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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.
Moreover, the size of the data set collected vary with the popularity of the social media platform i.e. social media platforms having high number of users will have more data than platforms having less user base. [8] Scraping is a process in which the APIs collect online data from social media. The data collected from Scraping is in raw format.
Social media mining uses concepts from computer science, data mining, machine learning, and statistics. Mining is based on social network analysis, network science, sociology, ethnography, optimization and mathematics. It attempts to formally represent, measure and model patterns from social media data. [1]
Buzz in Social Media Dataset Data from Twitter and Tom's Hardware. This dataset focuses on specific buzz topics being discussed on those sites. Data is windowed so that the user can attempt to predict the events leading up to social media buzz. 140,000 Text Regression, Classification 2013 [55] [56] F. Kawala et al.
A social network diagram displaying friendship ties among a set of Facebook users.. Social network analysis (SNA) is the process of investigating social structures through the use of networks and graph theory. [1]
Social network advertising, also known as social media targeting, is a group of terms used to describe forms of online advertising and digital marketing that focus on social networking services. A significant aspect of this type of advertising is that advertisers can take advantage of users' demographic information , psychographics , and other ...
A simple social network: the nodes represent people or actors and the edges between nodes represent some relationship between actors. Katz centrality computes the relative influence of a node within a network by measuring the number of the immediate neighbors (first degree nodes) and also all other nodes in the network that connect to the node under consideration through these immediate neighbors.
Small-world networks have been used to estimate the usability of information stored in large databases. The measure is termed the Small World Data Transformation Measure. [27] [28] The greater the database links align to a small-world network the more likely a user is going to be able to extract information in the future. This usability ...