While scrolling your Instagram feed or going through the comment section of a post, you must have seen low-quality, high-volume content promoting something, featuring exaggerated praise and generic, identical linguistic styles.
Sometimes comments or similar texts are not even relevant to the original posts. So, naturally, you get annoyed by them, right?
Yes, these are called AI-generated spam.
These texts are mostly meant to promote something or provoke readers to buy a service, or simply to increase subscriptions and sharing.
Right now, Google and the internet are flooded with these types of AI-generated spam, whether it's social media posts, product reviews, comments, or simply the entire blogs of a particular website.
These kinds of AI-generated spam can ruin the reputation or go against the content policies of a certain website, brand, media house, or news portal.
So, if you are one of them, using a content moderation process is essential to catch AI-generated spam and protect your image, originality, and policies.
Here are six steps to build a content moderation pipeline.
AI Detection
As we are talking about spotting AI-generated spam, texts, or any kind of content, the very first step or level of the pipeline should be checking for AI-generated content.
For that, you can simply include a well-reputed AI detector that is expert at catching AI spam.
Once content goes through the detection process, you can mostly figure out which ones require changes and where they are required.
So, implementing AI detectors should be the first step of the moderation pipeline.
Content Ingestion
Content ingestion is the main guard at the door or the front desk of your moderation pipeline. It kind of filters everything at the very beginning.
It collects all forms of content: text, images, messages, comments, or any media uploads. Instead of submitting different kinds of content separately, you can upload all types of content in a single service platform.
The process, firstly, identifies glitches, unusual materials, and errors. Then it standardizes and normalizes the content, which matches the policies and aligns with the stylistic features of a certain website or publisher.
For example, it can fix and organize the text and the structure of the content you give it, like cutting out the extra white space and fixing Unicode characters.
Also, it can resize an image to match content guidelines or common standardized image quality. Alongside that, it will convert the file to the required format as well.
Finally, it adds and tags structured metadata with each submission and content, which is essential to verify the content.
So, content ingestion should be the fundamental process in the content moderation pipeline.
Detect Linguistic Style
Detecting language and linguistic style will help you a lot to catch AI spam. Most AI spam has very low-quality LLM system which create generic linguistic patterns that make content look shallow.
These features are pretty obvious for marking content as AI spam. And, since AI generates spams on a large volumes, all of them are pretty much the same.
They mostly use similar vocabulary, sentence structure, and the same length and writing style, which are easily identifiable.
So, you must ensure you implement a process for checking the linguistic styles and language properties.
If the content goes through the linguistic filtering process once, it can identify sophisticated AI-generated spam as well.
Spam Indicator
The primary purpose of AI-generated spam is, most of the time, promoting a product, reaching a large group of people with unsolicited messages, forcing people to subscribe or share something, and ultimately making money.
So, usually, the texts of the spam, being generic at the same time, are very exaggerated and preachy. So, there are some pointers that indicate that a text, message, or a post is AI-generated spam.
There are some platforms and tools for spam detection. These tools easily analyze the spam score and find the signals, pointers, and indicators for spam.
So, if you can apply a spam indicator in your content moderation workflow, you can ensure that the content does not contain AI-generated spam.
Policy Violation Pointer
Content publication and moderation processes should, and usually do, have strict policies and regulations.
These regulations and protocols are usually designed in such a way that when content with AI-generated spam goes through the approval pipeline, it conflicts with the policies. It is considered a violation of the policies.
For example, the policy may include a ban on the use of certain language that might be used in promotional spam. Or it may also include strict restrictions on external suspicious links without disclosure.
So, whenever this kind of content appears in the content pipeline and goes through the policy verification process, the policy disapproves the content.
So, implement solid policies in the pipeline that can catch spam and help you to erase it.
Human Review
Finally, always make sure that there is human oversight in the pipeline to closely analyze and review the content manually before publishing, if all the previous steps missed something.
Humans can find all the nuanced errors, unnatural tone, or slightly paraphrased AI-generated texts that can possibly turn out to be spam.
If you want your content to be perfectly filtered and AI spam-free, do review it on your own or include an expert reviewer in the approval workflow.
How Does AI-Generated Spam Cause Harm?
Building a pipeline or systematic workflow for content review is important anyway. But it is specifically important to catch AI-generated spam.
AI-generated spam can harm you in different ways, especially if you are in the digital publishing business, own a SaaS company, or run an online business.
First of all, it takes away trust from your website or brand when your customers see fake and fabricated reviews, comments with overt praise of your brand, and AI-generated content on your website.
Secondly, AI-generated spam usually contains keyword stuffing, which may annoy readers, and they might not be interested in your website or social media posts.
Thirdly, AI spam is so manipulative that it distracts customers' concentration from the original message of the content. Whether the spam message or comments are meant for your brand or they are promoting other pages, they will surely divert readers' attention.
Fourthly, your competitors may use AI spam to spread a high volume of negative reviews of your brand or website to damage your reputation, which will severely influence your prospects and affect your business. This is perhaps the most concerning issue.
Finally, these types of spam in large amounts harm your originally created content and other creative content creators, affecting the search engine algorithm.
As these spams get mixed with other original content, they sometimes rank higher than the original content. So, the authentic pieces of content are left unnoticed, which badly influences their reach, customer engagement, and revenues.
Conclusion
Evidently, AI spam does not have any positive consequence on anything. Nonetheless, we can not stop AI spams to get generated, as someone will always be responsible for spreading this spam. But what we ourselves can do is verify content to catch it.
Restricting AI spam generation and following all the steps and strategies may be difficult at the beginning. But once you build a solid content moderation pipeline with proper tools and policy enforcement, it will be very easy to get rid of them.
Related DevToolLab Tools
- Keyword Density Checker - measure the keyword stuffing described above, so a submission that repeats a target phrase gets flagged before a human reads it.
- Text Similarity Calculator - compare two submissions and score how alike they are, which is how you catch spam generated in bulk from one template.
- Invisible Character Remover - strip zero-width spaces and hidden Unicode from pasted text during the ingestion step, including the characters used to evade filters.
- Remove Line Breaks - normalize the extra whitespace and stray newlines that the ingestion stage is supposed to clean up before content reaches review.