Based on the 2022 Hootsuite research, approximately 4.62 billion users globally are actively engaged on social media, indicating a roughly 10% growth compared to previous years. This uptrend in social media activity underscores the increasing number of users involved in creating, sharing, and exchanging content online as the social media landscape continues to evolve.

As user-generated content proliferates in areas such as e-commerce, news websites, and community forums, the need for AI-driven moderation tools becomes increasingly crucial.

In the digital landscape, disinformation and inappropriate content are pervasive issues, leaving users uncertain about the content's source and how to effectively filter it. Content moderation, a widely employed social media screening practice, serves as a means to approve or reject user-generated comments and content. This task involves the removal of content violating established rules to ensure that published posts align with community guidelines and terms of service. This encompasses offensive, vulgar, or potentially violent audio, video, text, pictures, posts, and comments.

"As content can be created faster, the need to review and moderate content more quickly also increases,"

What is AI Content Moderation?

AI content moderation is a machine learning model. It uses natural language processing (NLP) and incorporates platform-specific data to catch inappropriate user-generated content.

5 distinct AI Content Moderation Methods

• Pre-moderation: Utilizing Natural Language Processing (NLP), businesses can proactively analyze content for adherence to guidelines, automatically rejecting material that contains offensive or threatening language. This method minimizes the reliance on human moderators by swiftly identifying and handling content that violates preset criteria.
• Post-moderation: This approach allows users to post content in real-time without undergoing pre-moderation. Subsequently, a moderator reviews the content after it's posted. Users may encounter content that breaches community guidelines before a moderator intervenes to block or remove it.
• Reactive moderation: In this method, users take on the role of moderators, evaluating posts against their community standards to determine compliance. This crowd-sourced approach leverages the community itself for content moderation, reducing the necessity for dedicated human moderators.
• Distributed moderation: Similar to reactive moderation, this strategy involves users voting to decide whether a post aligns with or violates community standards. Posts with more positive votes gain greater visibility, while those reported by users as violations are more likely to be restricted from view.
• User-only moderation: Users are empowered to filter out content they consider inappropriate, and moderation is limited to registered and approved users. For instance, if several registered users flag a post, the system automatically blocks it from being visible to others.

AI Content Moderation Used in Famous Social Media Platforms

Famous platforms such as Facebook, YouTube and Tiktok use AI-powered content moderation to block graphic violence and sexually explicit/pornographic content. As a result, they managed to improve their reputation and expand their audience. However, as the problematic content grows both in volume and severity, international organizations and states are concerned about the impact of such content on users and moderators.

Among the main concerns are the lack of standardization, subjective decisions, poor working conditions of human moderators, and the psychological effects of constant exposure to harmful content. In response to these critical issues that traditional content moderation has raised, automated practices are in active use to make social media safe and responsible.

Type of Contents

• Text Content: Moderating text contents employs Natural Language Processing (NLP) algorithms to comprehend the meaning and emotions conveyed in text. Techniques like sentiment analysis categorize text into emotions such as bullying, anger, or sarcasm and further label it as positive, neutral, or negative. Name-Entity Recognition (NER) identifies names, locations, and companies, providing insights into brand mentions and user demographics.
• Voice Content: voice analysis technology utilizes AI solutions to translate voice to text, apply NLP and sentiment analysis, and interpret the tone of voice.
• Image Content: This combines text classification and vision-based search techniques. Algorithms detect harmful images, pinpointing the specific location of offensive content. Image processing algorithms identify areas within images, categorizing them based on criteria. Object character recognition (OCR) moderates text within images. These techniques detect offensive language and objects in unstructured data. Approved content is published, while flagged content undergoes manual moderation.

The dilemma surrounding user-generated content is nuanced, with both positive and negative aspects. On one side, it serves as a valuable platform for community members to express opinions, share knowledge, and voice concerns. Conversely, the manual moderation of this content can be daunting and resource-intensive.. For instance, every minute sees 240,000 images shared on Facebook, 65,000 images posted on Instagram, and 575,000 tweets on Twitter, underscoring the sheer volume of content that requires review.The urgency of data moderation aligning closely with real-time considerations is crucial. This alignment is necessary to effectively protect users from harmful content while still enabling meaningful interactions within the online community. In essence, finding a balance between allowing the free expression of ideas and safeguarding against potential harm is a continuous challenge in the dynamic landscape of user-generated content.

Gillespie, T. (2020). Content moderation, AI, and the question of scale. Big Data & Society, 7(2), 205395172094323. https://doi.org/10.1177/2053951720943234

Feerst, A. (2022, September 8). The use of AI in online content moderation - Digital Platforms and American Life: A project by the American Enterprise Institute. Digital Platforms and American Life: A Project by the American Enterprise Institute. https://platforms.aei.org/the-use-of-ai-in-online-content-moderation/

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