The current investment in the research and analysis of big data is due in part to the creation of digital tools and technologies which can rapidly produce and collect larger stores of data than ever before. Some of the largest producers of big data within our society are the millions of social media users who regularly engage with mobile applications and web-based platforms. Through the constant engagement that users have with social media applications, advertisements, and content online, data science students and professionals have become more invested in how information and data can be captured from the cloud and used to generate new insights and findings.
Collecting and analyzing social media data comes with unique challenges and concerns when it comes to data privacy and security, as well as data storage and management. Social media data is usually produced through user-generated content and engagement, which can contain personal or sensitive information and data. Consequently, social media researchers and data scientists have to take extra precautions to protect the identity and information of users. For some, the ethical concerns around analyzing social media data (especially findings with the potential to make it easier for corporations and technologies to influence users) can also make this particular type of data too difficult to handle.
However, for those who are willing to take those precautions, working with social media can be useful to data science professionals across multiple fields and industries. Through analyzing social media data, you can discover and communicate key insights about consumer behavior, beliefs, and even identify future trends. Social media analysis has become a burgeoning subset of the larger field of data science, offering new opportunities for individuals with a background in programming, engineering, and even cultural and sociological study. The following article unpacks the growing role of social media in data science and some key tools that you should be using to engage more with this popular and fast-growing producer of data.
Using Social Media in the Data Science Industry
The interest and investment in social media analysis at this time is not only seen with the type of employment opportunities that are available in this cross-section but also the education and degrees that one can pursue. In addition to more general degrees in data and information science, data science students are now being encouraged to pursue further education in social media analysis, with degrees such as Master of Science in Digital Social Media and Masters in Social Data Science as an example of these specializations.
These degrees also represent the many ways that students and professionals can apply their knowledge in the data science industry. Through working with social media data in a way that allows you to examine human behavior and experience. The following list includes some of the data science industries where social media data and platforms are most commonly used:
- Business, Advertising, and Marketing- One of the primary ways that social media is used within data science is for consumer insights research. Through tracking how users engage with digital advertisements, corporate social media content, and search engine optimization (SEO), social media marketers and web developers can learn more about audiences, market niches, and how to increase traffic and engagement with a specific platform, website, or brand.
- Research and Network Analysis—Within academic settings such as nonprofit institutions, laboratories, education, and government, social media data can be used to gather information about changing demographics and trends. In particular, social media data offers significant insights about cultural and political beliefs, economic growth and decline, as well as societal norms and the relationships between groups and individuals.
- Science and Technology—Social media platforms rely on algorithms and recommendation systems to work. Data science professionals can apply their knowledge of automation and machine learning to many science and technology companies that are focused on engineering social media platforms.
- UX Design and Software Development—Collecting data on how users engage with social media can also be used to help better design the technologies that they rely on or encompass. Whether it is the need to update a mobile application or create a new platform or tool, user experience design and software development rely on the insights garnered from the analysis of social media data to improve upon or create new products.
Top 3 Data Science Tools for Social Media Analysis
Regardless of the industry that you are interested in, the data science tools listed below include some of the most popular programming languages and platforms used to collect and analyze social media. These tools are ranked from the order of most to least background experience and knowledge required to use them.
1. Python
As one of the most versatile programming languages available to data science students and professionals, Python is especially useful for collecting and analyzing social media data when you already know what it is that you want to analyze. Through web scraping and other forms of automation, Python can be used to create programs and code which allow you to search and analyze data from a social media platform and around a specific topic, time period, or even keywords and phrases. Python is commonly used within large technology companies that rely on recommendation systems and algorithms. If you already have some knowledge of how to use Python, or if you are experienced in programming, this is the go-to tool for social media data researchers, scientists, and engineers.
2. Google Analytics
Commonly used within the worlds of social media marketing and advertising, Google Analytics is one of the most widely used web analytics tools for search engine optimization and boosting web traffic. These tools can also be used for data science projects that require learning more about consumer behavior. Through user tracking, machine learning, and intelligence reports, data scientists or social media analysts can use this platform to collect information on how users engage with a brand, a business, or specific products, services, websites, and advertisements. Then, this information can be used to update business and marketing strategies to reflect the desired outcome of the client, company, or prospective consumer.
3. Sprout Social
Advertised as a platform for both social media strategists and data scientists, Sprout Social offers data science tools for working with Facebook, X (formerly known as Twitter), Instagram, and other social media sites. The platform’s focus on content and competition makes it simple to compare social media data across channels or companies. These insights can then be used to learn more about user behavior and business goals, whether that be tracking interactions with a brand or interest in a specific business or niche. Due to its ease of use, this platform is one of the best options for beginner data science students and professionals.
Bonus: Platform Specific Analytic Tools
In addition to these more well-rounded data science tools, each social media platform offers its own data science tools which are tailored to the unique technological affordances of the platform. For example, TalkWalker is a platform that was created to collect and analyze social media data generated from X. These tools are especially useful to data science students and professionals who are less familiar with analyzing social media data because these tools provide everything that you need to not only analyze social media data that you have collected but also to continue collecting data as it comes in.
Getting Started with Social Media Data
Now that you have some background on some of the more popular social media-focused data science tools, it is an excellent idea to learn more about collecting social media data. Tthere are several sites that can be used to find libraries of social media data to work on different forms of analysis and data science projects. One site that has curated large stores of social media data is Documenting the Now, which offers several tools and libraries for data science students and professionals who are interested in doing social media research and network analyses focused on examining trends in culture and society. In addition, sites such as GitHub offer many pre-constituted datasets for various social media sites that you can use to form different analyses or algorithmic reconfigurations such as network analysis, sentiment analysis, and machine learning.
Want to Learn More About Social Media and Data Science?
Social media is one of the fastest-growing data sources that we have, with social media users across platforms creating content every second of every day. With so much data being generated from social media data, a background in programming and machine learning is especially useful, and Noble Desktop’s Python Developer Certificate includes instruction on how to understand key aspects of social media data collection and analysis such as the role of algorithms and automation. For students and professionals who are interested in advertising and marketing, the Digital Marketing Certificate also offers background on Google Analytics and analyzing audience data to make predictions and better business strategies. If you require more general knowledge, Noble Desktop’s data science courses offer instruction in several data science tools which can be used to analyze social media platforms and data. In addition to these live online data science classes, you can also find in-person data science classes at a location near you.