Tending to Content Concerns Using Moderation in 2022
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- Mike Hanson
- April 21, 2022
The rise of UGC (user-generated content) and the expansion of online platforms that facilitate it has led to the need for content moderation, including the filtering of profanity, sexual content, and hate speech.
UGC is not always safe for consumption. The rise of UGC and the expansion of online platforms that facilitate it has led to the need for content moderation, including the filtering of profanity, sexual content, and hate speech. There are countless examples of UGC that can be harmful. For instance, online forums in which users can post comments have been used to spread misinformation and polarize public opinion on political issues as well as scientific ones. Social media platforms like Twitter have also been used to spread misinformation. This issue is exacerbated by bots (automated social media accounts) that amplify the reach of this false or misleading information.
There are also examples of UGC that can present dangers even when they contain no offensive content. User reviews posted on popular sites like Amazon are not always honest reflections of an individual’s opinions or experiences; for example, businesses may pay “shills” (individuals who provide positive reviews in exchange for compensation) to give glowing reviews about their products or services as part of a marketing strategy designed to boost sales volumes at little cost compared with paid advertising channels such as Google Ads (formerly known as AdWords).
The ubiquity of user-generated content, combined with the increase in collaborative media environments and social networks means that companies are coming up against new problems as they develop their strategies.
One example of this shift is the ubiquity of user-generated content, combined with the increase in collaborative media environments and social networks. This has posed some interesting challenges for companies that want to manage their brand image.
Companies can no longer control the conversation through their content alone. As a result, they need to find other ways of pushing back against negative commentary or activity or trying to influence the tone and direction of conversations.
While there are advantages to having a more open dialogue with customers, it also opens up opportunities for misuse—and holds brands accountable for any issues that arise as a result.
A common approach to determining whether or not a particular piece of content is acceptable is to have humans monitor it manually, but this involves both a huge amount of labor and an increasing number of false positives which can hinder productivity.
A common approach to determining whether or not a particular piece of content is acceptable is to have humans monitor it manually, but this involves both a huge amount of labor and an increasing number of false positives which can hinder productivity. Humans are not only slow and error-prone when performing this task, but they are also highly variable in their judgments. In addition, they tend to get tired quickly and are potentially biased in their judgments. Overall, manual monitoring methods tend to be very expensive.
These issues are compounded because although profanity isn’t suitable for every business and field, there are many where profanity would be considered normal language use.
Content moderators must be able to understand the cultural context in which profanity is used. For example, when I wrote a blog post about bad manners in the workplace and included an image of a coworker that had a rude message on their T-shirt, I didn’t mean to offend anyone. However, this led to some angry comments from people who thought I was being insensitive towards those with an autism spectrum disorder. If content moderation systems were not able to detect sarcasm or humor when using profanity, they could end up flagging such parts of speech as inappropriate content
Another option to manually check your content is using Artificial Intelligence (AI) tools, which can automatically determine if images or text are safe or not, though these may also lead to inappropriate content slipping through the cracks.
AI is becoming increasingly sophisticated, and it can be used to moderate content. AI moderation tools can scan images or text in content that should be flagged. In some cases, this technology may even be able to automatically remove the offending item. However, because AI is not perfect and there are often limitations on what an AI tool can detect, inappropriate content can still slip through the cracks from time to time when using these tools. For example, if you have a photo of your baby wearing a diaper that has been flagged by an AI tool for nudity because it shows the child’s bare bottom, but you know the image is safe and would like it to appear on your site anyway, you may need a human moderator to manually check and approve the image before it can appear online.
In addition to detecting inappropriate content, moderation tools use Artificial Intelligence (AI) which allows them to learn from past experiences so they know better what types of images should be flagged in future situations similar to those that occurred earlier without needing human assistance again after initial training sessions occur where humans are needed periodically until enough examples have found success with avoiding errors using this method over manually checking every time an error occurs due as humans cannot do repetitive tasks such as reading comments or looking through photos without making mistakes themselves eventually leading people tired out after a while which makes them either more prone towards mistakes than when first doing these checks initially since their tiredness level increases each minute spent engaged in this activity causing people susceptible towards feeling fatigued thereby increasing likelihoods for incorrectly flagging something safe then deleting its contents thus resulting in missing important posts like those containing very helpful information about hackers attacking websites so learning how best respond appropriately during such incidents becomes crucial knowledge needed ahead beforehand rather than being caught unprepared while lacking sufficient data collected earlier beforehand.”
AI tools can be trained to recognize the context or intent behind users’ submissions; however, they often misjudge content as well and will flag images with legitimate images being removed while harmful ones pass through undetected.
Although AI tools can be trained to identify content that is deemed inappropriate, they often misjudge context and intent and will flag images as harmful when they are benign or vice versa. This results in legitimate content being removed while harmful content passes through undetected. AI tools lack the ability cannot intent behind users’ submissions, which makes them unreliable tools compared to human monitors.
There is no perfect solution yet but companies should carefully evaluate their needs when deciding on how best to moderate user-generated content so that they’re not losing out due to incorrect assumptions about risk levels associated with certain types of profanity filtering systems.
Using a combination of keyword analysis and ML algorithms, AI tools today can determine whether or not certain words in user-generated content are being used hatefully, profanely, or unprofessionally. They can even give estimates as to how likely it is that the content will be seen as inappropriate.
When a piece of user-generated content is flagged by an AI tool, the business must then decide what to do about it. The first step is to evaluate the flagged content—which may involve looking at other user-generated content in determining if there’s a pattern of abusive behavior.
The final step of moderation involves removing harmful posts or images from the site entirely, once they’ve been identified as such by AI tools. This should be done immediately upon detection so as not to spread misinformation further into your community (and potentially cause harm).