We present a method for inferring semantic social networks from textual data by analyzing terminological similarities using topic modeling. Applied to a corpus of 3,698 scientific articles in astrobiology, this approach identifies "hidden communities of interest" (HCoIs)—groups with shared semantic content—and enables diachronic analysis of community evolution.

This study employs Network Theory to investigates the similarities and differences in co-occurrences within Ancient Greek and Latin texts from pre-Christianity and during the early Christian period. The case study focusses on mentions surrounding mentions of Plato in these texts.

This paper examines debates on a German anti-hate speech law called Netzwerkdurchsetzungsgesetz (NetzDG) on Twitter/X. By applying network analysis, NLP methods, and close reading, it investigates content published by the most retweeted accounts mentioning the law, considering their potentially high influence in shaping the discussion on the platform.

This work discusses ongoing tests and development of an R Shiny Web Application to visualize a dataset of National Health Service, Centers for Disease Control and Prevention, and World Health Organization Tweets during 2020. Interactive visualizations display different text analytics using tidytext and quanteda.

Gephi Lite, a web based and lighter version of Gephi, aims at pursuing Gephi original ambition: democratizing network visualisation edition through visual means. In this paper we present what the web does to network visualisation edition while presenting Gephi Lite main features.