Case Study: Content Moderation

30K+ docs web scrapped & annotated for Content Moderation

There’s an increasing demand for AI-powered content moderation
that strive to secure the online space where we connect & communicate.

As social media usage continues to grow, the
problem of cyberbullying has surfaced as a
significant hurdle for platforms striving to
ensure a secure online space. A staggering
38% of individuals encounter this
detrimental conduct on a daily basis,
emphasizing the urgent demand for inventive
content moderation approaches.
Organizations today rely on the use of
artificial intelligence to address the enduring
problem of cyberbullying proactively.

Cybersecurity:

Facebook’s Q4 Community Standards Enforcement Report revealed – action on 6.3 mn pieces of bullying and harassment content, with a proactive detection rate of 49.9%

Education:

A 2021 study found that 36.5%% of the students in the united states between the ages of 12 & 17 years experienced cyberbullying at one point or other during their schooling.

According to a 2020 report, the global content moderation solutions market was valued at USD 4.07 billion in 2019 and was expected to reach USD 11.94 billion by 2027, with a CAGR of 14.7%.

Real World Solution

Data that moderates global conversations

The client was developing a robust automated
content moderation Machine Learning
model for its Cloud offering, for which they
were looking for domain-specific vendor who
could assist them with accurate training data.

Leveraging our extensive knowledge in natural language processing (NLP), we assisted the client in gathering, categorizing, and annotating more than 30,000 documents in both English and Spanish to build automated content moderation Machine Learning Model bifurcated into Toxic, Mature, or Sexually Explicit content catagories.

Problem

Web scraping 30,000 documents in both Spanish and English from prioritized domains
Categorizing the gathered content into short, medium, and long segments
Labeling the compiled data as toxic, mature, or sexually explicit content
Ensuring high-quality annotations with a minimum of 90% accuracy.

Solution

Web Scrapped 30,000 documents each for Spanish & English from BFSI, Healthcare, Manufacturing, Retail. The content was further bifurcated into short, medium & long documents
Successfully labeling the classified the content as toxic, mature, or sexually explicit content
To achieve 90% quality, Shaip implemented a two-tier quality control process:
» Level 1: Quality Assurance Check: 100% of the files to be validated.
» Level 2: Critical Quality Analysis Check: Shaips’s CQA Team to assess 15%-20% of the retrospective samples.

Result

The training data helped in building automated content moderation ML model that can yield several outcomes beneficial for maintaining a safer online environment. Some of the key outcomes include:

Efficiency to process vast amt of data
Consistency in ensuring uniform enforcement of moderation policies
Scalability to adapt to growing user base and content volumes
Real-time Moderation can identify &
remove potentially harmful content as it is generated
Cost-effectiveness by reducing the reliance on human moderators

Examples of Content Moderation

Accelerate your Conversational AI
application development by 100%

Creating clinical NLP is a critical task that requires tremendous domain expertise to solve. I can clearly see that you are several years ahead of Google in this area. I want to work with you and scale you.

Google, Inc. Director

My engineering team worked with Shaip’s team for 2+ years during the development of healthcare speech APIs. We have been impressed with their work done in healthcare-specific NLP and what they are able to achieve with complex datasets.

Google, Inc. Head of Engineering

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