Ethics of Data Mining

Data mining, at its core, is simply the process of obtaining large data sets and utilizing various analytical tools to investigate and interpret these data sets. In doing this, data mining is used to discover patterns within these large sets in order to develop and test models based on them. As technological advancements continue to develop, skills, such as data mining, have become more and more prevalent. In fact, many ordinary individuals often interact with data mining tools everyday whether it is through their search history or social media. Through data mining, companies can gain access to intimate user data and use it to generate consumer profiles and intelligence.

Types of Data Mining

Predictive Data Mining

As suggested by the name, predictive data mining is data mining done with the purpose of predicting or forecasting trends. When companies buy or gain access to user data, they can sort through this data and use forecasting tools to create theories based on the obtained user information. This by and large makes heavy use of a businesses' analytical software as results from this type of data mining can often prove to be inaccurate or misleading. The most typical use of predictive data mining is through trend forecasting. A company may sort through a customer's purchase or search history to create targeted advertisements based on this data. In this way, such kind of data mining is often referred to as proactive.

Descriptive Data Mining

On the other hand, descriptive data mining takes a more reactive approach. Instead, of making future predictions based on customer data, descriptive data mining relies on using concrete analysis to identify correlational relationships based off of already existing data. Therefore, the data and results provided in this type of data mining is concrete and precise. Businesses gain insight from past data and use this to learn and establish underlying patterns. For example, a company might keep track of user data on their website to see which part of their site receives the most interaction compared to which parts of the site receive the least amount of interaction. From this, a business might be able to identify touch-points on their website connected to certain user behavior. Businesses often use a combination of both of these data mining techniques to best serve their interests.

Relationship Between Data Mining and Social Media

Nature of Relationship

Though there are many ways miners can collect data, social media sites are amongst the largest sources of user information and data mining. Through the nature of social media, many platforms such as Twitter, Instagram, Meta(formerly known as Facebook), and TikTok have access to the personal data of users including gender, ethnicity, age, and even location. Additionally, through each platform's own algorithms, they can track what posts users are liking, how much time they spend on each site, and engagement with other users and ads. Through this, social media platforms have a plethora of information on their users that they can sell to other companies looking to get more data on their potential customer profiles. This is often used in respect to company marketing efforts. Social media companies tend to use the aforementioned user data in order to generate customer profiles based on both demographic and behavioral factors. This is useful for other companies in getting advertisements for their products specifically targeted to those who are most likely to purchase the product. As such, social media platforms present themselves as a lucrative space to mine data.