Abstract
Risk and uncertainty are disliked but inevitable. The nature of these has changed and new sources of risk have risen. To mitigate risk and maintain financial stability, the firms need to adapt. The world wide web and, within it, social media have had tremendous growth and wide coverage lately, making them determining forces in any economic activity. This has led to generation of large amount of data on myriad concerns. Recent developments in computing technology has thrown open the possibility of mining useful information from the enormous and dynamic data. The chapter outlines the growth of social media and social media analytics and its financial implications to businesses, consumers, and governments. It details how risk management and social media, two domains earlier considered more diverged than chalk and cheese are now inextricably linked and explains using various cases how social media analytics is used to manage risk and uncertainty. The authors also look at the emerging challenges with these developments.
TopIntroduction
The Chinese curse, “May you live in interesting times,” has never been truer than today. With ‘Disruptive Innovation’ being the new buzzword, there is rapid change all around. This period of Information Revolution has virtually connected people across the globe. An increasingly large amount of time is spent online today. With the increase in mobile devices and improved access to internet, people today consume most of their information online. This has further created many online businesses as well as driven traditional businesses electronically. More recently, social media has gathered considerable forces online. Young (2017) finds that one in every three minutes spent online is devoted to social media. Enormous amount of data and content is generated through channels of social media like Twitter, LinkedIn, Facebook and Instagram, to name a few, and this data is continually updating in real time. Evidently social media is a vital medium to reach large online audience in their day to day lives with ease. The increasing economic activities and improved means of information transmission have brought in new kinds of uncertainty and risks too. The gargantuan information available can itself be tapped to mitigate these new risks. With such significance of social media in the current scenario, how important is its role to aid risk mitigation decisions is worth researching.
Financial stability can comprise different things for different economic entities — the firm, the government and the household. But across these divides, financial stability can be understood as the resistance to economic shocks and not losing the ability to fulfil its basic functions. Core risks can be classified into four types, namely: Market Risk, Credit Risk, Liquidity Risk and Operational Risk. In layman terms, market risk arises when loss is associated to the factors that affect the market, such as stock prices, foreign exchange rates, etc. (Arshad, Zafar, Fatima, & Khan, 2015; Hull, 2018). Credit risk arises when an entity fails to fulfill its commitments towards its counter parties. Liquidity risk arises when an investment cannot be traded instantaneously to counter or minimize a loss. Operational risk arises when loss is a product of operational failures, which can be external or internal in nature that can include technical failures, frauds because of failed internal processes, and other such events. With the digital socialism in trend, it is imperative to understand these risks in the context of social media.
Thus, there is a need for firms to adapt to maintain financial stability and counter those risks. The present chapter studies how such adaption is worked upon by firms in the present period and how they would for the changing times ahead. The rest of the chapter is organized as follows. The next section looks at the rise of social media, making it an obligatory part of business, and its implications for business. The third section describes the various aspects of social media analytics — both content and structure-based analytics — and looks at the latest inroads into the realm of big data. This is followed by looking at ways in which social media and social media analytics are used for financial stability as well as solutions and recommendations. Lastly, the authors highlight future concerns and challenges for social media analysis in times to come, indicate directions for future research and conclude.
Key Terms in this Chapter
Big Data: This is data characterized by its large volume, wide variety, and high velocity. Volume refers to high quantity of data usually running into terabytes. The data is not only numbers or text, but can include images, videos, etc., making it very varied and the data is generated rapidly which is called its high velocity.
Web 2.0: As opposed to the traditional world wide web (retroactively called Web 1.0), Web 2.0 has a lot of inputs generated by users. These are in the form of forums, microblogging, social networking and wikis (a server program that allows users to collaborate in forming the content of a website [e.g., Wikipedia]). An information architecture consultant Darcy DiNucci coined the term Web 2.0 in 1999, but it was popularized by Tim O’Reilly, founder of O’Reilly Media.
Uncertainty: When the outcome of an event is uncertain and one doesn’t know all possible outcomes and/or their probabilities, then outcome is said to be uncertain.
Occupational Fraud: Occupational fraud is the use of one’s occupation for personal enrichment through the deliberate misuse or misapplication of the organization’s resources or assets. It could include any of payment fraud, procurement fraud, and travel and subsistence fraud, personnel management, exploiting assets or information and receipt fraud.
Content-Based Analytics: In social media analysis, content-based analytics involves the analysis on the contents posted by users on social media. This includes text, images, and videos.
Text Analysis: This is the process of creating high-quality structured data for analysis from unstructured and heterogeneous textual data. The structured data is then analyzed to derive usable conclusions. In social media analysis, the raw text could be in the form of tweets, Facebook posts, comments on social media, hashtags, and blog posts.
Risk: When the outcome of an event is uncertain, but one is aware of the probabilities of each outcome, the outcome is said to possess risk.
Social Media Analytics: It is the process of gathering, structuring, analyzing and gaining actionable insights from data available on social media. This is data generated from the conversations of stakeholders on social media.
Structure-Based Analytics: In social media analysis, structure based analytics or social network analytics is concerned with evaluating the structural attributes of a social network and extracting intelligence from the relationships among the participating entities. The structures are modelled visually with nodes, and edges connecting the nodes.