Can Machine Learning help in Content Moderation?

Content moderation is a critical part of the SEO ecosystem, one which largely lacks oversight. As such, it is not unreasonable for companies to turn to machine learning for a system that, at least, is as effective as human workers. With the past few years bringing in countless debates on whether companies are engaging in automated technology, we can reasonably assume that they are not. However, a few companies have begun using automated content moderation to create software to scrub social media of harmful material.

While no system is 100% effective at this job, content moderation software allows companies to conduct a small window of time to ensure that posts and videos have been removed from the internet before they have the chance to go viral.

This is critical to ensuring that content is kept from becoming hate speech, scam advertisements, and other damaging information. This is not a matter of simply looking at the content that has been reported, however. Content moderation software must also track whether the people who have reported the content changed their minds.

The purpose of this is to deter people from reporting content that has been edited to make it seem more damaging than it is in reality. Even if the content is re-posted elsewhere, the original report was enough to alert the system to the possibility that it has already been uploaded to another site, which allows content moderation software to run a check and remove the content if it turns out to be spam.

There are currently several companies that sell moderation software, though there is a lot of competition among them and it is almost impossible to determine which is the best.

Unfortunately, machines still struggle with what constitutes a harmful post. Like many other content moderation systems, content moderation software must determine whether content will be considered offensive or whether it will be considered hateful. As such, it is not uncommon for moderators to use their power to remove content that is deemed unacceptable.

While there is no denying that these tools are effective at removing content that may hurt the public, they are not foolproof. As long as companies invest in the website content moderation services and the other systems, they must take every measure possible to ensure that they do not simply become a technological black box.

 

How Facebook Uses Machine Learning for Content Moderation

Consider the content moderation process at Facebook. In an effort to make good on the company’s mission of making the world more open and connected, Facebook has had to deal with the inherent conflict between privacy and openness, as well as social responsibility and profitability.

These conflicting motivations have led to some controversial decisions. For instance, the company will use image detection to determine whether image of something, deemed by Facebook’s reviewers as in violation of its standards, should be removed from its service.

It then uses image detection to run through a list of stored images to see if any of the photographs were reported by other users. Only if none of those images is found to violate Facebook’s policies will Facebook determine that the image should not be deleted.

But in reality, image detection has other downsides. First of all, it may also be biased against minority groups, as noted by various legal experts. Secondly, having a team of humans making the decision, rather than a machine, leads to more nuanced and intelligent decisions.

This system-level implication of image detection is a cautionary tale, as machine learning algorithms can be biased, as humans can be. Even if an algorithm is executed at the level of neural networks, such algorithms have their own biases and limitations.

 

5 Techniques for Content Moderators

Content moderation is the process of making sure content that is not permitted on a website is removed from public view. Admins and content moderators use various kinds of techniques in order to ensure that those who produce content that is not allowed are prevented from delivering it. Some techniques include:

 

1. Verification – Methods such as checking IP addresses, scanning a message or status of a URL in order to ensure it is not user generated, etc.

 

2. Signature – After determining that the web page or email is not user generated, the author/producer of the content is then asked to verify that the email address of the author belongs to the sender of the content.

 

3. Barcode Scanning – Barcode scanning is a way of creating unique identification codes that then identify the content and/or user that created it.

 

4. Interaction – The user is then asked to give proof that he or she did not create the content. The form requires the user to verify his or her user profile (e.g., a user name, or personal info).

 

5. Response Recognition – Then, the moderator/admins use software to track down the author of the content. This is done by asking the author to reply to the question sent by the moderator or using web-based services to identify a user profile or send an email with the author’s user name and user ID.

 

As you can see, a variety of different methods are used to determine that the content that has been published is not authentic.

 

 

Conclusion

Machine learning is a powerful tool for increasing visibility and audience size for web publishers, even for organizations that have no interest in developing new tech. The purpose of this guide was to explore the different types of machine learning and to give a brief overview of their applications. Given that machine learning is rapidly changing and becoming more integrated into modern web technology, it would be difficult to cover everything without a full understanding of the technology itself.