Social Media Screening Platform

  • Social media
  • USA
  • 2018 - till now

Social Media Screening Platform

About the client

The project is a web-based AI-powered platform for comprehensive social media background screening. Its supertask is to streamline potential employee background checks for companies, tackling employment risk management.

Value proposition

and key technology:

The product’s fundamental value proposition lies in providing trained AI which would do the same background check work as a human, only in an hour or two instead of a week. From employment screening to government scans, the solution helps businesses and organizations detect offensive online behavior and build safe workplace environments.

The main asset in question is the unique NLP AI that analyzes all of the subject’s given social media platforms (Facebook, Instagram, YouTube, Twitter (X), LinkedIn and TikTok). The system can identify potential flags and sentiments (positive/negative sentiments, politics, hate speech, prejudice, self-harm, drugs, threats, nudity, etc.), analyzing thousands of posts and flagging content with 14 risk factors.


In this project, we were asked to develop a machine learning system for collecting, analyzing, and reporting data from social media like FB, Instagram, Twitter, and others under 14 criteria.

The main challenge our client faced at the dawn of development concerned the main solution’s functionality, namely the feature of analyzing the candidate’s profile

  • by a third-party program (NLP AI in this case) because of most social media privacy policy, which is only allowing to access that information for the logged-in user’s page;
  • in bulk, meaning the possibility to analyze several profiles at once with the speed modern technologies can offer.

To put it more technically, our primary task was to develop a social media scrapping solution that would not just be able to access the logged-in user’s personal information but also analyze other people’s content without any obstacles.

Solution Overview:


We started by developing a PoC: while we didn’t have any blockers on the way of implementing AI and NLP analysis to such a product, we still had the challenge of scrapping and maintaining the data of different users. 

  • We spent one month conducting a PoC on a small volume of posts to prove the possibility of the principle. 
  • Establishing the concept meant we could create a scrapping solution that gets publicly available data (gathering posts, images and related information to analyze it further). 
  • To tackle the issue of scrapping any given user’s data, we leveraged Selenium scrapping solutions.

NLP Analysis + Cloud

The solution’s principle combines the advanced machine learning module for content and image analysis and the NLP algorithms for marking posts under the risk criteria. This combination created a comprehensive system that processes a large amount of visual and written content from six social networks and provides reliable results in a matter of hours.

The product is designed according to microservices architecture best practices. It provides the client with certain benefits:

  • There’s an option to easily extend data sources (social networks).
  • A feature toggle allows the platform’s owner to enable or disable certain features for selected user accounts in real time.
  • Autoscale Cloud Server Infrastructure (with the help of Kubernetes Cluster), which enables the queue of requests (it’s possible to schedule scrapping or set them in a queue), but most importantly it enables load balancing. It means that the client only pays for the server whenever we actually have a need to conduct the scrapping. To achieve cutting hosting costs and application scalability during peak loads, we leveraged serverless infrastructure. Depending on the amount of data consumed from a particular source, scale-up and scale-down are possible.
  • The system encapsulates business logic to integrate with the data source.
  • The solution implements an end-to-end DevOps process letting the business concentrate on functionality and on-time feature delivery.

Web-based Solution

We used .NET for back-end development and Angular for the front-end: these frameworks are a proper choice for large-size systems with an extensive workload and a need for quick data processing (back-end) and feature-rich user interface (front-end). We’ve built the front-end as a single-page application (SPA) while also integrating the payment system.


We made the platform compliant with the FCRA and EEOC guidelines for social media background checks, meaning that it doesn’t access the information (posts or other content) the user has voluntarily limited, hidden, or deleted, thus only relying on what’s actually available on the UI part.


We Used:

  • Back End

    • .NET
  • Front End

    • Angular
  • DevOps & Cloud

    • Azure
    • Docker
    • GitLab CI/CD
  • Database Development

    • Elasticsearch


The project’s tangible results in key facts and numbers:

  • 5 years of harmonious work on the product (our and client’s teams integrated);
  • ML-powered content analysis and NLP algorithms for marking potentially risky social media posts;
  • Easy application scaling even during peak loads;
  • Serverless infrastructure to cut hosting costs.
  • 1 month for the Proof-of-Concept stage to confirm the technical feasibility despite previous unfavorable assessments;
  • 9 months of development to get the first platform users;
  • We have developed a technological framework that works with a number of networks as a versatile method.
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