We have fact-checking coverage in 23 official EEA languages: Bulgarian, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, German, Greek, Hungarian, Italian, Latvian, Lithuanian, Norwegian, Polish, Portuguese, Romanian, Slovak, Slovenian, Spanish and Swedish.
We have fact-checking coverage in a number of other European languages or languages used in Europe which affect European users, including Georgian, Russian, Turkish, and Ukrainian and we can request additional support in Azeri, Armenian, and Belarusian.
In terms of global fact-checking initiatives, we currently cover more than 60 languages and 130 markets across the world, thereby improving the overall integrity of the service and benefiting European users.
In order to effectively scale the feedback provided by our fact-checkers globally, we have implemented the measures listed below.
- Insights reports. Our fact-checking partners provide regular reports identifying general misinformation trends observed on our platform and across the industry generally, including new/changing industry or market trends, events or topics that generated particular misinformation or disinformation.
- Proactive detection by our fact-checking partners. Our fact-checking partners are authorised to proactively identify content that may constitute harmful misinformation on our platform, which our moderators assess against our Community Guidelines, and suggest prominent misinformation that is circulating online that may benefit from verification.
- Fact-checking guidelines. Where relevant, we create guidelines and trending topic reminders for our moderators which are informed by previous fact checking assessments. This helps our teams leverage the insights from our fact-checking partners and supports swift and accurate decisions on flagged content regardless of the language in which the original claim was made.
Moderation teams working dedicated misinformation queues receive enhanced training on our misinformation policies and have access to the above-mentioned tools and measures, which enables them to make accurate content decisions across Europe and globally.
We place considerable emphasis on proactive detection to remove violative content and reduce exposure to potentially distressing content for our human safety experts. Before content is posted to our platform, it's reviewed by automated moderation technologies which identify content or behavior that may violate our policies or For You feed eligibility standards, or that may require age-restriction or other actions. While undergoing this review, the content is visible only to the uploader.
If our automated moderation technology identifies content that is a potential violation, it will either take action against the content or flag it for human review. In line with our safeguards to help ensure accurate decisions are made, automated removal is applied when violations are more clear-cut.
Some of the methods and technologies that support these efforts include:
- Vision-based: Computer vision models can identify objects that violate our Community Guidelines, such as weapons or hate symbols.
- Audio-based: Audio clips are reviewed for violations of our policies, supported by a dedicated audio bank and "classifiers" that help us detect audios that are similar or modified to previous violations.
- Text-based: Detection models review written content like comments or hashtags, using foundational keyword lists to find variations of violative text. Artificial Intelligence (AI) that can interpret the context surrounding content—helps us identify violations that are context-dependent, such as words that can be used in a hateful way but may not violate our policies by themselves. We also work with various external experts, like our fact-checking partners, to inform our keyword lists.
- Similarity-based: "Similarity detection systems" enable us to not only catch identical or highly similar versions of violative content, but other types of content that share key contextual similarities and may require additional review.
- Activity-based: Technologies that look at how accounts are being operated help us disrupt deceptive activities like bot accounts, spam, or attempts to artificially inflate engagement through fake likes or follow attempts.
- LLMs: We use multimodal LLMs to help moderate content faster and more consistently at scale, from taking automated action on activity like fake engagement, to empowering teams with better moderation tools and risk insights.
- Content Credentials: We launched the ability to read Content Credentials that attach metadata to content, which we can use to automatically label AI-generated content that originated on other major platforms.
Continuing to leverage the fact-checking output in this way enables us to further increase the positive impact of our fact checking programme.
