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Social network analysis (SNA) is the process of investigating social structures through the use of networks and graph theory. [1] It characterizes networked structures in terms of nodes (individual actors, people, or things within the network) and the ties , edges , or links (relationships or interactions) that connect them.
Social media has grown in popularity, and many people around the world now use it. People use social media to share information, ideas, personal messages, and other content (such as videos). [1] Around 95% of young people between the ages of 13–17 use at least one social media platform, [2] making it a major influence on young adolescents ...
Graph-tool is a python module for efficient analysis of graphs. Its core data structures and algorithms are implemented in C++, with heavy use of Template metaprogramming, based on the Boost Graph Library. It contains a comprehensive list of algorithms. R: Social network analysis within the versatile and popular R environment
Social media analytics or social media monitoring is the process of gathering and analyzing data from social networks such as Facebook, Instagram, LinkedIn, or Twitter. A part of social media analytics is called social media monitoring or social listening. It is commonly used by marketers to track online conversations about products and companies.
The social graph is a graph that represents social relations between entities. In short, it is a model or representation of a social network, where the word graph has been taken from graph theory. The social graph has been referred to as "the global mapping of everybody and how they're related". [1]
Module:Chart creates bar and pie charts on Wikipedia without need for external tools; Many spreadsheet, drawing, and desktop publishing programs allow you to create graphs and export them as images. gnuplot can produce a wide variety of charts and graphs; see samples with source code at Commons. In Python using matplotlib
According to the Surgeon General's 2023 advisory on social media use in youth, negative mental health outcomes are particularly elevated for those who spend more than three hours per day on social ...
The three domains that neural systems rely on to be strengthened to support social media use are social cognition, self-referential cognition, and social rewarding. [11] When someone posts something on social media, they think of how their audience will react, while the audience thinks of the motivations behind posting the information. [12]