English

Signed ego network model and its application to Twitter

Social and Information Networks 2022-12-02 v2

Abstract

The Ego Network Model (ENM) describes how individuals organise their social relations in concentric circles (typically five) of decreasing intimacy, and it has been found almost ubiquitously in social networks, both offline and online. The ENM gauges the tie strength between peers in terms of interaction frequency, which is easy to measure and provides a good proxy for the time spent nurturing the relationship. However, advances in signed network analysis have shown that positive and negative relations play very different roles in network dynamics. For this reason, this work sets out to investigate the ENM when including signed relations. The main contributions of this paper are twofold: firstly, a novel method of signing relationships between individuals using sentiment analysis and, secondly, an investigation of the properties of Signed Ego Networks (Ego Networks with signed connections). Signed Ego Networks are then extracted for the users of eight different Twitter datasets composed of both specialised users (e.g. journalists) and generic users. We find that negative links are over-represented in the active part of the Ego Networks of all types of users, suggesting that Twitter users tend to engage regularly with negative connections. Further, we observe that negative relationships are overwhelmingly predominant in the Ego Network circles of specialised users, hinting at very polarised online interactions for this category of users. In addition, negative relationships are found disproportionately more at the more intimate levels of the ENM for journalists, while their percentages are stable across the circles of the other Twitter users

Keywords

Cite

@article{arxiv.2206.15228,
  title  = {Signed ego network model and its application to Twitter},
  author = {Jack Tacchi and Chiara Boldrini and Andrea Passarella and Marco Conti},
  journal= {arXiv preprint arXiv:2206.15228},
  year   = {2022}
}

Comments

This work was partially funded by the H2020 SoBigData++ (Grant No 871042), H2020 HumaneAI-Net (Grant No 952026), and CHIST-ERA SAI (Grant No not yet available) projects

R2 v1 2026-06-24T12:09:35.181Z