Your browser doesn't support javascript.
Community evolution in retweet networks.
Evkoski, Bojan; Mozetic, Igor; Ljubesic, Nikola; Kralj Novak, Petra.
  • Evkoski B; Department of Knowledge Technologies, Jozef Stefan Institute, Ljubljana, Slovenia.
  • Mozetic I; Jozef Stefan International Postgraduate School, Ljubljana, Slovenia.
  • Ljubesic N; Department of Knowledge Technologies, Jozef Stefan Institute, Ljubljana, Slovenia.
  • Kralj Novak P; Department of Knowledge Technologies, Jozef Stefan Institute, Ljubljana, Slovenia.
PLoS One ; 16(9): e0256175, 2021.
Article in English | MEDLINE | ID: covidwho-1381280
ABSTRACT
Communities in social networks often reflect close social ties between their members and their evolution through time. We propose an approach that tracks two aspects of community evolution in retweet networks flow of the members in, out and between the communities, and their influence. We start with high resolution time windows, and then select several timepoints which exhibit large differences between the communities. For community detection, we propose a two-stage approach. In the first stage, we apply an enhanced Louvain algorithm, called Ensemble Louvain, to find stable communities. In the second stage, we form influence links between these communities, and identify linked super-communities. For the detected communities, we compute internal and external influence, and for individual users, the retweet h-index influence. We apply the proposed approach to three years of Twitter data of all Slovenian tweets. The analysis shows that the Slovenian tweetosphere is dominated by politics, that the left-leaning communities are larger, but that the right-leaning communities and users exhibit significantly higher impact. An interesting observation is that retweet networks change relatively gradually, despite such events as the emergence of the Covid-19 pandemic or the change of government.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Pandemics / Social Media / Online Social Networking / SARS-CoV-2 / COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2021 Document Type: Article Affiliation country: Journal.pone.0256175

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: Pandemics / Social Media / Online Social Networking / SARS-CoV-2 / COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2021 Document Type: Article Affiliation country: Journal.pone.0256175