With automated content moderation, your platform gets pre-screened by an algorithm and only brings in human moderators for the most severe content. This allows for a faster growth and preservation of your Trust and Safety standards.
However, the coronavirus pandemic has shown that relying solely on automation is not an easy solution. So how does it work?
Using automated tools, platforms can automatically flag content that may violate the terms of service. This allows them to remove harmful material from their platforms before it can reach users.
This can be done by detecting illegal or harmful elements in images, text, videos and even live streams. A number of different AI algorithms can be leveraged, depending on the platform’s needs.
For example, computer vision can detect specific words or symbols and recognize their position within the image to spot unwanted content. Sentiment analysis can also help identify the tone of text and check its compliance with content moderation policies.
However, it is important to remember that there are limitations to this type of automated tool. For instance, automated technology can be biased and misunderstand context or rely on a limited set of examples to make decisions. This can lead to stigmatization and silencing already disproportionately subjected communities online. It is therefore necessary to ensure that the technology only makes a decision when it is truly necessary and that human review remains in place.
Natural Language Processing
Artificial intelligence content moderation tools allow for quicker, more accurate, and cost-effective options for screening user-generated content against community guidelines. These solutions help prevent the risk of legal and reputational damage caused by harmful or inappropriate material.
Automative content moderation software recognizes and responds to images, video, and text. Using Alibaba’s years of Big Data analytics, this technology can detect content related to violence, terrorism, drugs, and more. It automatically flags and forwards content for human review or removes it from the platform entirely.
Text moderation AI features use natural language processing to filter text, including emojis, for offensive words and phrases. It also uses a technique called topic modeling to find the most relevant topics within a piece of text. This is similar to how search engines rank websites. Lastly, image content moderation automation leverages image recognition, along with vision-based searching and tagging algorithms to identify unwanted elements in a picture. Those items are then forwarded to manual moderation.
Sentiment analysis can be a useful tool for a company’s content moderation strategy, allowing companies to gain a better understanding of how their audience feels about their products. For example, a racial slur or an image of a naked person could damage a company’s reputation if not moderated in time.
Sentiment analysis uses Natural Language Processing to identify a piece of writing’s tone, which can be positive, neutral, or negative. To do this, sentiment analysis software uses libraries of adjectives that have been manually scored beforehand. For example, words such as “bad” and “horrible” have a similar score, but they can have different meanings depending on the context in which the word is used.
In customer service and call center applications, sentiment analysis is useful for identifying how customers feel about a product or experience. It can also be used to determine a customer’s satisfaction level and help companies improve their product and service. However, this type of data can be difficult to collect in a scalable manner.
In the wake of public pressure, CEOs of major platforms have made promises that they will employ automated software to spot hate speech, threats, or pornography more quickly and fairly than humans. But AI alone is not enough to handle the enormous volumes of UGC that are posted online at any given moment.
A strategy that uses a combination of AI techniques to prescreen content is the most effective way for businesses to scale their operations and keep user engagement high. In this model, human moderators are involved only after a machine has identified potentially harmful or business-unfriendly content, which is then flagged for their review. This approach saves moderator hours and allows for rapid responses to damaging or offensive content. But it can also be difficult to train the algorithms to understand nuances in language, visuals, and cultural norms. That is why it is important for human moderators to be provided with contextual data about specific violations.