機械学習がコンテンツ変更を最適化する方法
There are more than 4.5 billion internet users, and this number is growing every day. The internet generates billions of images and videos as well as messages and posts. These users are looking for a positive, safe experience on their favorite social media platforms and online retailers. The solution is content moderation. It removes data that is explicit, abusive or fake, fraudulent, harmful, or not compatible with business.
Companies used to rely on human content moderators for content moderation. However, as content usage grows, this approach is not cost-effective nor efficient. Instead, organizations are investing in machine learning (ML), strategies to create algorithms that automatically moderate content.
Artificial intelligence (AI) allows online businesses to scale more quickly and ensure consistency in content moderation. While it doesn’t eliminate human moderators (humans-in-the loop), they can still provide ground truth monitoring and be able to handle more nuanced, contextual content issues. It does decrease the number of content moderators required to review content. This is a good thing because unwanted exposure to harmful material can have an adverse effect on your mental health. This task can be left to machines, which is a benefit for both the company and its employees.
Moderating Content in the Real World
Companies use ML-based content moderating for various digital media uses, including chatbots and chatrooms. Online retail and social media are two of the most popular applications.
Social Media
Social media is plagued by a content problem. Facebook alone boasts over 2 billion users, who collectively watch over 100 million hours of video per day and upload more than 350 million photos each day. It would take a lot of time and money to hire enough people to manually check the volume of content that is being created by this traffic. AI reduces the burden by checking text, usernames and images for hate speech and cyberbullying. It also checks for explicit or harmful material, spam, fake news and other misleading content. The algorithm can also delete or ban users who don’t conform to a company’s terms.
Online Shopping
Social platforms aren’t the only ones that need content moderation. Online retailers can also use content moderation tools in order to present quality content that is business-friendly to their customers. For example, a hotel booking site may use AI to scan all images of hotel rooms and remove any that do not comply with site rules (e.g. no persons can be seen in a photograph). Retailers can also use a combination ML and AI techniques to customize their products.
How does content moderation work?
Companies will have different content queues and escalation policies for ML-based review system systems. However, they will generally include AI moderation at step one, two, or both.
Pre-moderation. AI modifies user content before it is posted. Users can then see content that has been deemed not to be harmful. The AI model will remove content that has a high likelihood of being harmful or unfriendly to business. The AI model will flag content that it believes is not reliable or business-friendly for human review if it has low confidence in its predictions.
Post-moderation. Post-moderation. If the AI reviews the content, it will use the same process as step 1, automatically deleting harmful material.
AI can use a variety of ML techniques depending on the media to predict content.
Text
Natural language processing (NLP): Computers rely on NLP to understand human speech. To remove unfavorable languages, they may use keyword filtering.
Sentiment analysis: The internet is all about context. Computers can use sentiment analysis to identify tones such as anger or sarcasm.
Knowledge bases: Computers are able to use databases of information to predict which articles will be fake news and identify common scams.
Image and Video
Object detection: Images and videos can be used to identify objects such as nudity in photos or videos that do not meet the platform standards.
Scene understanding: Computers are able to comprehend the context of what is ラベル付け in a scene and make more informed decisions.
All Data Types
Companies may use user trust technology, regardless of the data type. Computers can classify users who have a history of spamming or posting explicit content as “non-trusted” and will be more vigilant about any content they post in the future. Fake news is also dealt with by reputation technology: Computers are more likely than ever to identify unreliable news sources and label them as false.
Content moderation is a constant source of new training data. A computer will route content to a human reviewer who will then label it as harmful or not and feed the labeled data back into the algorithm for future improvement.
How to Overcome the Challenges of Content Moderation?
AI models face many challenges in content moderation. Due to the sheer volume of content, it is necessary to create fast models that don’t compromise accuracy. Data is what makes it difficult to create an accurate model. Because most of the data collected by companies is kept as their property, there are very few public content datasets available for digital platforms.
Language is another issue. Your content moderation AI must be able to recognize multiple languages and the contexts in which they are used. The internet is global. Your model must be updated regularly with new data as language changes over time.
There are also inconsistencies in definitions. What is cyberbullying? To maintain trust and confidence in moderation, it is important that these definitions are consistent across your platform. Users are always creative and will find loopholes in moderation. You must constantly retrain your model in order to eliminate fake news and scams.
Be aware of biases in content moderation. Discrimination can occur when language or user characteristics are involved. To reduce bias, diversifying your training data will be crucial. This includes teaching your model how to understand context.
It can seem impossible to create an effective content moderation platform with all these obstacles. It is possible to succeed: Many organizations turn to third party vendors to provide enough training data and a group of international individuals to label it. To deliver scalable and efficient models, third-party partners can also provide the necessary expertise in ML-enabled Content Moderation Tools.
The Real World dictates Policy: Content moderation decisions should be based on the policy. However, policy must quickly evolve to address any gaps, gray areas or edge cases that may arise, especially for sensitive topics. Monitor market trends and make recommendations to improve policy.
Manage Demographic Bias. Content moderation is more effective, reliable, trustworthy, and efficient when the moderators are representative of the overall population in the market being moderated. You must define the demographics and manage diversity sourcing to ensure that your data isn’t subject to any demographic bias.
Create a Quality Management Strategy with Expert Resources. Content moderation decisions can be scrutinized in today’s political climate. A comprehensive strategy is essential for identifying, correcting and preventing errors. We are often able to recommend and help clients implement a strategy that is tailored to their specific needs. This includes developing a team of policy experts and establishing quality control review hierarchies.
What Labelify can do for you
We have over 4 years experience helping companies to build and launch AI models. We are proud to offer data classification pipelines that will help you with your content moderation requirements. Our proprietary quality control technology delivers high accuracy and precision. It is supported by our platform features and expertise to ensure that you can achieve rapid delivery and scalability.
Find out more about our expertise, and how we can assist you with your content moderation needs.
