More and more online services nowadays are using Machine Learning (ML), the method of data analysis that will automate the building of analytical mode. Systems can surely learn from data, make decisions with very little human intervention, and even identify patterns. ML is the driving force behind such processes.
All those billions of people who are using the internet are generating just as much content in the form of videos, images, posts, and many more. While these users want to visit various platforms or retailers where they want to have a safe experience, the content has to be regulated. That’s when content moderation comes into the game, as it aims to remove abusive, explicit, harmful, and fake data.
How does it work?
The content queues and escalation rules for review systems based on ML will vary depending on the company, although they’ll generally include some AI moderation:
- Pre-moderation: AI will moderate the user content before posting, and the one that isn’t categorized as harmful will be made visible for the users. As there’s no such thing as perfection in life, it won’t be for AI as well. There will be content that has a high probability of being harmful or at least not business-friendly, and it will be removed. There’s also the possibility for the AI model to flag the content for human review if there are not enough clues to label it as harmful or not.
- Post-moderation: When users report harmful content, it will be used for review by AI or humans. If the review is done by AI, it will follow the same process described above.
There is plenty of harmful content out there on the vast world of the internet, and we’ll describe some of it below that will also undergo the content moderation process:
Nudity, Pornographic content
We can find sexually explicit content on so many corners of the internet. We all know why this type of content is harmful, especially for children. Artificial intelligence (AI) will usually detect this type of content easily through image processing methods.
Different platforms also have different ways of dealing with pornographic content – Instagram is one of the most strict platforms when it comes to such content, as it completely forbids it. Video chat platforms such as Omegle have been heavily using machine learning to detect and prevent bad actors in their platforms.
We all have a pretty good idea of what ‘abusive’ means, and in this case, it refers to that type of online behavior meant to offend someone or make the person feel bad, such as hate speech, cyber-aggression, cyber-bullying, and more.
Some of these online behaviors are certainly difficult to detect, but plenty of companies have invested a lot of effort to try to detect them automatically through intelligent programs.
Self-harm and suicide had been some of the outcomes of the victims of online abusive content, which led to a lot of pressure on social networks to do their best in banning such content. Image processing, social network analysis, and natural language processing are some of the ways to identify abusive content.
Fake and misleading content
Commonly known as ‘fake news’ [PDF], this is perhaps the most difficult type of content to identify and prohibit. Pretty much nobody can say for sure every time if a piece of information is correct or not, but that doesn’t mean that companies have to give up hope.
Some of the best chances are incorporating natural language processing, commonsense knowledge bases, and various reputation-based factors via social network analysis and stylistic elements of the content.
In general, the internet has improved the lives of many people, and we can’t imagine the world without it nowadays. The online realm is helping us have a job, a bank account, it found a spouse for many of us, and the long list of benefits could continue forever. But still, it also has a dark side, and we must think twice before making any move online.