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Social and Economic Impact of Social Media and Framework for the Prediction of Economic Trends

Introduction

During the last decade, the world has witnessed yet another technological revolution i.e. social media revolution, following the dot com boom at the end of last century. The emergence of cheaper and faster connectivity options, and cheaper mobile devices have enabled the widespread use of social media platforms both in developed and developing economies. This has hugely transformed social interactions and economic activity throughout the world. Digital innovation and social networking are now shaping business and governance practices. New business models are emerging, customer-supplier relationships are building, and access to local and global markets is getting easier and faster than ever. Internet and resultant digital and social innovation is playing an increasingly dominant role in driving economic growth, and have contributed more than 20% towards the overall GDP growth of world’s bigger economies .

There are 2.307 billion active social media users out of which 85% are mobile social user, with a growth rate of 17% during last year. Social media platforms such as Facebook, Twitter and LinkedIn are increasingly being used by people to share their interests, talk about difference aspects of their daily lives, and voice opinions. The rich contents of this generated data are a valuable source for businesses and organisations to get insight into people’s daily lives to identify their preferences and emerging trends. Businesses and organisations are already using this data to market and improve their products and services through targeted advertisement and customer feedback.

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The increased awareness brought out by social media about local and global events is shaping public opinion and influencing decisions. Moreover, the global political, social and economic landscape is changing rapidly, and in some cases unexpectedly. Therefore, it is important for businesses, investors and governments to understand public response to such events and react in timely manner. One recent example of such an event is the UK’s vote to leave the European Union, which invited huge response from general public as well as investors rethinking their buying or investment decisions, and social media was largely used to share their concerns and opinions. Hence the monitoring and analysis of such events and their responses are vital for business to adapt their strategies, and governments or policy makers to take appropriate measures. This can also help identify market gaps and investment opportunities. The traditional ways of carrying out such analysis are time-consuming and generally rely on surveys whereas data from social media provides more real-time information.

Problem Statement and Objectives

Currently, the monitoring and analysis of social media contents by businesses is mainly focused on advertising their products and services, and building customer relations. The potential of such content in the detection or identification of economic trends is largely untapped. The use of social media content in analysing public or investor’s sentiment and reaction to local or global events, their impact on economic growth and prediction of economic trends has still not been studied well.

Our research will focus on the use of textual data from social media to study different factors or events driving public or investor’s decision, resultant trends and their impact on economic growth. However, just the plain analysis of textual data or words may not provide sufficient insight into how people factually feel about a particular event, so it is important to also consider the context of words and associated sentiment.

It is also worthwhile to mention that in certain cases, the predicted trend may not be entirely reliable and reflective of ground data, as people are not always going to act the way they say they will. Therefore, in order to improve the accuracy of analysis, it is also important to validate it by taking into consideration the ground data e.g. any upward or downward trends.

Keeping in view the above discussion, we set out the following objectives for our research:

Analyse factors or events influencing opinions and decisions by different sectors including general public, businesses and investors.
Identify the trends in economic activity by studying the factors and their response.

Study the impact of certain events and their responses on the economic growth.

Capture the findings of above three in a model to be used as the basis of a conceptual framework.

Propose a framework or tool to identify and predict economic trends through social media content based on our model.

Related Work

SocialIntell provides social media monitoring services for different sectors but the monitoring and analysis task is manually performed by human analysts, which is cumbersome and time-consuming. Bank of England has included social media monitoring as part of their advanced analytics to identify economic trends to help in policymaking, specifically to study interest rates. They are using a combination of tools and softwares for this purpose instead of a single framework, however we do not know the effectiveness of their model or analysis.

Social media text and sentiment analysis has also been used in disaster and emergency management studies to detect emergency events, track people’s responses to emergency well as detecting rumours and validating the authenticity of the emergency information.

Methodology

First of all we will choose appropriate social media platforms from where we will collect data for our study. Secondly, we will select a number of events from the past on which we will perform an initial case study. For this purpose, we will use some simple data mining tools for text analysis to see what opinions were shared on social media during those events, what trend or economic predictions could be made from that data and how representative the results were of the actual impact. Based on this, we will also weigh different factors and see which are more important than others. The findings and factors identified in the study will be captured into a prediction model. The evaluation of our model will then be done on another set of events to validate its effectiveness and accuracy.

On the basis of our prediction model, we will propose the concept of building a framework with an interface with social media platforms and implementation of all the phases from textual analysis to identifying trends and generating predictions.

Beneficiaries

Beneficiaries of this research may include businesses, financial sector, policy makers, invertors and/or entrepreneurs following market trends and looking to identify potential opportunities or risks.

Dissemination and Exploitation

The findings of our study shall be disseminated through publications in relevant conferences and journals.