The hidden dimension of information diffusion: A latent space representation of Social Media News Sharing behavior.
CoRR(2023)
摘要
In times marked by an abundance of news sources and the widespread use of
social media for staying informed, acquiring accurate data faces increasing
challenges. Today, access to information plays a crucial role in shaping public
opinion and is significantly influenced by interactions on social media.
Therefore, studying the dissemination of news on these platforms is vital for
understanding how individuals stay informed. In this paper, we study emergent
properties of media outlet sharing behavior by users in social media. We
quantify this behavior in terms of coordinates in a latent space proposing a
metric called Media Sharing Index (MSI). We observe that the MSI shows a
bimodal distribution in this latent dimension, reflecting the preference of
large groups of users for specific groups of media outlets. This methodology
allows the study of the extent to which communities of interacting users are
permeable to different sources of information. Additionally, it facilitates the
analysis of the relationship between users' media outlet preferences, their
political leanings, and the political leanings of the media outlets.
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