It attempts to formally represent, measure and model patterns from social media data.
[1] In the 2010s, major corporations, governments and not-for-profit organizations began mining to learn about customers, clients and others.
Platforms such as Google, Facebook (partnered with Datalogix and BlueKai) conduct mining to target users with advertising.
[2] Scientists and machine learning researchers extract insights and design product features.
During the 2016 United States presidential election, Facebook allowed Cambridge Analytica, a political consulting firm linked to the Trump campaign, to analyze the data of an estimated 87 million Facebook users to profile voters, creating controversy when this was revealed.
[5] As defined by Kaplan and Haenlein,[6] social media is the "group of internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of user-generated content."
Social media mining faces grand challenges such as the big data paradox, obtaining sufficient samples, the noise removal fallacy, and evaluation dilemma.
These influencers are determined by recognition, activity generation, and novelty—three requirements that can be measured through the data mined from these sites.
Social media networks can use this information themselves to suggest to their users possible friends to add, pages to follow, and accounts to interact with.
Modern social media mining is a controversial practice that has led to exponential gains in user growth for tech giants such as Facebook, Inc., Twitter, and Google.
Users typically have access to a varied set of analytics specific to people that interact with them on social media, and can use these as building blocks for their own targeting and growth strategies through ads and posts that cater to their audiences.