Every day, millions of terabytes of data are generated online—and a significant chunk of that is user-generated. With the rapid rise of social media platforms and digital communities, user-generated content (UGC) has exploded across the internet. As people spend increasingly more time online, content in the form of text, images, videos, memes, and voice notes is created and shared at an unprecedented scale. From social media posts and chat messages to livestreams and viral videos, UGC is driving digital engagement like never before.
While UGC enables users to share opinions and experiences, it also brings with it several risks if left unchecked. When users are given the freedom to generate their own content, they sometimes share illegal or harmful content, such as hate speech, misinformation or explicit material. If left unchecked, such content can bring disrepute to a platform’s credibility, alienate users and even lead to legal consequences. Hence, content moderation on online platforms is necessary to ensure that users experience genuine engagement and have meaningful discussions, instead of experiencing cyberbullying or toxic behaviour. As a case in point, Statista.com reports that Facebook took down 5.8 million pieces of hate speech content in the fourth quarter of 2024.
Considering the enormous amount of content produced every second, relying solely on human moderators to review and manage everything in real time is practically unfeasible. Compounding this challenge is the emotional toll on content moderators, who are routinely exposed to graphic, disturbing, and harmful material. This repeated exposure can lead to severe stress, burnout, and long-term mental health issues—making the case for scalable, intelligent moderation solutions even more urgent.
the evolution of content moderation
In the early days of the Internet, the digital landscape was largely unregulated, characterized by minimal oversight and few formal rules governing online behavior. The content created those days was heavy on text, with fewer images and videos. The emphasis was on freedom of expression, where everybody had a voice.
Moving on to the 2000s, the Internet matured. Platforms like Facebook, Youtube and Twitter/X brought millions of users together. UGC gathered steam during this phase and companies understood the need to keep these platforms safe and usable. This is when human moderators were hired to manually review reported posts, remove offensive material, and enforce community guidelines.
By the 2010s, the volume of content being generated across platforms was becoming gargantuan and beyond what human teams could feasibly manage. This is when artificial intelligence (AI) and machine learning (ML) stepped in and became game-changers in the area of content moderation. Platforms started deploying algorithms to automatically detect and remove inappropriate content. YouTube, for example, began using AI to flag videos that violated copyright or community standards. Though automated moderation increased efficiency, some problems still remained. AI tools did not understand context, nuances and cultural differences. This led to situations where legitimate content was mistakenly removed and harmful content occasionally slipped through.
Today, content moderation is more complex and sophisticated than ever before. Most platforms employ a hybrid model that combines automated tools with human review. While AI alone might not be enough to understand the finer nuances of the content being checked, a human-in-the-loop can help with more detailed moderation. While AI can do a bulk of the work, humans are needed for reviewing edge cases and appeals.
Today there are several regulations shaping content moderation practices. The European Union's Digital Services Act (DSA) and India’s IT Rules 2021 are examples of how governments are holding tech companies accountable for what is posted on their platforms. Companies are also increasing transparency, publishing regular content moderation reports, and collaborating with fact-checkers and civil society groups to improve accuracy and fairness. In fact, in January this year, Meta shifted from centralized third-party fact-checking to a collaborative moderation model where users contribute community notes to posts which are verified by others.
While the sheer scale of content has made manual moderation unsustainable, it is clear that AI alone isn’t enough. Human oversight, ethical safeguards, transparency, and collaborative tooling are essential to navigate emerging threats like AI-generated abuse and deepfakes. Content moderation will undergo major transformation in the coming years and platforms will need to adopt smarter, more adaptive systems that balance freedom of expression with safety and accountability.
how IBPM can help?
Organizations are shifting from reactive threat management to proactive approaches that integrate safety at the core—leveraging a combination of human expertise, AI-driven moderation, and comprehensive frameworks. Infosys BPM offersrobust Trust & Safety (T&S) solutions across sectors like eCommerce, gaming, media, travel, BFSI, and healthcare. Backed by deep expertise in generative AI and digital transformation, our practice protects users and platforms, ensures compliance with regulations, and powers secure, trustworthy digital experiences that foster growth and long-term resilience.