The Intersection of Data Science and Social Media

Social media usage has skyrocketed in recent years, but never more so than it has since the COVID-19 outbreak, as lockdowns restrictions shifted user behavior and habits towards more digital dependency. In July 2020, there was a 10.4 percent growth in social media use compared to the previous year, and TikTok gained 12 million individual U.S. visitors in March 2020. Furthermore, 46 percent of women and 42 percent of men claim that their time on social media has increased as a result of the pandemic.

While data science has certainly played a significant role in social media, and vice versa, the pandemic has forever altered the impact and importance of analytics in this space. Social media platforms have become a playground for real-time trend analysis and the focal point for modern business marketing strategies.

The trajectory of social media’s role in business development is going to increase in the coming years, and corresponding career roles will emerge as a result. We’re already seeing the popularity of job skills in this field, including web analysts, AI Engineer, machine learning engineers, digital marketing specialists, social media analysts, and advanced mobile marketers, and now is the best possible time for both practicing and aspiring industry professionals to be exploring the collision between data science and social media. 

Acceleration of Data Sources 

The remarkable rise in social media usage has ushered in a growing accumulation of both structured and unstructured data from various formats, such as pictures, videos, sounds, text, and geolocations. 

Social media has progressed to become a critical driver for collecting and spreading information across sectors like entertainment and media, journalism, business, politics, and science. This growth opens up new potential for analysis and pattern discovery that provide insights into key challenges, trends, influences, and market and social changes.  

The complexity of performing data discovery, collection, and preparation for analytics and predictive modeling often depends on the particular application domain, data source and format, methods, and goals. Data science techniques such as big data analytics, data mining, machine learning, and AI are widely used to this end. 

For example, a marketing firm looking to identify patterns in customer behavior to forecast future purchasing habits would need to utilize predictive analytics and modeling, including cluster models, regression analysis, and cluster filtering, all of which are rooted in statistical and ML algorithms.

Similarly, social media platforms like Facebook, Instagram, and LinkedIn use ML, AI, and Natural Language Processing (NLP) to personalize user experiences, parse massive volumes of data, recognize people in pictures, and guide content development. 

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Broadening Use Cases 

The correlation between data science and social media has been repeatedly demonstrated across numerous use cases, shaping business strategies, scientific and medical research, product development, and marketing optimization.

Enhancing Decision Making

Organizations analyze big data to uncover trends in real-time and use these competitive insights to inform their decision making, minimize business risks, and connect that social data to their bottom line. 

Medical and Scientific Research

Social media is a vital healthcare resource to mitigate misinformation, offer real-time updates to the public, and raise awareness about health issues.  Public healthcare organizations, academic researchers, and scientists use data science to take advantage of the availability and accessibility of data for educational, collaboration, and research recruitment purposes. 

Product Development

Companies use ML driven social listening techniques for smart analysis of natural language content, along with videos and photos. This helps them get a hold of early trends or rapidly changing preferences so they can develop products that best fit the market. 

Marketing Strategy Improvement

Social media analytics and big data analytics are used to support marketing strategies, including measuring marketing performance KPIs, monitoring the effectiveness of campaigns, and creating contextualized, personalized ads and content based on customer sentiment. 

Monitoring COVID-19’s Impact

Data science techniques are currently used to monitor the worldwide impact and momentum of the pandemic by analyzing social media, and to help inform the public about COVID-19 facts and risks. 

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Disruption Shapes Career Opportunities

Traditional forms of communication have been fundamentally disrupted by social media, presenting emerging opportunities for data science practitioners to guide and support everything from academic research and health awareness to digital marketing and service development.  

For those who want to broaden their skill horizons in data science and social media, Simplilearn offers specific educational courses in Data Science and Business Analytics to guide both existing and potential career paths. 

About the Author

Ronald Van LoonRonald Van Loon

Named by Onalytica as the world's #1 influencer in Data and Analytics, Automation, and the Future Economy (Tech), Ronald is the CEO of Intelligent World and one of the top thought leaders in Data Science and Digital Transformation.

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