Abstract
Online social networks are ubiquitous, have billions of users, and produce large amounts of data. While platforms like Reddit are based on a forum-like organization where users gather around topics, Facebook and Twitter implement a concept in which individuals represent the primary entity of interest. This makes them natural testbeds for exploring individual behavior in large social networks. Underlying these individual-based platforms is a network whose “friend” or “follower” edges are of binary nature only and therefore do not necessarily reflect the level of acquaintance between pairs of users. In this paper,we present the network of acquaintance “strengths” underlying the German Twittersphere. To that end, we make use of the full non-verbal information contained in tweet–retweet actions to uncover the graph of social acquaintances among users, beyond pure binary edges. The social connectivity between pairs of users is weighted by keeping track of the frequency of shared content and the time elapsed between publication and sharing. Moreover, we also present a preliminary topological analysis of the German Twitter network. Finally, making the data describing the weighted German Twitter network of acquaintances, we discuss how to apply this framework as a ground basis for investigating spreading phenomena of particular contents.
Highlights
Over the past two decades, online social networks (OSN) have become a part of the daily life of billions of individuals worldwide
Uncovering the underlying network of acquaintances of the German Twittersphere would provide a testbed for further investigations and open the possibility for further investigation of dynamical processes, such as the propagation of information—and misinformation—on a network of acquaintances
To generate large-scale networks of social connectivity built up using Twitter interaction data only
Summary
Over the past two decades, online social networks (OSN) have become a part of the daily life of billions of individuals worldwide Since their operation inherently generates data, OSNs allow the study of human behavior at massive scales. The main aim of this paper is to uncover the network of (weighted) acquaintances of the German Twitter platform, in order to assess the dynamical aspects on tweet sharing activity. To this end, we introduce a framework. To generate large-scale networks of social connectivity built up using Twitter interaction data only We archive this using a weighting function that measures the strength of connectivity for Twitter user pairs based on their communication and sharing behavior. The same claim has been supported in a w ork[9] analysing around 3.2 million users with the aim of building an interaction network to map communities of acquaintances
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
