With more than half of the world's population using one or more social media platforms every day, businesses in all industries have noticed the importance of social media data mining. Without further ado, let’s delve into the world of social media data mining to learn about it and its benefits.
Jun 22 2021 ● 10 min read
As its name implies, social media data mining refers to the process of mining social data. Unlike regular data mining, social media data mining explores beyond the internal databases and systems of a given company or research firm.
It typically involves the collection, processing, and analysis of raw data obtained from social media platforms such as Facebook, Instagram, Twitter, TikTok, LinkedIn, YouTube, and others, to uncover meaningful patterns and trends, draw conclusions, and provide insightful and actionable information.
Social media data mining harvests various types of social data that are either publicly available (e.g., age, gender, job profession, geographic location, etc.) or are generated on a daily basis on social media platforms (e.g., comments, likes, clicks, etc.).
Typically, the data represents people’s attitudes, connections, behavior, and feelings towards a certain topic, product, or service. Depending on the social media platform in question, this data may include the number of followers, comments, likes, or shares, if the targeted social media data comes from Facebook, Twitter’s retweets or the number of impressions, or Instagram’s engagement rates and hashtag usage.
When trying to optimize your social content, promote your online business, discover influential customers, or improve marketing and engagement strategies, you should always focus on gathering the above-mentioned types of data.
Generally, the process of mining social data involves a combination of statistical techniques, mathematics, and machine learning.
The first step is to gather and process social data from different social media sources. Apart from social media platforms such as Facebook, Twitter, or YouTube, data miners also extract data from various blogs, news sites, forums, or any other public pages where users interact and leave comments. All of this information must then be processed before proceeding to the next step.
Once data is collected and processed, what follows is the application of various data mining techniques which allow for easier identification of common patterns and the correlation of various data points in large datasets. Some of the more commonly used social media data mining techniques include classification, association, tracking patterns, predictive analytics, keyword extraction, sentiment analysis, and market/trend analysis.
Moreover, social media data mining also employs a number of social media data mining software solutions to optimize the process of mining. Some of the best-known data mining software solutions include the following: Microsoft SharePoint, Sisense, IBM Cognos, RapidMiner, and Dundas BI. Provided that a more in-depth examination of data is needed, data miners may decide to use machine learning in the process as well.
The final step in the mining process is to create a visual representation of the insights obtained from the whole process in order to deliver the information to the targeted audience. This is usually done by using social media analytics or a variety of data visualization tools, such as Infogram, ChartBlocks, Tableau, and Datawrapper, to name a few.
Due to the massive amounts of user-generated data that is being collected and analyzed through this process, social media data mining has found wide usage and is increasingly being recognized as an invaluable asset in many fields. Although it has primarily been used for business purposes, this process is nowadays often employed by researchers and by government agencies as well.
Companies, hotels, retailers, airlines, manufacturers, and even political groups buy data sets from data mining companies to help them personalize the customer's experience, improve marketing strategies and service satisfaction, and optimize their businesses, in general.
Here are some examples of who and how social media data mining is used:
• Some of its major uses in businesses include targeted marketing campaigns, market research, sales enablement, predictive analytics, influencer marketing, and monitoring of brand reputation.
• Trend analysis - Businesses use social media data mining to gain valuable insights into currently trending keywords, mentions, and topics on social media platforms.
• Event detection (social heat mapping) - This metric is of great importance for agencies and researchers who use social media monitoring.
• Social spam detection - Social media data mining allows for easier detection of spammers and bots on social media platforms like Instagram and Twitter.
• Ecommerce - Social media data mining is used to analyze how people talk about products.
• Digital media - Social media data mining is also applied to the field of digital media. For example, the content that is to be shown on a particular digital billboard may be decided upon through conducting a social media data mining process in order to cater to the audience’s preferences or needs.
• Bloggers and social media influencers - Social media data mining is often used by bloggers and social media influencers to help them analyze the attitudes and feelings of their followers, what they are talking about, and how they feel about certain topics of discussion.
• Brands - Social media data mining helps brands with important decision-making, for example, when deciding about potential future markets.
• Research purposes - Researchers find the use of social media data in their research a valuable asset to their work due to the magnitude and easy accessibility of the data. Social media data mining can be applied in different research domains, including social science, research, health research, and technology research. Some of its uses in the research field include gathering opinions, conducting research, recruiting study participants, undertaking participative ‘citizen science', or fostering stakeholder involvement.
• Government agencies - Social media data mining is also increasingly being used by government agencies for the purpose of welfare-focused interventions. One way social media data mining does this is by tracking residents’ moves as they document their activities at tagged locations throughout the day. Clearly, social media mining can be a powerful tool that can help improve residents’ lives and the safety of communities.
Being a relatively novel field of study, social media data mining has raised a number of challenges and issues regarding its use for different purposes. One of the major problems with its use is contained in the question of whether or not the practice of mining social data is ethical.
As mentioned above, social media data mining uses large amounts of user-generated data that is publicly available, which means that users have agreed to publicly share their personal data and are aware of the fact that anyone can view the things they’ve posted, commented on, or shared on social platforms.
However, there is still the issue of variable perceptions of what is considered ‘public’ and ‘private’, which, along with the issues of ownership and intellectual property, the difficulty of guaranteeing anonymity, or obtaining consent through informed choice, all pose even further challenges for data miners. Moreover, the lack of a clear ethical framework for working with social media data adds to the complexity of the whole process of gathering, analyzing, and visualizing user-generated data.
Over recent years, there have been several data privacy breaches involving the use of social media data mining due to unclear principles, laws, and rules regarding the use of this powerful tool. Large social media companies, including Facebook and Google, have been implicated in disputes over their improper use of social data, and news about Facebook’s Cambridge Analytica scandal in 2018 has been circulated worldwide.
To sum up, clear ethical principles and laws regarding the use of social data need to be established so that businesses, researchers, and government agencies can reap the benefits offered by social media data mining.
Published on Jun 22 2021
WRITTEN BY
Nikola GemesNikola is a content marketer at Whatagraph with extensive writing experience in SaaS and tech niches. With a background in content management apps and composable architectures, it's his job to educate readers about the latest developments in the world of marketing data, data warehousing, headless architectures, and federated content platforms.
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