Data Science and its use in Social Media

Data Science belongs to the field of Statistics and Computer Science. It is the process of discovering patterns in great amounts of data.

We can explore and evaluate large databases automatically or semi-automatically with the objective of finding repetition models, tendencies and rules that explain the behaviour of that information.


What type of data can be gathered from Social Media?

Social Media Data Science can gather various types of social data that are available publicly such as: age, gender, occupation, geographical location, etc, or that are generated daily in social media platforms such as comments, likes, clicks, etc.

Data can also represent attitudes, connections, behaviours and feelings of people as regards a topic, product or service. Depending on the social media platform, data can come from retweets, facebook impressions, hashtag use on Twitter or Instagram, etc. This data can include followers count, comments and likes among others.


How does AI and Data Science help on Social Media analysis?

The exploration capacity in large databases with Natural Language Processing techniques facilitates the work of communicators, as it is possible to extract and classify automatically what is really important in social media.


There are two analysis models in Data Science:

– Predictive

They estimate the future value of an «objective variable» (For example: the sales volume of a new product) from historical data and other known variables in this case, for example, the sales historical data of similar products and the detected demand)

Within this model is data mining based on a classification (For example: if client X will repeat or not his purchase) and data mining based on regression (How long will it take to reach the Y objective)

Read about Predictive Models


They detect patterns in data that organise the information in new ways and facilitate analysis, but they do not predict future behaviour. For example, the identification of unsatisfied necessities in our target audience.

Within descriptive data mining, one can find clustering (classifying users in groups according to their characteristics), association rules (in order to establish relations among different variables) and sequencing. 

Read about Descriptive Models


Practical implications

Thematic post categorization allows companies to identify which post topics are the most interesting for their audience. This allows the company to design the most appropriate strategy to attract the attention of its customers.

Let’s take a company from the financial industry as an example. After using AI to analyse social media posts of relevant users of your audience, one could determine that posts related to real estate are one of the most shared. This information would help the company to promote the marketing of mortgage products or offer information on legal aspects regarding this area.

Now let’s take an example of a company from the energy industry. After examining the posts of its competitors on Twitter, the company could determine that the posts related to sustainability and environment issues receive a high number of retweets. This could help the company reorient its image, giving greater emphasis to issues related to corporate social responsibility.


The application of automated algorithms for the thematic categorization of publications can provide the company with valuable information for them to decide which strategy is the most appropriate to reach its target audience.

To conclude, Data Science and AI in general can only be seen as allies in terms of business management.


Did you know about the use of Data Science in Social Media?

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