A social good is something that benefits the largest number of people in the largest possible way, such as clean air, clean water, healthcare, and literacy. Also known as “common good,” social good can trace its history to Ancient Greece philosophers and implies a positive impact on individuals or society in general. It also provides the basis for charity or philanthropic work.

KEY TAKEAWAYS

  • In recent times, social good is used to refer to corporate initiatives that aim to enhance the social contract of corporations by promoting practices that are better for the environment and overall society.
  • Corporations gain employee trust and loyalty by providing them with a sense of purpose and loyalty in their work.
  • Social media has become an important tool to promote social good.

The capitalism-based definition of business states that companies exist only to provide the maximum possible return to shareholders. This has often not run parallel to serving the common good in ways such as promoting clean air and water or financial independence for all citizens. As corporations focus more on corporate sustainability efforts and social responsibility in recognition of a de facto social contract with the public, their business models may expand to include more work to promote social good in their day-to-day strategies and operations.

Corporations keen to promote an image of themselves as socially conscious and responsible have created programs that seek to highlight their work toward social good. Aside from the positive feelings such programs generate, doing work that benefits the social good can give a company a sense of purpose and passion. That can help with productivity, innovation, and growth, as employees who believe in their company’s mission tend to invest more of their effort and passion into their work. Working toward a social good also has the effect of building bonds with the community. In helping a community or group of people, a company may hope that their effort is rewarded with sales.

Corporate investment in the social good can also help a company build and maintain its brand and its identity, as well as loyalty. A good example of this is the Newman’s Own brand, which discloses clearly on its label, “all profits to charity.” Those charities include ones related to ecology, conservation, and religious causes, among others.

Increasingly, social good has been connected with social media in that its definition has expanded to include a shareable deed or sentiment. Social media platforms are becoming a part of the social good in that they are an efficient way to educate the public, and advocate and fundraise for programs that support the social good. It also means that individuals, not just governments, corporations, or charities, can advocate for social good.

Aristotle described the common good as “proper to, and attainable only by, the community, yet individually shared by its members.”

As climate change becomes a mainstream issue, oil companies have increasingly come in for criticism due to their role in polluting the atmosphere. They have created separate divisions in order to promote their environmental image. For example, Total, France’s biggest petroleum major, allocated 4.3% of its budget to investing in renewable energy technologies in 2018. Equinor, Norway’s biggest energy company, plans to spend between 15–20% of its budget on renewable energy by 2030. British Petroleum has created a separate division to invest in renewable energy ventures.

Social media analytics is the process of gathering and analyzing data from social networks such as Facebook, Instagram, and Twitter. It is commonly used by marketers to track online conversations about products and companies. Social media analytics also defined 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.” 

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.

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:

  • Structure: Structured data is a data that has been organized into a formatted repository – typically a database – so that its elements can be made addressable for more effective processing and analysis. The unstructured data, unlike structured data, is the least formatted data.[3]
  • Language: Language becomes significant if we want to know the sentiment of a post rather than number of mentions.
  • Region: It is important to ensure that the data included in the analysis is only from that region of the world where the analysis is focused on. For example, if the goal is to identify the clean water problems in India, we would want to make sure that the data collected is from India only.
  • Type of Content: The content of data could be Text (written text that is easy to read and understand if you know the language), Photos (drawings, simple sketches, or photographs), Audio (audio recordings of books, articles, talks, or discussions), or Videos (recording, live streams).
  • Venue: Social media content is getting generated in a variety of venues such as news sites and social networking sites (e.g. Facebook, Twitter). Depending on the type of project the data is collected for, the venue becomes very significant.
  • Time: It is important to collect data posted in the time frame that is being analyzed.
  • Ownership of Data: Is the data private or publicly available? Is there any copyright in the data? These are the important questions to be addressed before collecting data.

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 could 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 analysis will result in :

  • Depth of Analysis: Simple descriptive statistics based on streaming data, ad hoc analysis on accumulated data or deep analysis performed on accumulated data. This analysis dimension is really driven by the amount of time available to come up with the results of a project. This can be considered as a broad continuum, where the analysis time ranges from few hours at one end to several months at the other end. This analysis can answer following type of questions:
  • How many people mentioned Wikipedia in their tweets?
  • Which politician had the highest number of likes during the debate?
  • Which competitor is gathering the most mentions in the context of social business?
  • Machine Capacity: The amount of CPU needed to process data sets in a reasonable time period. Capacity numbers need to address not only the CPU needs but also the network capacity needed to retrieve data. This analysis could be performed as real-time, near real-time, ad hoc exploration and deep analysis. Real-time analysis in social media is an important tool when trying to understand the public’s perception of a certain topic as it unfolding to allow for reaction or an immediate change in course. In near real-time analysis, we assume that data is ingested into the tool at a rate that is less than real-time. Ad hoc analysis is a process designed to answer a single specific question. The product of ad hoc analysis is typically a report or data summary. A deep analysis implies an analysis that spans a long time and involves a large amount of data, which typically translates into a high CPU requirement.
  • Domain of Analysis: The domain of the analysis is broadly classified into external social media and internal social media. Most of the time when people use the term social media, they mean external social media. This includes content generated from popular social media sites such as Twitter, Facebook and LinkedIn. Internal social media includes enterprise social network, which is a private social network used to assist communication within business.[5]
  • Velocity of Data: The velocity of data in social media can be divided into two categories: data at rest and data in motion. Dimensions of velocity of data in motion can answer questions such as: How the sentiment of the general population is changing about the players during the course of match? Is the crowd conveying positive sentiment about the player who is actually losing the game? In these cases, the analysis is done as arrives. In this analysis, the amount of detail produced is directly correlated to the complexity of the analytical tool or system. A highly complex tool produces more amounts of details. The second type of analysis in the context of velocity is an analysis of data at rest. This analysis is performed once the data is fully collected. Performing this analysis can provide insights such as: which of your company’s products has the most mentions as compared to others? What is the relative sentiment around your products as compared to a competitor’s product?

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.

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.

  • Understand the audience: before building the visualization, set up a goal, which is to convey great quantities of information in a format that is easily assimilated by the consumer of information. It is important to answer “Who is the audience?”, and “Can you assume the audience has the knowledge of terminologies used?” An audience of experts will have different expectations than a general audience; therefore, the expectations have to be considered.
  • Set up a clear framework: the analyst needs to ensure that the visualization is syntactically and semantically correct. For example, when using an icon, the element should bear resemblance to the thing it represents, with size, color, and position all communicating meaning to the viewer.
  • Tell a story: analytical information is complex and difficult to assimilate, thus, the goal of visualization is to understand and make sense of the information. Storytelling helps the viewer gain insight from the data. Visualization should package information into a structure that is presented as a narrative and easily remembered. This is important in many scenarios when the analyst is not the same person as a decision-maker.

Sentiment analysis (also known as opinion mining or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine.

Social network analysis (SNA) is the process of investigating social structures through the use of networks and graph theory. It characterizes networked structures in terms of nodes (individual actors, people, or things within the network) and the ties, edges, or links (relationships or interactions) that connect them. Examples of social structures commonly visualized through social network analysis include social media networks, memes spread, information circulation, friendship and acquaintance networks, business networks, knowledge networks, difficult working relationships, social networks, collaboration graphs, kinship, disease transmission, and sexual relationships. These networks are often visualized through sociograms in which nodes are represented as points and ties are represented as lines. These visualizations provide a means of qualitatively assessing networks by varying the visual representation of their nodes and edges to reflect attributes of interest.

Social network analysis has emerged as a key technique in modern sociology. It has also gained a significant following in anthropology, biology, demography, communication studies, economics, geography, history, information science, organizational studies, political science, public health, social psychology, development studies, sociolinguistics, and computer science and is now commonly available as a consumer tool (see the list of SNA software).

Social network analysis is also used in intelligence, counter-intelligence and law enforcement activities. This technique allows the analysts to map covert organizations such as a espionage ring, an organized crime family or a street gang. The National Security Agency (NSA) uses its clandestine mass electronic surveillance programs to generate the data needed to perform this type of analysis on terrorist cells and other networks deemed relevant to national security. The NSA looks up to three nodes deep during this network analysis. After the initial mapping of the social network is complete, analysis is performed to determine the structure of the network and determine, for example, the leaders within the network.[57] This allows military or law enforcement assets to launch capture-or-kill decapitation attacks on the high-value targets in leadership positions to disrupt the functioning of the network. The NSA has been performing social network analysis on specific occasions.