25 years ago, internet merely existed in daily life, yet Gen Z, especially 2000s babies, could never imagine life without internet nowadays. As a part of cyberspace, social media has predominantly grown in the past few years, and becomes an everyday routine of a lot of people. Thus, social media plays an important role that unveils many potentials for communication, and especially business strategies across industries. However, huge data sets from social media are unstructured that are hard to work with. As a result, we can handle these unstructured data sets by social media listening, or social media monitoring, a process of identifying and assessing information said on social media such as rising trends, opinions on brands and products,…
There are several ways to approach social media monitoring. One of the techniques is data mining, a topic covered in MIS 2502. Predictive analytics is a process of exploration and analysis of large data sets. To achieve this goal, we can use Rstudio to extract information from social media platforms like Facebook, resulting in discovery of meaningful patterns. There are many useful packages in Rstudio to do data mining. With Facebook, Tavish Deeptendu Bikash Dhar downloaded data of credit card pages from The Facebook Graph API as a token and connected to Rstudio to get page information. He obtained number of posts from Visa, MasterCard and Amex Singapore pages, as well as engagement in terms of the total number of likes, comments and shares. At the end of his analysis, Dhar concluded that offers related to travel and contests with prizes would bring the maximum engagement. This finding can help credit card companies design better social media campaigns in the future!
Briefly, utilizing power tools like RStudio to bring useful analyses can help business get a competitive edge in this era of social media.
Dhar, D. B. (2015, September 20). Retrieved April 25, 2017, from https://rstudio-pubs static.s3.amazonaws.com/110580_9dba6d6ac8b1432d96e973320f4506d4.html