User-generated content (UGC) includes brand-specific content customers post on social media platforms. It includes all types of text and media content, including audio files posted on relevant platforms for purposes like marketing, promotion, support, feedback, experiences, etc.
Given the ubiquitous presence of user-generated content (UGC) on the web, content moderation is essential. UGC can make a brand look authentic, trustworthy, and adaptable. It can help in increasing the number of conversions and help build brand loyalty.
However, brands also have negligible control over what users say about their brand on the web. Hence, content moderation with AI is one of the ways to monitor the content posted online about a specific brand. Here’s all you need to know about content moderation.
The Challenge of Moderating UGC
One of the biggest challenges with moderating UGC is the sheer volume of content that requires moderation. On average, 500 million tweets are posted daily on Twitter (Now X), and millions of posts and comments are published on platforms like LinkedIn, Facebook, and Instagram. Keeping an eye on every piece of content specific to your brand is virtually impossible for a human being.
Hence, manual moderation has a limited scope. Plus, in cases where urgent reaction or mitigation is required, manual moderation won’t work. Another stream of challenges comes from the impact of UGC on the emotional well-being of the moderators.
At times, users post explicit content causing extreme stress to the individuals and leading to mental burnout. Moreover, in a globalized world, effective moderation requires a local content analysis approach, which is also a big challenge for individuals. Manual content moderation may have been possible a decade ago, but it’s not humanly possible today.
The Role of AI in Content Moderation
Where manual content moderation is a massive challenge, unmoderated content can expose individuals, brands, and any other entity to offensive content. Artificial Intelligence (AI) content moderation is an easy way out to help human moderators complete the moderation process with ease. Whether it’s a post mentioning your brand or a two-way interaction between individuals or groups, effective monitoring and moderation are required.
At the time of writing this post, OpenAI has unveiled plans to revolutionize the content moderation system with GPT-4 LLM. AI provides content moderation with the capability to interpret and adapt all sorts of content and content policies. Understanding these policies in real-time allows an AI model to filter out unreasonable content. With AI, humans won’t be explicitly exposed to harmful content; they can work at speed, scalability, and moderate live content as well.
[Also Read: 5 Types of Content Moderation and How to Scale Using AI?]
Moderating Various Content Types
Given the wide array of content posted online, how each type of content is moderated is different. We must use the requisite approaches and techniques to monitor and filter each content type. Let’s see the AI content moderation methods for text, images, video, and voice.
Text-Based Content
An AI program will employ natural language processing (NLP) algorithms to understand the text posted online. Not only will it read the words, but will also interpret the meaning behind the text and figure out the individual’s emotions. AI will use text classification techniques to categorize the content based on text and sentiments. In addition to this simple analysis, an AI program implements entity recognition. It extracts names of people, places, locations, companies, etc., while moderating.
Voice Content
AI programs use voice analysis for moderating the content posted in this format. These solutions use AI to translate voice into text format and then run NLP plus sentiment analysis. This helps the moderators with quick results on the tonality, sentiment, and emotion behind the voice.
Images Content
Computer vision is used to make an AI program understand the world and create a visual representation of all things. For image moderation, AI programs detect harmful and obscene images. It uses computer vision algorithms to filter out unhealthy images. Going into further detail, these programs detect the location of harmful elements in the image. The programs can categorize each section of the image according to its analysis.
Video Content
For video content moderation, an AI program will use all the techniques and algorithms we have talked about above. It will successfully filter out harmful content in the video and present results to human moderators.
Improving Human Moderators’ Work Conditions with AI
Not all content posted on the web is safe and friendly. Any individual exposed to hateful, horrific, obscene, and adult content will feel uncomfortable at some point. But when we employ AI programs for moderating content on social media and other platforms, it will protect humans from such exposure.
It can quickly detect content violations and protect human moderators from accessing such content. As these solutions are pre-programmed to filter out content with certain words and visual content, it will be easier for a human moderator to analyze the content and make a decision.
In addition to reducing exposure, AI can also protect humans from mental stress and decision bias and process more content in less time.
The Balance Between AI and Human Intervention
Where humans are incapable of processing tons of information quickly, an AI program is not as efficient in making decisions. Hence, a collaboration between humans and AI is essential for accurate and seamless content moderation.
Human in the Loop (HITL) moderation makes it easier for an individual to partake in the moderation process. Both AI and humans complement each other in the moderation process. An AI program will need humans to create moderation rules, adding terms, phrases, images, etc., for detection. Plus, humans can also help an AI become better at sentiment analysis, emotional intelligence, and making decisions.
[Also Read: Automated Content Moderation: Top Benefits and Types]
The Speed and Efficiency of AI Moderation
Content moderation’s accuracy hinges on AI model training, which is informed by datasets annotated by human experts. These annotators discern the subtle intentions behind speakers’ words. As they tag and categorize data, they embed their understanding of context and nuance into the model. If these annotations miss or misinterpret nuances, the AI might also. Hence, the precision with which humans capture the intricacies of speech directly impacts the AI’s moderation capabilities. This is where Shaip, can process thousands of documents with human-in-the-loop (HITL) to train ML models effectively. Shaip’s expertise in providing AI training data to process and filter information can help organizations empower content moderation and help brands maintain their reputation in the industry.
