Facebook, Twitter, Reddit, Instagram, TikTok and other online platforms have created whole new avenues for research. "Social Media Analysis" can be seen as the intersection of text analysis and network analysis. The "text" is often in the form of discussions, and may include images, emoji, and other media. The "media" is often structured in the form of a network, where the relationships between people and ideas become the focus of study. Information presented here is geared towards helping you collect social media data, and includes tools tailored for analyzing and visualizing data from specific social media platforms. For a more in-depth look at data sources you may want to use for your research, see our Data Services guide.
Related techniques: Text Analysis, Qualitative Analysis, Web Scraping
This technique is part of the Analysis activity.
Online Social Media Analysis and Visualization by
This edited volume addresses the vast challenges of adapting Online Social Media (OSM) to developing research methods and applications. The topics cover generating realistic social network topologies, awareness of user activities, topic and trend generation, estimation of user attributes from their social content, behavior detection, mining social content for common trends, identifying and ranking social content sources, building friend-comprehension tools, and many others. Each of the ten chapters tackle one or more of these issues by proposing new analysis methods or new visualization techniques, or both, for famous OSM applications such as Twitter and Facebook. This collection of contributed chapters address these challenges. Online Social Media has become part of the daily lives of hundreds of millions of users generating an immense amount of 'social content'. Addressing the challenges that stem from this wide adaptation of OSM is what makes this book a valuable contribution to the field of social networks.
Social Network Analysis - Community Detection and Evolution by
This book is devoted to recent progress in social network analysis with a high focus on community detection and evolution. The eleven chapters cover the identification of cohesive groups, core components and key players either in static or dynamic networks of different kinds and levels of heterogeneity. Other important topics in social network analysis such as influential detection and maximization, information propagation, user behavior analysis, as well as network modeling and visualization are also presented. Many studies are validated through real social networks such as Twitter. This edited work will appeal to researchers, practitioners and students interested in the latest developments of social network analysis.
Graph-Based Social Media Analysis by
Focused on the mathematical foundations of social media analysis, Graph-Based Social Media Analysis provides a comprehensive introduction to the use of graph analysis in the study of social and digital media. It addresses an important scientific and technological challenge, namely the confluence of graph analysis and network theory with linear algebra, digital media, machine learning, big data analysis, and signal processing. Supplying an overview of graph-based social media analysis, the book provides readers with a clear understanding of social media structure. It uses graph theory, particularly the algebraic description and analysis of graphs, in social media studies. The book emphasizes the big data aspects of social and digital media. It presents various approaches to storing vast amounts of data online and retrieving that data in real-time. It demystifies complex social media phenomena, such as information diffusion, marketing and recommendation systems in social media, and evolving systems. It also covers emerging trends, such as big data analysis and social media evolution. Describing how to conduct proper analysis of the social and digital media markets, the book provides insights into processing, storing, and visualizing big social media data and social graphs. It includes coverage of graphs in social and digital media, graph and hyper-graph fundamentals, mathematical foundations coming from linear algebra, algebraic graph analysis, graph clustering, community detection, graph matching, web search based on ranking, label propagation and diffusion in social media, graph-based pattern recognition and machine learning, graph-based pattern classification and dimensionality reduction, and much more. This book is an ideal reference for scientists and engineers working in social media and digital media production and distribution. It is also suitable for use as a textbook in undergraduate or graduate courses on digital media, social media, or social networks.
free | data collection only | web-based, using Google Sheets | easy to use
free, with paid upgrades available | data collection & basic analysis | web-based | easy to use
free | Python knowledge required | data collection only | output is in JSON format
twarc is a command line tool created by the Documenting the Now project. They have also created a catalog of public tweet ID data sets and a tool called Hydrator desktop application for turning tweet IDs into full tweets with metadata.
free for educational use; "pro" accounts available | data collection & basic analysis | web-based | comfort with GitHub, text editor and command-line required | output in CSV format
free | data collection only | PHP required | comfort with GitHub, text editor and command-line required | output in CSV format
free | data collection only | Python knowledge required
free, with paid upgrades available | data collection & analysis | web-based | easy to use
free plan limited to 500 pages/month | data collection only | web-based, using browser extension
free plan limited to 10k records per export | data collection only | Windows only
free | data collection only | programming required if not using existing data sets
free | Windows, Mac and Linux downloads | easy to learn