A Recipe for Social Media Analysis

TL;DR

This paper introduces a high-level functional model for social media analysis that synthesizes data to provide actionable insights across various domains, addressing key challenges in the field.

Contribution

It presents a domain-independent SMA model that combines data synthesis and operational intelligence for improved social media analysis.

Findings

The model can be applied across multiple domains.

It addresses key challenges in social media analysis.

Provides actionable recommendations based on synthesized data.

Abstract

The Ubiquitous nature of smartphones has significantly increased the use of social media platforms, such as Facebook, Twitter, TikTok, and LinkedIn, etc., among the public, government, and businesses. Facebook generated ~70 billion USD in 2019 in advertisement revenues alone, a ~27% increase from the previous year. Social media has also played a strong role in outbreaks of social protests responsible for political changes in different countries. As we can see from the above examples, social media plays a big role in business intelligence and international politics. In this paper, we present and discuss a high-level functional intelligence model (recipe) of Social Media Analysis (SMA). This model synthesizes the input data and uses operational intelligence to provide actionable recommendations. In addition, it also matches the synthesized function of the experiences and learning gained from the environment. The SMA model presented is independent of the application domain, and can be applied to different domains, such as Education, Healthcare and Government, etc. Finally, we also present some of the challenges faced by SMA and how the SMA model presented in this paper solves them.