Social media analytics is a six step iterative process of mining the desired business insights from social media data (Khan, 2015). At the center of the analytics is the organizational goals and objectives that we want to achieve with social media analytics.
Step 1: Data Source Identification
Data source identification stage is concerned with searching and identifying the right source of information for analytical purposes. Although, most of the data for analytics will come from business-owned social media platforms, such as an official Twitter account, Facebook fan pages, blogs, and YouTube channel. Some data for analytics, however, will also be harvested from nonofficial social media platforms, such as Google search engine trends data or Twitter search stream data.
Step 2: Data Extraction
Once a reliable and mineable source of data are identified, next comes extraction of the data. Most of the large-scale social media data extraction is done through an API (application programming interface). Mostly, the social media analytics tools use API-based data mining. APIs, in simple words, are sets of routines/protocols that social media service companies (e.g., Twitter and Facebook) have developed that allow users to access small portions of data hosted in their databases.
Step 3: Cleaning
Next comes removing the unwanted data from the automatically extracted data. Some data may need cleaning, while other data can go into analysis directly. In the case of the text analytics cleaning, coding, clustering, and filtering may be needed to get rid of irrelevant textual data using natural language processing (NPL).
Step 4: Analyzing the Data
At this stage, the clean data is analyzed for business insights. Depending on the layer of social media analytics under consideration and the tools and algorithm employed, the steps and approach to take will greatly vary. For example, nodes in a social media network can be clustered and visualized in a variety of ways depending on the algorithm employed. The overall objective at this stage is to extract meaningful insights without the data losing its integrity.
Step 5: Visualization
In addition to numerical results, most of the social media data will also result in visual outcomes. Effective visualization is particularly helpful with complex and large data sets because it can reveal hidden patterns, relationships, and trends. It is the effective visualization of the results that will demonstrate the value of social media data to top management.
Step 6: Consumption
While companies are quickly mastering sophisticated analytical methods, skills, and techniques needed to convert big data into information, there seems to a gap between an organization’s capacity to produce analytical results and its ability to effectively consuming it. Effective Consumption of analytics results relies on human judgments to interpret valuable knowledge from the visual data. Meaningful interpretation is of particular importance when we are dealing with descriptive analytics that leaves room for different interpretations.
1. Khan G. F., 2015, Seven layers of social media analytics: Mining business insights from social media text, actions, networks, hyperlinks, apps, search engine, and location data, CreateSpace Independent Publishing Platform.
Dr. Khan, Gohar is a Senior Lecturer at the University of Waikato, New Zealand. He is the Founding Director of the Center for Social Technologies, which investigates strategic, organizational, behavioral, legal, and economic aspects of social technologies. His work on social media and information technology has appeared in several refereed journals, conference proceedings, and books.