User-generated contents (UGCs) significantly impact the effectiveness of a business’s marketing strategies. Because UGCs are organic (i.e., not paid) content and are often from customers and end-users, their appropriateness is not always assured.
There have been times when failure to moderate these types of content properly has led to marketing blunders. One example is when the New England Patriots unintentionally published a racial slur.
That’s why content moderation is vital in businesses relying on organic and user-generated content. But what is content moderation, and how does it work?
Read on to find out.
What is content moderation?
Content moderation refers to screening content on a given platform to search for anything inappropriate or against the platform’s guidelines.
This process involves checking published content in the channel for anything that might go against a platform’s pre-set rules. Social media platforms and internet forums are the most common implementor of content moderation.
There are many reasons that necessitate content moderation. Foremost among these is the need to keep platforms safe environments and uphold their community guidelines. Content moderation keeps platforms safe for users and brands alike.
Some of the most commonly flagged content during content moderation often include:
- Violence
- Extremism
- Nudity
- Hate speech
- Copyright infringement
However, different platforms have varying guidelines on what types of content are subject to moderation.
Contents subject to moderation
Almost all content uploaded on online platforms can be subject to moderation. These include:
- Texts
- Images
- Videos
- Live streams
How content moderation works
For content moderation to work, a platform or media must follow a clear set of guidelines. These guidelines serve as the basis for moderators’ work – the community guidelines enforcers.
Aside from establishing what types of content are subject to review and removal, content moderation involves defining the thresholds of moderation itself. The moderation threshold refers to the level of sensitivity moderators should adhere to when reviewing content.
These thresholds usually depend on the platform users’ expectations and demographics, as well as the type of business to where the platform belongs.
Types of content moderation
Businesses and other platform owners can approach content moderation in different ways. As various platforms have varying content guidelines, a one-size-fits-all approach to content moderation will not work.
Below are some types of content moderation that moderators can choose from for the platforms they’re monitoring.
Automated moderation
This type of content moderation relies on technology for faster, easier, and safer content monitoring and removal. Automated moderation uses AI-powered algorithms to analyze texts, images, and videos in a fraction of the time it would take a human moderator.
Automated moderation is particularly helpful in screening inappropriate textual content. Its keyword screening capabilities (plus conversational patterns and relationship analysis in more advanced systems) can help moderate thousands of texts more than their human counterparts.
Pre-moderation
In this type of moderation, contents are sent to a review queue before they get published. Contents only go live once they have the approval of a moderator.
Pre-moderation is by far the most effective way of screening inappropriate content. However, it is a slow process and can be inefficient in the fast-paced online environment.
Post-moderation
In contrast with pre-moderation, contents go live before they are reviewed in the post-moderation approach.
Published content flagged for violating guidelines are then removed from the platform. Post-moderation is nowhere near as secure as pre-moderation. However, it is still the preferred approach in many digital platforms as it prevents bottlenecks in UGC approval.
Reactive moderation
Whereas automated moderation relies on technology, reactive moderation counts on users to help flag inappropriate published content. This approach allows users to mark and report content violating community guidelines.
While reactive moderation can be standalone, pairing it with post-moderation garners better results. The reporting feature of social media sites like Facebook and Twitter are examples of reactive moderation.
Distributed moderation
This is the least-used moderation method. Distributed moderation fully relies on its user community to monitor and take down inappropriate content. In this approach, users use a rating system to gauge whether a given content goes against the community guidelines.
AI vs. Human content moderation
Despite the effectiveness and efficiency of artificial intelligence (AI) in content moderation, human agency is still necessary for this field.
For all the speed of AI-powered moderation, it cannot match the accuracy of humans in reading between the lines and distinguishing between nuances of human communication.
Solely relying on AI-powered bots to man a platform’s content moderation can lead to false-positive and false-negative flags. For instance, users might have their posts deleted for using certain keywords (e.g., suicide, kill, or murder) without considering the textual context.
However, it doesn’t discount the fact that AIs are immensely faster than humans at moderating content across multiple platforms.
Reading the context of symbolism is another area where humans trump AIs. For instance, a user might upload an image with the swastika symbol. AI moderators may remove this photo for being offensive without considering that the swastika is a long-used symbol among some Asian religions.
For an effective platform moderation, a collaboration of human moderators aided by AI-powered tools is the key.
Challenges in content moderation
Moderating content across various platforms is a taxing job. Everyday, moderators are exposed to the types of content that platform owners don’t want their users to see. Constant exposure to hate-fueled text posts, extremely graphic images and videos of violence, and sexual content can negatively impact the mental health of moderators.
While automation helped take over moderation for many highly disturbing content, it has also increased the number of humans exposed to such content.
Before deployment, AI moderators need to be fed data they can use to filter offensive content. Human workers annotate these disturbing data and double-check if AIs are making the right decisions.
Thus, humans are still largely involved in removing disturbing content from platforms.
