What Is the Difference Between Automated and Human Content Moderation
User-generated content is a powerful branding tool. It has significantly become an integral part of recent business growth strategies. Thanks to its commendable influence on buyer behavior, businesses are compelled to employ different forms of moderation to regulate information shared about their services, and ultimately protect their brand and followers.
Ideally, automated content moderation and human content moderation should work together to produce optimum results for moderating content on your website and on your online communities.
However, comparing the difference between how the two operate individually, how each surpasses the other as well as how each benefit a brand is also a must. It paves the way for gaining a clearer view of why you need to strike the perfect balance between these two forms of regulating user content.
AI-based content moderation: How does it work?
The combination of AI and content moderation works using a so-called base model for the type of content it must moderate.
For example, an AI moderator is used to detect images depicting drugs or drug use. The AI will then be shown several pictures of different types of illegal drugs or images of people using drugs. To make the AI’s base model more specific, photos of apparatuses needed to take in prohibited substances are also provided. This step is stored as reference for the automated moderator once it starts sifting through dozens of images in an online community.
Natural language processing is also applied to make AI more useful in policing online content. It involves the smart derivation of content and cues from human language to help check, detect and moderate user content more aptly.
An example would be the use of semantics to detect and filter words and languages used in forums, comments, and chat messages. Remarks and posts that are detrimental to the brand’s online community and end-users can be understood and processed more accurately.
Due to the variety of user content being submitted in various online communities, AI moderation is being continuously modified for it to perform a broader array of moderation services. Recent updates on AI-powered content moderation has now made it capable of accomplishing the following:
- Automatically filtering images with prohibited content
- Blocking users violating posting guidelines
- Creating blacklists that contain words, phrases, and keywords for visual content to enable quicker detection and elimination of unacceptable content shared by end-users.
The beauty behind the use of AI in online content moderation lies in granting brands the opportunity to program and modify a set of guidelines to keep their business and end-users from being exposed to or associated with inappropriate content online. The process allows the prevention of disturbing and unacceptable content from surfacing on a business’ online branding channels.
A closer look at human moderation
Where computer-powered moderation is lacking, that’s where brands and human moderators can pinpoint strategies done by online scammers to spread malicious and disruptive content online. Human Moderation is able to judge user intention and therefore makes content moderation more efficient and adaptable.
It helps brands establish a human connection with its end-users while enforcing strict compliance to posting guidelines on its online community. For instance, an end-user posts a comment about an anti-bullying campaign.
An AI moderator would concentrate on the keywords ‘bully’ and ‘bullying’ but may disregard the intention of the person who posted the comment. There lies the possibility of flagging the campaign based on its name alone. Whereas, a human moderator can distinguish that the end-user’s purpose is not to promote bullying, but rather, to inform fellow users about an initiative to put a stop to bullying.
The subjective judgment of human-operated moderation aids in identifying subtle ways end-users express displeasure over fellow members or share content that may seem harmless at first but in reality, are very offensive to a select group of people. Sensing a wide variation of human emotions and the complex ways they may express it is how human moderation is able to discriminate between intentionally harmful content from those that are not.
There may also be certain cultural references that AI may not be able to recognize given that it is not programmed to detect such type of content.
Automation vs Human Content Moderation
Let’s weigh the pros and cons of content moderation using AI and human moderation through the following criteria:
- Bias or Judgment
Quantity is one of the first things people focus on when comparing and contrasting these two. The sheer quantity of user-generated content that needs to be regularly checked and monitored by human moderators is intimidating. Everybody knows that AI is faster than human content moderators.
AI moderation primarily helps in reducing the workload and psychological stress that come with live moderation. It has the capacity to accomplish moderation tasks in bulk, and therefore works best for repetitive and routine tasks that include detecting and deleting duplicate content, reading captions and metadata on images, as well as sorting files and images.
The increasing quantities of UGC that human moderators must handle daily also come with occasionally inappropriate subjects. Some posts exceed the threshold of what is morally acceptable that consistent exposure to such disturbing content often takes a toll on the moderators’ psychological stamina.
Graphically detailed and x-rated images promoting extreme violence, hate, racism, and otherworldly forms of harm to other people is a scene that can haunt a person’s peace of mind for a long time.
AI can be programmed simply to sort, detect, and eliminate unwanted content. Without the emotional component of content moderation, the result is a faster and more continuous monitoring process, all in real-time.
On the other hand, AI moderation also has its limitations. Its major blindspot is qualitative judgment. Human moderators exceed automated content filters in this regard. Beyond detecting prohibited and offensive words or phrases, moderators scrutinize and judge user intent behind the texts, images, and videos they post.
Profanity filters are often bypassed by forum and online community members. They do this by replacing letters and words with symbols, making it difficult for the AI’s base model to detect it or worse, approve the content as safe or nothing out of the ordinary.
Another accurate demonstration of qualitative judgment in human moderation is the adeptness to pinpoint fake news from real, reliable sources of information. There are people and organizations whose expertise lie in creating websites disguised as legitimate digital news outlets or sources of facts.
When fake news is presented to an auto moderator, it may fail to distinguish or perceive fake, misleading information from credible news and articles. Unless the AI is programmed to monitor false news, it will end up allowing fake information to be posted and reproduced on the brand’s website and social media pages.
Identity theft is a common practice of scammers. They steal photos from unsuspecting users or even celebrities, and assume their identity under a combination of made up personal information. Machine-run content monitoring cannot distinguish impostors or celebrities, but human moderators can.
Bias or Judgment
Quality may be the strength of human moderation, but it also has the inclination to go beyond intrinsic biases. Human moderation can be prone to scrutinizing user posts with intrinsic bias, making its moderation slightly skewed with personal opinions and beliefs.
Say, a moderator that lacks proper training may not have sufficient knowledge and background in judging UGC from an objective perspective. They might take profanities, offensive jokes, and rebuttals from followers personally. When this happens, disputes could arise between the moderators and online community members.
What if a moderator’s beliefs contradict the opinions of some of the users? It can be challenging to review user content that, albeit bearing a stark contrast against personal viewpoints, is not exactly offensive to others nor is it banned in the digital community. Sure, veteran content police may be immune to such disagreements, but this may not always be the case.
Also, there is a significant effect on the moderators’ well-being when they constantly have to enforce guidelines that contradict their convictions and personal viewpoints. When not addressed properly, the effect leaves them drained and burned out.
Moderation done by people can be outsourced. This is especially applicable to brands regularly catering to large volumes of end-user content. Outsourcing human moderation likewise benefits countries that have offices and branches overseas.
Hiring people from other countries to moderate user content, especially in areas where a brand has a target audience or a branch office, effectively eliminates moderation issues related to language and cultural context.
However, if a business has limited manpower for managing all the content it receives on a daily basis, then it could take a toll on the mental and emotional well-being of the moderators viewing all the disturbing content being posted, uploaded or shared by a brand’s followers.
Redistribution of tasks is also applicable in content moderation with AI. Companies that partner with BPO firms work together to produce a customized AI content moderation to suit the former’s unique online culture and diverse population of supporters.
Similarly, outsourcing automated moderators proves useful for apps and websites designed for forming intimate relations, as in the case of the online dating industry. Detecting profanities, inappropriate messages, and offensive remarks in real-time effectively boosts user security and trust.
Is AI better than humans in content moderation?
Automated and human moderation should not exist to replace each other.
The main goal of businesses employing moderation must be to strike the perfect balance between the individual roles and imperfections of AI and human moderators.
A great example of this would be to expand the predetermined standards for automatically moderating user-generated content. By consistently updating AI moderation’s base model, it gains the skill to take on more sensitive types of content and ultimately help diminish the high stress levels constantly experienced by human moderators.
It can also make moderation run by technology more flexible and efficient, particularly when monitoring user content that is more complicated in nature. In addition, AI moderators can be taught how to check videos on live stream to maximize a brand’s defense against malicious comments and posts.
Picture a scenario where both AI and human moderation can simultaneously check image and video content simultaneously. Dramatically reducing disruptive content is a huge possibility.
Another scenario where AI moderation and human-powered moderation are excellently complementing each other is making AI filters concentrate on detecting profanity, deleting spam, identifying posts with inappropriate content, as well as accepting and rejecting user comments.
Meanwhile, human moderation will concentrate on surveying any abnormal behavior or activity of members in a brand’s online community, tracking user posts with highly sensitive content, and analyzing intent behind questionable UGCs.
Harmony is the key.
The perfect combination of speed (auto moderation) and quality (human moderation) is a fatal weapon that businesses can use against hackers, scammers and individuals whose goal is to harm their credibility and end-users.