Every minute, 240,000 images are shared on Facebook, 65,000 images are uploaded on Instagram, and 575,000 tweets are posted on Twitter.
Simply put, tons of user-generated content are posted in various forms daily, and moderating what finds its way to your brand’s online platform can be overwhelming and tedious — unless you leverage AI content moderation.
AI can optimize the moderation process by automatically classifying, flagging, and removing harmful content.
To help you determine how your brand should leverage AI content moderation, let’s walk through what content moderation is and the different AI technology available.
How AI Content Moderation Can Help Your Brand
It’s common for AI content moderation to implement these guidelines.
Now that you know what content moderation is, let’s explore the different types of content moderation and how AI can play a role in scaling the process.
Types of Content Moderation
To understand how best to use AI to moderate content, you first need to know the different types of content moderation.
Pre-Moderation
Pre-moderation assigns moderators to evaluate your audience’s content submissions before making them public.
If you’ve ever posted a comment somewhere and it was restricted or delayed following approval, then you saw pre-moderation at work.
Pre-moderation aims to protect your users from harmful content that can negatively impact their experience and your brand’s reputation.
However, a downside to pre-moderation is that it can delay conversations and feedback from your community members due to the approval process.
Post-Moderation
With post-moderation, user-generated content is posted in real-time and can be reported as harmful after they are public. After the report is made, a human moderator or content moderation AI will flag and delete the content if it violates established rules.
Reactive Moderation
Some communities rely solely on their members to flag any content that violates community guidelines or is disliked by most users. This is called reactive moderation, a common process in small, tight-knit communities.
With reactive moderation, community members are responsible for reporting inappropriate content to the platform’s administration, consisting of community leaders or whoever runs the site.
Administrators will then check the flagged content to see if it violates any rules. If the administrators confirm the content violates the rules, they will manually remove it.
Distributed Moderation
Distributed moderation consists of community members voting on user-generated content submissions to determine if the content can successfully be submitted. The voting is often done alongside the supervision of senior moderators.
A positive takeaway from distributed moderation is that the process encourages higher participation and engagement from the community. However, it can be risky for brands to trust users to moderate content appropriately.
How AI Content Moderation Can Help Your Brand
It’s no secret that AI-powered tools like the ones available at HubSpot can boost productivity and save marketers time. This is especially true when it comes to content moderation.
Sifting through large amounts of inappropriate, malicious, or harmful content can take a toll on you and your colleagues.
And relying solely on humans can leave room for human error or result in damaging content remaining public for an extended time before it’s finally taken down.
AI content moderation can quickly remove or block various forms of content that clash with your brand. Below are some of the ways AI can optimize your content moderation.
AI Content Moderation for Texts
Natural language processing algorithms can decipher the intended meaning behind a text, and text classification can categorize text based on the content.
For example, AI content moderation can analyze a comment to determine if the text’s tone indicates bullying or harassment.
Entity recognition is another AI technique that can moderate text-based user-generated content. The method finds and extracts companies, names, and locations.
The AI can be used to track your brand’s mentions and your competitor’s mentions.
AI Content Moderation for Images and Videos
Computer Vision, also known as Visual-AI, is a field of AI used to extract data from visual media to determine if there is any unwanted or harmful content.
Furthermore, natural language processing and computer vision in tandem can analyze texts within an image, such as street signs or T-shirt slogans, to detect any suggestive content.
Both forms of AI content moderation can moderate user-generated videos and photos.
AI Content Moderation for Voice Recordings
Voice analysis is the technology used to evaluate voice recordings and their content. It combines several kinds of AI-powered content moderation tools.
For example, voice analysis could transcribe a voice recording into text and run a natural language processing analysis to identify the content’s tone and intention.
In short, AI content moderation can evaluate user-generated content more quickly and more efficiently than manual processes.
It allows your marketing team to spend less time sifting through content and more time crafting your next marketing campaign.
Using AI to optimize your content moderation process also protects your audience, brand, and team from harmful content, making for a more enjoyable experience.
