Entropy-rate as prediction method for newspapers and information diffusion
Andrea Russo, Antonio Picone, Vincenzo Miracula, Giovanni Giuffrida,, Francesco Mazzeo Rinaldi

TL;DR
This paper proposes using entropy-rate analysis of social network topics to predict online newspaper viewership and information diffusion, aiding organizations in managing communication strategies.
Contribution
It introduces a novel method leveraging information theory, specifically entropy-rate, to forecast social media trends and their impact on newspaper readership.
Findings
Entropy-rate correlates with online newspaper views.
Identified dynamics influencing information diffusion.
Method predicts communication volume and audience engagement.
Abstract
This paper aims to show how some popular topics on social networks can be used to predict online newspaper views, related to the topics. Newspapers site and many social networks, become a good source of data to analyse and explain complex phenomena. Understanding the entropy of a topic, could help all organizations that need to share information like government, institution, newspaper or company, to expect an higher activity over their channels, and in some cases predict what the receiver expect from the senders or what is wrong about the communication. For some organization such political party, leaders, company and many others, the reputation and the communication are (for most of them) the key part of a more and complex huge system. To reach our goal, we use gathering tools and information theory to detect and analyse trends topic on social networks, with the purpose of proved a…
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Taxonomy
TopicsOpinion Dynamics and Social Influence · Complex Network Analysis Techniques
