ArticleScientific reports2018
Entropy Measures of Human Communication Dynamics.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- Shannon Entropy and Lévy Statistics for Machine Learning-Assisted Spectral Sensing in Random Fiber Lasers With Magnetic Core-Shell Nanoscatterers.Nanophotonics (Berlin, Germany) · 2026Article
- Using LLMs to Infer Non-Binary COVID-19 Sentiments of Chinese Microbloggers.Entropy (Basel, Switzerland) · 2025Article
- Introducing Entropy into Organizational Psychology: An Entropy-Based Proactive Control Model.Behavioral sciences (Basel, Switzerland) · 2024Review
- Article
- Network Analytics Enabled by Generating a Pool of Network Variants from Noisy Data.Entropy (Basel, Switzerland) · 2023Article
- Characterizing Topics in Social Media Using Dynamics of Conversation.Entropy (Basel, Switzerland) · 2021Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human communication is commonly represented as a temporal social network, and evaluated in terms of its uniqueness. We propose a set of new entropy-based measures for human communication dynamics represented within the temporal social network as event sequences. Using real world datasets and random interaction series of different types we find that real human contact events always significantly differ from random ones. This human distinctiveness increases over time and by means of the proposed entropy measures, we can observe sociological processes that take place within dynamic communities.
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