The digital world is constantly evolving, and one catalyst that differentiates this platform from the others is user-generated content. Although companies worldwide have their websites and dedicated social media presence, users are more likely to trust the opinions of their fellow customers than go by the words of the business.
More than 4.26 billion people were active social media users in 2021. A number predicted to touch the 6 billion mark by 2027. The amount of content generated, captured, shared, and consumed on a global scale touched 64.2 zettabytes in 2020.
With new content being generated and consumed at a staggering pace, it has become essential that brands keep tabs on the content hosted on their platforms. Online platforms should be and remain a safe environment for their users.
[Also Read: Understanding Automated Content Moderation]
What is Content Moderation and Why?
User-generated content propels social media platforms, and content moderation refers to screening this content for inappropriate or offensive posts. Business and social media platforms have a specific standard for monitoring their hosting content.
The guidelines could include anything from violence, extremism, hate speech, nudity, copyright infringement, or anything offensive. The posted content will be flagged and removed if it doesn’t satisfy the standard.
The idea behind content moderation is to ensure the content is in-tune with the brand’s ideals and upholds the values of decency, trust, and safety.
Content moderation is crucial for businesses to maintain business standards, brand image, reputation, and credibility. Every second, the staggering amount of user-generated content posted on platforms makes it challenging for brands to keep out offensive and inappropriate content, text, videos, and images. Content moderation strategy helps brands maintain their image while allowing users to express themselves and shutting down offensive, explicit and violent content.
Which content types can you moderate?
Content moderation algorithms generally deal with three or a combination of these content types.
Text
The sheer amount of text– from comments to full-length articles – that needs moderation is quite staggering. Text posts are available almost anywhere in the form of comments, articles, forum posts, social media discussions, and other postings.
Text content moderation algorithms should be able to scan the text of different lengths and styles for unwanted content. Moreover, text moderation can be a difficult task owing to the complexities of language and cultural nuances.
Images
Image moderation is much simpler than text moderation, but it is essential to have proper guidelines or standards in place.
In addition, since cultural differences might come into play when moderating images, it is crucial to thoroughly understand and connect with the user community in several geographical locations.
Videos
Moderating video content is very difficult, as moderating videos can be time-consuming, unlike text or images. The moderator must watch the entire video before deeming it fit or unfit for consumption. Even if only a few frames in the video are explicit or disturbing, it will force the moderator to remove the entire content.
Live Streaming
Live streaming is, perhaps, the most challenging content to moderate. It is because video and accompanying text moderation have to happen simultaneously with the streaming.
How Does Content Moderation Work?
To get started on moderating the content on your platform, you should first put in place standards or guidelines that determine inappropriate content. These guidelines help moderators flag content for removal.
Define the sensitivity level or threshold content moderators should consider when reviewing content. The threshold should be defined based on your brand, the type of business, user expectations, and location.
Types of content moderation
You can choose from many moderation processes for your brand needs and user consent. Some of them are:
Pre-Moderation
Before the content is displayed on your site, it is in the queue for moderation. Only after the content is reviewed and deemed fit for consumption is it published on the platform. Although this is a safe method of blocking explicit content, it is time-consuming.
Post-Moderation
Post-moderation is the standard method of content moderation where there is a trade-off between user engagement and moderation. Although users are allowed to post their submissions, it is still queued for moderation. If the content has been flagged, it is reviewed and removed. Businesses strive to achieve a shorter review time so that inappropriate content isn’t allowed to stay online for too long.
Reactive Moderation
In reactive moderation, the user community is encouraged to flag inappropriate content that violates community rules and guidelines. In this method, the community’s attention is drawn to the content that needs moderation. However, the offensive content might stay on the platform for more extended periods.
Distributed Moderation
In a distributed moderation method, the online community can review, flag, and remove content they find offensive and against guidelines using a rating system.
Automated Moderation
As the name suggests, automated moderation uses various tools and systems to flag words or phrases and reject submissions. It works by filtering out certain banned words, images, and videos using machine learning algorithms.
Although technology-powered moderation is becoming prevalent, human moderation in review cannot be disregarded. Businesses, ideally, use a combination of automated tools and human moderators, at least for complex situations.
[Also Read: Case Study – Content Moderation]
How does Machine Learning help content moderation?
With more than 5 billion people using the internet and over 4 billion active on social media networks, it is not easy to be astonished at the sheer number of images, text, videos, posts, and messages generated daily. This mammoth content must be moderated in some way so that users accessing their social media sites can have a pleasant and enriching experience.
Content moderation came into being as the solution to remove content that is explicit, offensive, abusive, scamming, or against the brand ethos. Traditionally, businesses have relied entirely on human moderators to review online user-generated content published on their platforms. However, depending entirely on human moderators can make the process time-consuming, costly, and inefficient.
Businesses are now employing machine learning algorithms to automatically and efficiently moderate content. AI-powered content moderation has made the entire process efficient, faster, consistent, and cost-effective.
Although this process doesn’t eliminate the need for human moderators – human-in-the-loop, the contribution of human moderators helps deal with complex issues. Moreover, human moderators better understand language nuances, cultural differences, and context. When automated tools are used, with help from human moderators, it reduces the psychological impact of exposure to triggering content.
Challenges of Content Moderation
The main challenge of developing a content moderation algorithm is the need for speed, the ability to handle large data volumes, and maintaining accuracy. In addition, developing such a model needs large quantities of data. Yet, such data is challenging as most of the digital platforms’ content databases become the companies’ property.
Another major challenge when it comes to developing an accurate content moderation algorithm is language. A reliable content moderation application should be able to recognize several languages and understand cultural nuances, social contexts, and linguistic dynamism.
Since a language goes through several changes over time, as certain words that were innocent yesterday could have earned notoriety today – the ML model needs to keep pace with the changing world. For example, a nude painting could be explicit and voyeuristic or simply art.
How a piece of content is perceived or deemed inappropriate depends on the context. And it is crucial to have consistency and standards within your platform so that your users can trust your moderation efforts.
A typical user always tries to find loopholes in your guidelines and bypass moderation rules. However, your ML algorithm should be able to evolve with the changing times continuously.
Finally, it is the question of bias. Diversifying your training database and training models to detect context is critical. Although developing a reliable content moderation algorithm might seem challenging, it starts with getting your hands on high-quality training datasets.
Third-party vendors with the right expertise and experience in delivering adequate training datasets are the right places to begin.
Every business with a social presence needs a cutting-edge content moderation solution that helps build customer trust and impeccable customer experience. To build the application and train your machine learning model, you need access to a high-quality database devoid of bias, aligned with the latest linguistics and market-specific content trends.
With our years of experience helping businesses launch AI models, Shaip offers comprehensive data collection systems catering to diverse content moderation needs.
