User:Emkim123/sandbox

Data mining[edit]
Main article: Social media mining

Social media "mining" is a type of data mining, a technique of analyzing data to detect patterns. Social media mining is a process of representing, analyzing, and extracting actionable patterns from data collected from people's activities on social media. Social media mining introduces basic concepts and principal algorithms suitable for investigating massive social media data; it discusses theories and methodologies from different disciplines such as computer science, data mining, machine learning, social network analysis, network science, sociology, ethnography, statistics, optimization, and mathematics. It encompasses the tools to formally represent, measure, model, and mine meaningful patterns from large-scale social media data. Detecting patterns in social media use by data mining is of particular interest to advertisers, major corporations and brands, governments and political parties, among others

Techniques:
There are generally 4 steps that social media outlets use when data mining:


 * 1) Select a subject to study and use a tool to capture significant data
 * 2) Use a distinct evaluation system that clusters similar items in a study to represent the data set accurately and with precision
 * 3) Apply the information gathered from the evaluation system to update the metadata on the specific product
 * 4) Follow up with user's experience to refine the multimedia product

These four steps are the basis that social media outlets use when applying their data mining techniques but they are subject to change based on what the company is deems more important for the customer experience. Some social media outlets when doing data mining do not take in account the context of the individual's information. This can be problematic because sometimes the data they gather could not be accurate to what the consumer is interested in. To fix this problem some social media outlets have added capture time and Geotagging that helps provide information about the context of the data as well as making their data more accurate. In there are generally two types when data mining and that is supervised learning and unsupervised learning. Some of these data mining techniques include decision tree learning, Naive Bayes classifier, Bootstrap aggregating, and Boosting methods. Data mining social media outlets have better results when using techniques that focus more on the individual's behavior on social media rather than using a standardized model to find data.