Interactions in Information Spread

TL;DR

This paper emphasizes the importance of modeling user interactions with information in online spreading processes, proposing a new interaction model that improves understanding of information diffusion.

Contribution

It introduces a novel interaction modeling approach that accounts for previous information exposure, addressing gaps in existing models of information spread.

Findings

Interaction modeling enhances understanding of information diffusion.

The proposed approach outperforms existing methods.

Considering interactions is crucial for accurate spread modeling.

Abstract

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.