User:Hbaranowski/Social media mining

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Uses[edit]
Social media mining is used across several industries including business development, social science research, health services, and educational purposes. Once the data received goes through social media analytics, it can then be applied to these various fields. Often, companies use the patterns of connectivity that pervade social networks, such as assortativity—the social similarity between users that are induced by influence, homophily, and reciprocity and transitivity. These forces are then measured via statistical analysis of the nodes and connections between these nodes. Social analytics also uses sentiment analysis, because social media users often relay positive or negative sentiment in their posts. This provides important social information about users' emotions on specific topics.

These three patterns have several uses beyond pure analysis. For example, influence can be used to determine the most influential user in a particular network. Companies would be interested in this information in order to decide who they may hire for influencer marketing. These influencers are determined by recognition, activity generation, and novelty—three requirements that can be measured through the data mined from these sites. Analysts also value measures of homophily: the tendency of two similar individuals to become friends. Users have begun to rely on information of other users' opinions in order to understand diverse subject matter. These analyses can also help create recommendations for individuals in a tailored capacity. By measuring influence and homophily, online and offline companies are able to suggest specific products for individuals consumers, and groups of consumers. 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.

The above is copied from Social media mining

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Privacy Concerns Surrounding Data Mining

While widespread use of big data is encouraged and can help analysts understand social and health trends, there is a need for data to also have the proper privacy and security measures in place. "Informational privacy" is the term used by scholars to address the new privacy concerns that come from data mining  Informational privacy can fall under two categories: 1) the technology collects data about individuals without their knowledge or consent, and 2) individuals are aware of their data being collected, but are not aware of where the information goes. To mitigate these privacy risks, many professionals are researching methods and ways in which social media data mining can be controlled in order to protect the personal information of its users. A popular solution is PPDM, or privacy-preserving data mining. The goal of this revolutionary system is to utilize the data to create successful algorithms, while also protecting and hiding the data that is classified and personal.