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Data Science and Social Media: An Intersection
Data Science and Social Media: An Intersection

October 4, 2022

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In recent years, social media usage has increased dramatically, but never more so than since the COVID-19 outbreak, when lockdowns and other restrictions changed user behavior and habits to become more dependent on technology. Social media usage increased by 10.4% in July 2022 compared to the previous year, and in March 2020, TikTok attracted 12 million unique U.S. visitors. In addition, 46% of women and 42% of men say the pandemic has caused them to spend more time on social media.

While social media and data science have undoubtedly interacted in significant ways, the pandemic has permanently changed the role and significance of analytics in this field. Social media platforms are now the main focus of contemporary business marketing strategies and a playground for real-time trend analysis.

In the upcoming years, social media's trajectory in business development will grow, resulting in related career roles. Web analysts, AI engineers, machine learning engineers, digital marketing experts, social media analysts, and advanced mobile marketers are just a few of the jobs in this field that are already in high demand, so this is the best time for both working professionals and those looking to break into the field to study how data science and social media interactions.

Increase in Data Sources

A growing body of structured and unstructured data in various formats, including images, videos, sounds, text, and geolocations, has accumulated due to the remarkable increase in social media usage.

As time has passed, social media has developed into a vital tool for gathering and disseminating information in various fields, including journalism, business, politics, and science. This expansion creates new opportunities for pattern discovery and analysis that can shed light on important issues, influences, and market and social changes.

The specific application domain, data source and format, techniques, and objectives often determine the complexity of data discovery, collection, and preparation for analytics and predictive modeling. This involves extensive data science techniques like big data analytics, data mining, machine learning, and AI.

Expanding the use cases

Numerous use cases have repeatedly shown the relationship between data science and social media, influencing business strategies, scientific and medical research, product development, and marketing optimization.

  • Making Better Decisions

Big data analysis is used by businesses to identify trends in real-time, use these competitive insights to guide decision-making, reduce business risks, and link social data to their bottom line.

  • Scientific and medical research

Social media is a crucial tool in the healthcare industry for reducing misinformation, providing the public with real-time updates, and increasing awareness of health-related issues. Data science is used by public healthcare organizations, academic researchers, and scientists to benefit from the accessibility and availability of data for learning, collaboration, and research recruitment.

  • Product Creation

Businesses use ML-driven social listening techniques for intelligent analysis of videos, photos, and content in natural language. This enables them to capture emerging trends or rapidly shift preferences to create the most suitable products for the market.

  • Enhanced Marketing Strategy

In order to support marketing strategies, social media analytics and big data analytics are used. These tools are used to create contextualized, personalized ads and content based on customer sentiment, track the effectiveness of campaigns, and measure marketing performance KPIs.

  • Observing the Effects of COVID-19

In order to better inform the public about the facts and dangers of COVID-19, data science techniques are currently being used to track the pandemic's global impact and momentum by examining social media.

Conclusion

Social media has fundamentally changed how traditional forms of communication are used, opening up new opportunities for data science professionals to direct and support everything from scholarly research and health education to digital marketing and service development.

 


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