Social media analytics or social media monitoring is the process of gathering and analyzing data from social networks such as Facebook, Instagram, LinkedIn, or Twitter. A part of social media analytics is called social media monitoring or social listening. It is commonly used by marketers to track online conversations about products and companies. One author defined it as "the art and science of extracting valuable hidden insights from vast amounts of semi-structured and unstructured social media data to enable informed and insightful decision-making."[1]
There are three main steps in analyzing social media: data identification, data analysis, and information interpretation. To maximize the value derived at every point during the process, analysts may define a question to be answered. The important questions for data analysis are: "Who? What? Where? When? Why? and How?" These questions help in determining the proper data sources to evaluate, which can affect the type of analysis that can be performed.[2]
Data identification is the process of identifying the subsets of available data to focus on for analysis. Raw data is useful once it is interpreted. After data has been analyzed, it can begin to convey a message. Any data that conveys a meaningful message becomes information. On a high level, unprocessed data takes the following forms to translate into exact message: noisy data; relevant and irrelevant data, filtered data; only relevant data, information; data that conveys a vague message, knowledge; data that conveys a precise message, wisdom; data that conveys exact message and reason behind it. To derive wisdom from an unprocessed data, we need to start processing it, refine the dataset by including data that we want to focus on, and organize data to identify information. In the context of social media analytics, data identification means "what" content is of interest. In addition to the text of content, we want to know: who wrote the text? Where was it found or on which social media venue did it appear? Are we interested in information from a specific locale? When did someone say something in social media?
Attributes of data that need to be considered are as follows:
Data analysis is the set of activities that assist in transforming raw data into insight, which in turn leads to a new base of knowledge and business value. In other words, data analysis is the phase that takes filtered data as input and transforms that into information of value to the analysts. Many different types of analysis can be performed with social media data, including analysis of posts, sentiment, sentiment drivers, geography, demographics, etc. The data analysis step begins once we know what problem we want to solve and know that we have sufficient data that is enough to generate a meaningful result. How can we know if we have enough evidence to warrant a conclusion? The answer to this question is: we don't know. We can't know this unless we start analyzing the data. While analyzing if we found the data isn't sufficient, reiterate the first phase and modify the question. If the data is believed to be sufficient for analysis, we need to build a data model.
Developing a data model is a process or method that we use to organize data elements and standardize how the individual data elements relate to each other. This step is important because we want to run a computer program over the data; we need a way to tell the computer which words or themes are important and if certain words relate to the topic we are exploring.
In the analysis of our data, it's handy to have several tools available at our disposal to gain a different perspective on discussions taking place around the topic. The aim here is to configure the tools to perform at peak for a particular task. For example, thinking about a word cloud, if we take a large amount of data around computer professionals, say the "IT architect", and built a word cloud, no doubt the largest word in the cloud would be "architect". This analysis is also about tool usage. Some tools may do a good job at determining sentiment, where as others may do a better job at breaking down text into a grammatical form that enables us to better understand the meaning and use of various words or phrases. In performing analytic analysis, it is difficult to enumerate each and every step to take on an analytical journey. It is very much an iterative approach as there is no prescribed way of doing things.
The taxonomy and the insight derived from that analysis are as follows:
The insights derived from analysis can be as varied as the original question that was posed in step one of analysis. At this stage, as the nontechnical business users are the receivers of the information, the form of presenting the data becomes important. How could the data make sense efficiently so it could be used in good decision making? Visualization (graphics) of the information is the answer to this question.[5]
The best visualizations are ones that expose something new about the underlying patterns and relationships contain the data. Exposure of the patterns and understating them play a key role in decision making process. Mainly there are three criteria to consider in visualizing data.
See also: information visualization.
| Common use-cases for social media analytics | Required business insight | Social media analytics techniques | Social media performance metrics | |
|---|---|---|---|---|
| Social media audience segmentation | Which segments to target for acquisition, growth or retention? Who are the advocates and influences for the brand or product? | Social network analysis | Active advocates, advocate influence | |
| Social media information discovery | What are the new or emerging business-relevant topics or themes? Are new communities of influence emerging? | Topic trends, sentiment ratio | ||
| Social media exposure & impact | What are the brand perceptions among constituents? How does brand compare against competitors? Which social media channels are being used for discussion? | Social network analysis, natural language processing | Conversation reach, velocity, share of voice, audience engagement | |
| Social media behavior inferences | What is the relationship between business-relevant topics and issues? What are the causes for expressed intent (buy, churn etc.)? | Natural language processing, clustering, data mining | Interests or preferences (theme), correlations, topic affinity matrices |
Recent research on social media analytics has emphasized the need to adopt a business intelligence-based approach to collecting, analyzing, and interpreting social media data.[7] [8] Social media presents a promising, albeit challenging, source of data for business intelligence. Customers voluntarily discuss products and companies, giving a real-time pulse of brand sentiment and adoption.[9] Social media is one of the most important tools for marketers in the rapidly evolving media landscape. Firms have created specialized positions to handle their social media marketing. These arguments are in line with the literature on social media marketing that suggests that social media activities are interrelated and influence each other.[10]
Moon and Iacobucci (2022)[11] focused on the marketing applications of social media analytics. Such applications include consumer behavior on social media, social media impact on firm performance, business strategy, product/brand management, social media network analysis, consumer privacy and data security on social media, and fictitious/biased content on social media. In particular, consumer privacy and data security are becoming more and more important in the social media universe given the increasing risk stemming from social media data breaches. In a similar vein, suspicious social media postings have significantly increased along with the growth of social media. Luca and Servas (2015)[12] reported that firms have a potential incentive to use fake postings when they have increased competition. Therefore, upgrading our ability to identify and monitor suspicious postings (e.g., fake reviews on Yelp) has become an important part of social media platform management.[13]
Muruganantham and Gandhi (2020) proposed a Multi-Criteria Decision Making (MCDM) model to prove that social media users' preferences, sentiments, behavior, and marketing data are related to social media analytics. Internet users are closely connected and show a high degree of mutual influence in social ideology and social networks, which in turn affects business intelligence.[14]
The possibilities of the dangers of social media analytics and social media mining in the political arena were revealed in the late 2010s. In particular, the involvement of the data mining company Cambridge Analytica in the 2016 United States presidential election and Brexit have been representative cases that show the arising dangers of linking social media mining and politics. This has raised the question of data privacy for individuals and the legal boundaries to be created for data science companies in relevance to politics in the future. Both of the examples listed below demonstrate a future in which big data can change the game of international politics. It is likely politics and technology will evolve together throughout the next century. In the cases with Cambridge Analytica, the effects of social media analytics have resonated throughout the globe through two major world powers, the United States and the U.K.
The scandal that followed the American presidential election of 2016 was one involving a three-way relationship between Cambridge Analytica, the Trump campaign, and Facebook. Cambridge Analytica acquired the data of over 87 million[15] unaware Facebook users and analyzed the data for the benefit of the Trump campaign. By creating thousands of data points on 230 million U.S. adults, the data mining company had the potential to analyze which individuals could be swayed into voting for the Trump campaign, and then send messages or advertisements to said targets and influence user mindset. Specific target voters could then be exposed to pro-Trump messages without being aware, even, of the political influence settling on them. Such a specific form of targeting in which select individuals are introduced to an above-average amount of campaign advertisement is referred to as "micro-targeting."[16] There remains great controversy in measuring the amount of influence this micro-targeting had in the 2016 elections. The impact of micro-targeting ads and social media data analytics on politics is unclear as of the late 2010s, as a newly arising field of technology.
While this was a breach of user privacy, data mining and targeted marketing undermined the public accountability to which social media entities are no longer subject, therefore twisting the democratic election system and allowing it to be dominated by platforms of “user-generated content [that] polarized the media’s message.”[17]
Analysis of Facebook political groups and postings by social media analytics firm, CounterAction, have shown the role of social media giants in protest movements such as attempts to overturn the 2020 United States presidential election and the 2021 United States Capitol attack.[18] [19]
During the 2016 Brexit referendum Cambridge Analytica attracted controversy for its use of data gathered from social media. A similar case took place in which a breach and Facebook data was acquired by Cambridge Analytica. There was concern that they had used the data to encourage British citizens to vote to leave the European Union in the 2016 EU referendum.[20] After a three-year investigation it was concluded in 2020 that there had been no involvement in the referendum.[21] Besides Cambridge Analytica, several other data companies such as AIQ[22] and the Cambridge University Psychometric Centre[23] were accused of, then investigated by the British government for their possible abuse of data to promote unlawful campaign techniques for Brexit.[24] [25] The referendum ended with 51.89% of voters supporting the withdrawal of the United Kingdom from the European Union. This final decision impacted politics within the United Kingdom, and sent ripples across political and economic institutions worldwide.[26]