has been exposed
to. In this document, we review the works done on interaction modeling and
underline several aspects of interactions that complicate their study. Then, we
present an approach seemingly fit to answer those challenges and detail a
dedicated interaction model based on it. We show our approach fits the problem
better than existing methods, and present leads for future works. Throughout
the text, we show that taking interactions into account improves our
comprehension of information interaction processes in real-world datasets, and
argue that this aspect of information spread is should not be neglected when
modeling spreading processes.

Social and Information Networks