Content moderation is one of the major aspect of managing online platforms and communities. It englobes the review, filtering, and approval or removal of user-generated content to maintain a safe and engaging environment. In this article, we’ll provide you with a comprehensive glossary to understand the key concepts, as well as its definition, challenges and best practices to follow.

What is Content Moderation ? 

Content moderation is the strategic process of evaluating, filtering, and regulating user-generated content online. It helps creating a safe and positive user experience by removing or restricting content that violates community guidelines, is harmful, or could offend users. An effective moderation system is designed to find a balance between promoting freedom of expression and protecting users from inappropriate or harmful content. The European Commission adopted a recommendation on measures to effectively tackle illegal content online.

Key Terms in Content Moderation

To better understand the field of content moderation, we will now explore some key terms and concepts:

API

API stands for Application Programming Interface. It allows different programs to communicate and share information, enabling platforms to integrate and interact with external services or applications.

Automated & AI-powered Moderation

Automated and AI-powered moderation relies on algorithms and artificial intelligence to analyze and filter content. This approach uses image recognition, natural language processing, and other automated content analysis techniques to identify and remove inappropriate content.

Automation Rate

Automation rate refers to the extent to which content moderation tasks can be automated. Platforms can achieve higher automation rates by using AI technology and automated tools to filter and moderate content efficiently.

Average Reviewing Time (ART)

Average Reviewing Time (ART) measures the average time it takes for a piece of content to be reviewed by human moderators. Balancing the reviewing time is necessary to maintain a balance between speed and accuracy in content moderation.

Balancing Free Speech and Content Restrictions

Content moderation involves finding a balance between allowing freedom of expression and maintaining a safe and respectful environment. Platforms must enforce content policies to prevent harmful or inappropriate content while respecting users right to express themselves.

Code of Conduct

A code of conduct outlines the ethical guidelines and rules of behavior for users on a platform. It typically includes policies on respectful behavior, non-discrimination, and other ethical considerations.

Community Guidelines

Community guidelines are rules and expectations that outline acceptable behavior and content standards for platform users. These guidelines help maintain a positive and inclusive online community.

Content Policies

Content policies define what types of content are allowed or prohibited on a platform. These policies dictate what users can write, post, and share, including guidelines on hate speech, explicit content, harassment, and other inappropriate content.

Copyright Infringement

Copyright infringement refers to the unauthorized use of copyrighted material without the permission of the copyright owner. Platforms must moderate and remove content that infringes on copyright laws to avoid legal implications.

Decentralized Moderation

Decentralized moderation refers to the distribution of moderation tasks across a network of users or community members. This approach can involve peer-to-peer networks or blockchain technology to ensure moderation is not only controlled by a central authority.

False Positive

A false positive refers to an alert or moderation action that incorrectly identifies content as violating platform policies. It can occur when automated moderation tools or algorithms mistakenly flag content as inappropriate.

Filters

Filters play a crucial role in content moderation by automatically identifying and removing inappropriate content, such as hate speech or explicit images. Filters help prevent such content from reaching the platform’s audience.

Hate Speech and Harassment

Hate speech refers to offensive, threatening, or discriminatory speech that targets individuals or groups based on characteristics such as race, gender, religion, or ethnicity. Harassment involves targeted attacks on individuals or groups and is also considered inappropriate content.

Human Moderation

Human moderation involves the manual review and moderation of content by human moderators. It can include a team of moderators who review flagged content, monitor for inappropriate activity, and enforce platform policies.

Image Recognition

Image recognition technology can analyze and classify images. In content moderation, it is used to identify and remove inappropriate or explicit images. It can also be used to approve relevant content, such as images of people in appropriate contexts.

Inappropriate Content

Inappropriate content refers to any content that violates a platform’s community guidelines or terms of service. It can include hate speech, harassment, explicit content, or any other content that goes against platform policies.

Machine Learning

Machine learning is a type of artificial intelligence that allows software to learn and improve over time without explicit programming. In content moderation, machine learning can be used to improve the accuracy and efficiency of automated moderation tools.

Manual Moderation

Manual moderation involves human moderators reviewing and moderating content. This can include reviewing flagged content, monitoring for inappropriate activity, and enforcing platform policies.

Misinformation and Fake News

Misinformation refers to false or inaccurate information that is spread intentionally or unintentionally. Fake news includes hoaxes, conspiracy theories, and other forms of misinformation. Content moderation helps prevent the spread of misinformation.

Natural Language Processing (NLP)

Natural Language Processing (NLP) is a technology that analyzes and understands human language. In content moderation, NLP is used to identify and remove inappropriate language, hate speech, and other violations of platform guidelines. It helps machines understand the nuances and context of human communication.

Platform-generated Content

Platform-generated content refers to content that is generated by the platform or website itself. This can include automated posts, system-generated messages, and advertisements.

Take-down

Take-down refers to the action of removing content or a user from a platform. Platforms can take down content or suspend users who violate community guidelines or engage in inappropriate behavior.

Terms of Service

Terms of Service are the legal agreements that users must agree to in order to use a platform. These agreements outline the terms and conditions of platform usage and the consequences for violating them.

Types of Content to Moderate

Content moderation englobes various types of user-generated content. Let’s explore some of the most common types and their unique features:

Text moderation

Text-based user-generated content includes written content created by users and shared with others. This can range from blog posts and social media updates to comments on forums and reviews of products or services.

Examples of user-generated text:

• Entries on forums and communities
• User-profile presentations
• Reviews of a product or service
• Item descriptions on marketplaces
• Comments from users on other pieces of content

Text moderation involves reviewing and evaluating textual content to ensure compliance with platform guidelines. Challenges in text moderation include identifying hate speech, abusive language, and harmful content that may not always be explicit. AI-driven natural language processing (NLP) technologies have significantly improved the accuracy and efficiency of text moderation, helping platforms proactively detect and remove problematic content.

Image moderation

Images play a crucial role in enhancing user engagement and visual appeal on online platforms.

Examples of images to moderate:

• User-profile pictures and videos
• Product pictures
• Visualization for sharing economy services
• Endorsements for products and services
• Reviews with accompanying images

Image moderation involves reviewing and filtering images to ensure they comply with platform guidelines and legal requirements.

Video Moderation

User-generated videos can be entertaining, informative, or even harmful.

Examples of videos to moderate:

• Graphic violence: Videos depicting violence, fights, assaults, etc.
• Sexual content: Videos with explicit or adult content that may not be suitable for all viewers.
• Copyright infringements: Videos that use copyrighted content without permission.
• Self-harm: Videos where users threaten to harm themselves or others.

Video moderation involves reviewing and removing videos that violate platform policies or contain explicit or violent content. The challenges in video moderation include identifying inappropriate or harmful content within videos, understanding visual context, and addressing emerging threats in real-time. Advanced computer vision and machine learning technologies are key to effective video moderation, allowing platforms to accurately identify and remove harmful videos swiftly.

Audio moderation

User-generated audios have increased in the past years and could be the subject of harmful content.

Examples of audios to moderate if harmful:

• Podcasts
• Voice messages
• Audio comments

Audio moderation focuses on evaluating and filtering audio content, including voice messages and audio comments. The challenges in audio moderation include identifying offensive language, hate speech, and other harmful content within the audio. AI-powered voice recognition and sentiment analysis technologies play a vital role in enhancing audio moderation accuracy, enabling platforms to monitor and manage audio content more effectively.

Types of Content Moderation

Content moderation can be categorized into different types based on the timing and approach of the moderation process. Let’s explore the most common types of content moderation:

Pre-moderation

Pre-moderation involves reviewing and approving content before it is published on a platform. Human moderators carefully evaluate each submission to ensure it complies with platform guidelines and policies.

Post-moderation

Post-moderation refers to reviewing and moderating content after it has been published on a platform. Users can flag or report inappropriate content, which is then reviewed and removed if necessary by human moderators.

Reactive Moderation

Reactive moderation is carried out in response to user reports or complaints about specific content. Human moderators review reported content and take appropriate action based on the platform’s policies and guidelines.

Proactive Moderation

Proactive moderation involves actively monitoring and detecting inappropriate content before it is reported. This can include the use of automated tools and artificial intelligence to flag and remove content that violates platform guidelines.

Distributed Moderation

Distributed moderation refers to the decentralization of content moderation across a network of users or community members. This approach allows users to report and moderate content within their communities, reducing the load on centralized moderation teams.

Moderation Metrics and Key Performance Indicators (KPIs)

Moderation metrics are a range of quantitative and qualitative measurements that evaluate the effectiveness of content moderation efforts like : user-reported issues, response times and the accuracy of content removal decisions. These metrics help platforms ensure a safe and inclusive online environment.

Key Performance Indicators, on the other hand, are specific, measurable values that gauge the overall performance and success of moderation strategies. KPIs may include user satisfaction, the reduction of harmful content, and the mitigation of potential legal risks.

By tracking these metrics and KPIs, organizations can refine their moderation approaches, favorise user experience, and create a digital space that aligns with community standards and expectations.

User Engagement and Communication

User engagement is the level of interaction and participation users have with the content, features, and discussions on a platform. It englobes actions such as : likes, comments, shares and contributions to discussions. It reflects how deep is the connection between users and the platform.

Effective communication means clear and timely exchange of information between the platform and its users such as : announcements, updates, and responses to user inquiries or feedback.

Strong user engagement and communication participates into giving a sense of community, loyalty, and trust among users. It creates a positive and dynamic online environment.

Challenges in Content Moderation

Scale and Volume

Online platforms generate an overwhelming amount of user-generated content daily. Managing such vast volumes manually poses significant challenges and requires strong moderation strategies.

Contextual Nuances

Automated moderation tools may struggle to comprehend the subtle nuances of certain content, leading to potential over- or under-censorship. Context plays a vital role in accurately assessing the appropriateness of content, and striking this balance is a complex challenge.

Emergent Threats

As the digital landscape evolves, new forms of harmful content continually emerge, making it challenging for moderation systems to adapt and stay ahead of emerging threats.

Balancing Freedom of Expression

Platforms must navigate the delicate balance between upholding freedom of speech and curbing hate speech, misinformation, or content that poses potential harm to users.

Managing False Positives and Negatives

False positives occur when a system incorrectly identifies something as a positive result, such as flagging legitimate content as inappropriate.

False negatives happen when the system fails to detect actual positive instances, allowing inappropriate content to go unnoticed.

The best way to avoid those errors is to :

• Monitor continuously
• Fine-tune the algorithms,
• Using user feedback for a more performant system.

Legal and Ethical Considerations

Platforms must respect the legal consideration and comply with laws related to defamation, hate speech, intellectual property, and privacy.

Creating a balance between freedom of expression and preventing harm is a hard ethical challenge for content moderation teams. Here are the ways to make this easier:

• Transparency in content removal processes
• Clear community guidelines
• Consistent enforcement are critical ethical considerations.
• Content moderation efforts should align with legal requirements and ethical standards.
  

Best Practices in Content Moderation

Utilizing Automation and AI

Incorporating automated moderation tools and AI algorithms enables platforms to efficiently identify potentially harmful content, saving time and resources. Automated systems can quickly flag and prioritise content for further review by human moderators.

Robust Guidelines and Training

Establishing clear and comprehensive moderation guidelines is essential for ensuring consistent and fair evaluations. Regular training for human moderators is also crucial to enhance their judgement and understanding of platform policies.

Proactive Moderation

Emphasising proactive content monitoring allows platforms to identify and address potential issues before they escalate, safeguarding user safety and platform reputation.

User Reporting Mechanisms

Providing users with accessible and user-friendly reporting mechanisms empowers them to contribute to moderation efforts. Quick and efficient reporting helps platforms identify and respond to problematic content promptly.

Case Studies of Content Moderation

• The Reddit case :

Reddit is a special case because for this website content moderation goes beyond language barriers. Reddit has to moderating content across different languages and communities. A case study examines a dataset of 1.8 million Reddit comments in English, German, Spanish, and French. This show the need for cross-lingual transfer learning, addressing human biases in label noise, and developing adaptive moderation models. This study shows the challenges of auto moderation and the opportunities for improvement.

• The Youtube case :

YouTube has faced numerous challenges in content moderation. Creators often complain about the lack of transparency and consistency in the platform’s appeals process. Content takedowns and strikes can have significant consequences for creators, potentially leading to channel suspension. One case study in particular show the challenges faced by YouTube creators in appealing strikes and the inconsistent decision-making process.

This specific incident involved popular YouTube creators MoistCr1TiKaL and Markiplier, both received strikes for reacting to a viral video. While the strikes were initially upheld, the online backlash and pressure from fans led YouTube to reverse its decision and issue an apology. This case study raises questions about the necessary elements of an appeals process and the conditions under which off-platform attention can influence content moderation decisions.

The Evolution of Content Moderation

Content moderation has evolved through the years with the advancements in technology and the need to adapt to new challenges. From manual review processes to the integration of sophisticated AI-powered systems: the main goal in the evolution of content moderation is to achieve higher efficiency, accuracy, and adaptability.

AI and machine learning algorithms have played a major role in improving moderation capabilities. AI algorithms can now learn from past moderation decisions by analysing patterns and data, resulting in more accurate identification and removal of harmful content. This evolution helps platforms to continuously refine their content moderation processes and respond more effectively to emerging threats.

Future Trends in Content Moderation

Here is the list of the future trends coming in content moderation :

• Artificial intelligence and machine learning : This will help create a more sophisticated automated content analysis and moderation.
• Explainable AI models : necessary to provide transparency and accountability in moderation decisions.
• Context-aware moderation : with the online spaces growing, the focus is going to be on recognizing the nuances of language and cultural differences.
• Decentralized and blockchain-based moderation solutions : this will give a more transparent and resistant approach to censorship.
• User empowerment : platforms will be giving users more control over their content visibility and moderation preferences.
• Responsible and user-centric content moderation practices : ethical considerations, bias mitigation, and user privacy will be the main concerns.

The future of content moderation is expected to be dynamic, marked by cutting-edge technology, user-centric approaches, and a commitment to ethical standards.

Checkstep’s Content Moderation Solutions

Checkstep’s moderation solutions are engineered to address the challenges faced by platforms in content management with precision and efficacy. By combining advanced AI capabilities with human expertise, Checkstep’s solutions offer a comprehensive approach to moderation.

Advanced AI and Automation

Checkstep harnesses the power of AI and automation to efficiently review and filter large volumes of user-generated content. Checkstep’s AI can quickly identify potentially harmful materials, enabling human moderators to focus on complex cases that require nuanced judgment.

Contextual Understanding

Checkstep’s AI is equipped with advanced contextual understanding, reducing false positives and negatives. This ensures a balanced approach, respecting freedom of expression while maintaining a safe environment for users.

Regulatory Compliance

Checkstep helps online platforms stay compliant with regulations by providing transparency reporting, streamlining the processing of copyright-related issues, and enabling a fast response to meet the requirements for reporting obligations of online harms.

Easy integration

Checkstep was built by developers for developers. Simple SDKs and detailed API documentation means minimal effort is needed to be up and running.

Team Management

Checkstep’s platform is designed to support large teams of moderators, offering prompts for breaks and additional training support to ensure efficiency and well-being. Checkstep’s solution also caters to multiple roles within the Trust and Safety department, supporting data scientists, head of policy, and software engineers for online harm compliance.

Conclusion

Content moderation is a vital aspect of maintaining a safe, credible, and engaging online environment, especially with the enforcement of the Digital Services Act (DSA). By implementing effective content moderation strategies and leveraging technologies such as AI and machine learning, platforms can ensure the quality and integrity of user-generated content. Therefore, It is necessary for businesses and organizations to understand the importance of content moderation and adopt best practices to create a positive online experience for their users.

Checkstep’s moderation solutions exemplify the best practices in the industry, offering a seamless blend of advanced AI capabilities and human judgment. Checkstep, by understanding contextual nuances, proactively monitoring content, and empowering users to participate in the moderation process, ensures platforms can effectively balance freedom of expression with user safety, providing a safe digital space for users.

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