DSA Transparency Report - April 2025

Introduction

This report covers the content moderation activities of X’s international entity X Internet Unlimited Company (“XIUC”) (formerly known as Twitter International Unlimited Company (“TIUC”)) under the Digital Services Act (DSA), during the date range 1 October, 2024 to 31 March, 2025.

We may refer to “notices” as defined in the DSA as “user reports” and “reports”.

Description of our Content Moderation Practices

Our content moderation systems are designed and tailored to mitigate systematic risks without unnecessarily restricting the use of our service and fundamental rights, especially freedom of expression. Content moderation activities are implemented and anchored on principled policies and leverage a diverse set of interventions to ensure that our actions are reasonable, proportionate and effective. Our content moderation systems blend automated and human review paired with a robust appeals system that enables our users to quickly raise potential moderation anomalies or mistakes.

Policies 

X's purpose is to serve the public conversation. Violence, harassment, and other similar types of behaviour discourage people from expressing themselves, and ultimately diminish the value of global public conversation. Our Rules are designed to ensure all people can participate in the public conversation freely and safely.

X has policies protecting user safety as well as platform and account integrity. The X Rules and policies are publicly accessible on our Help Center, and we are making sure that they are written in an easily understandable way. We also keep our Help Center regularly updated anytime we modify our Rules.

For the purposes of the summary tables below, the X policy titles in use at the start of the reporting period have been retained, even if they changed throughout the period.

Enforcement 

When determining whether to take enforcement action, we may consider a number of factors, including (but not limited to) whether:

• The behaviour is directed at an individual, group, or protected category of people;
• The report has been filed by the target of the abuse or a bystander;
• The user has a history of violating our policies;
• The severity of the violation; and
• The content may be a topic of legitimate public interest.

When we take enforcement actions, we may do so either on a specific piece of content (e.g., an individual post or Direct Message) or on an account. We may employ a combination of these options. In most cases, this is because the behaviour violates the X Rules.

To enforce our Rules, we use a combination of machine learning and human review. Our systems are able to surface content to human moderators who use important context to make decisions about potential violations. This work is led by an international, cross-functional team with 24-hour coverage and the ability to cover multiple languages. We also have a complaints process for any potential errors that may occur.

To ensure that our human reviewers are prepared to perform their duties we provide them with a robust support system. Each human reviewer goes through extensive training and refreshers, they are provided with a suite of tools that enable them to do their jobs effectively, and they have a suite of wellness initiatives available to them. For further information on our human review resources, see the section titled “Human resources dedicated to Content Moderation”.

Reporting violations

X strives to provide an environment where people can feel free to express themselves. If abusive behaviour happens, we want to make it easy for people to report it to us. EU users can also report any

violation of our Rules or their local laws, no matter where such violations appear.

Transparency

We always aim to exercise moderation with transparency. Where our systems or teams take action against content or an account as a result of violating our Rules or in response to a valid and properly scoped request from an authorised entity in a given country, we strive to provide context to users. Our Help Center article explains notices that users may encounter following actions taken. We promptly notify affected users about legal requests to withhold content, including a copy of the original request, unless we are legally prohibited from doing so. We have also updated our global transparency centre covering a broader array of our transparency efforts. 

Content Moderation Governance Structure

Own Initiative Content Moderation Activities

X employs a combination of heuristics and machine learning algorithms to automatically detect content that we believe violates the X Rules and policies enforced on our platform. We use combinations of natural language processing models, image processing models and other sophisticated machine learning methods to detect potentially violative content. These models vary in complexity and in the outputs they produce. For example, the model used to detect abuse on the platform is trained on abuse violations detected in the past. Content flagged by these machine learning models are either reviewed by human content reviewers before an action is taken or, in some cases, automatically actioned, based on the historical accuracy of the model’s output. Heuristics are typically utilised to enable X to react quickly to new forms of violations that emerge on the platform. Heuristics are common patterns of behaviours, text, or keywords that may be typical of a certain category of violations. Pieces of content detected by heuristics may also get reviewed by human content reviewers before an action is taken on the content. These heuristics are used to flag content for review by human agents proactively.

Testing, Evaluation, and Iteration

Automated enforcements under the X Rules and policies undergo rigorous testing before being applied to the live product. Both machine learning and heuristic models are trained and/or validated on thousands of data points and labels (e.g., violative or non-violative) including those that are generated by trained human content moderators. For example, inputs to content-related models can include the text within the post itself, the images attached to the post, and other characteristics. Training data for the models comes from both the cases reviewed by our content moderators, random samples, and various other samples of pieces of content from the platform.

Use of Human Moderation

Before any given algorithm is launched to the platform, we verify its detection of policy violating content or behaviour by drawing a statistically significant test sample and performing item-by-item human review. Reviewers have expertise in the applicable policies and are trained by our Policy teams to ensure the reliability of their decisions. Human review helps us to confirm that these automations achieve a level of precision, and sizing helps us understand what to expect once the automations are launched.

In addition, humans proactively conduct manual content reviews for potential policy violations. We conduct proactive sweeps for certain high-priority categories of potentially violative content both periodically and during major events, such as elections. Content moderators also proactively review content flagged by heuristic and machine learning models for potential violations of other policies, including our adult content, violent content, child sexual exploitation (CSE) and violent and hateful entities policies.

Once reviewers have confirmed that the detection meets an acceptable standard of accuracy, we consider the automation to be ready for launch. Once launched, automations are monitored dynamically for ongoing performance and health. If we detect anomalies in performance (for instance, significant spikes or dips against the volume we established during sizing, or significant changes in user complaint/overturn rates), our Engineering (including Data Science) teams - with support from other functions - revisit the automation to diagnose any potential problems and adjust the automations as appropriate.

Automated Moderation Activity Examples

A vast majority of all accounts that are suspended for the promotion of terrorism and CSE are proactively flagged by a combination of technology and other purpose-built internal proprietary tools. When we remove CSE content with these automated systems, we immediately report it to the National Center for Missing and Exploited Children (NCMEC). NCMEC makes reports available to the appropriate law enforcement agencies around the world to facilitate investigations and prosecutions.

Our current methods deploy a range of internal tools and and third party solutions that utilises industry standard hash libraries (e.g., PhotoDNA) to ensure known CSAM is caught prior to any user reports being filed. We leverage the hashes provided by NCMEC and industry partners. We scan media uploaded to X for matches to hashes of known CSAM sourced from NGOs, law enforcement and other platforms. We also have the ability to block keywords and phrases from Trending and block search results for certain terms that are known to be associated with CSAM.

We commit to continuing to invest in technology that improves our capability to detect and remove, for instance, terrorist and violent extremist content online before it can cause user harms, including the extension or development of digital fingerprinting and AI-based technology solutions. Our participation in multi-stakeholder communities, such as the Christchurch Call to Action, Global Internet Forum to Counter Terrorism and EU Internet Forum (EUIF), helps to identify emerging trends in how terrorists and violent extremists are using the internet to promote their content and exploit online platforms.

You can learn more about our commitment to eradicating CSE and terrorist content, and the actions we’ve taken here. Our continued investment in proprietary technology is steadily reducing the burden on people to report this content to us.

Scaled Investigations

These moderation activities are supplemented by scaled human investigations into the tactics, techniques and procedures that bad actors use to circumvent our Rules and policies. These investigations may leverage signals and behaviours identifiable on our platform, as well as off-platform information, to identify large-scale and/or technically sophisticated evasions of our detection and enforcement activities. For example, through these investigations, we are able to detect coordinated activity intended to manipulate our platform and artificially amplify the reach of certain accounts or their content.  

Indications of Accuracy for Content Moderation

The possible rate of error of the automated and human means used in enforcing X Rules and policies is represented by the number of Content Removal Complaints (appeals) received and the number of Content Removal Complaints that resulted in reversal of our enforcement decision (successful appeals) by remediation type and by country.

Closing Statement on Content Moderation Activities

Our content moderation systems are designed and tailored to mitigate systematic risks without unnecessarily restricting the use of our service and fundamental rights, especially freedom of expression. Content moderation activities are implemented and anchored on principled policies and leverage a diverse set of interventions to ensure that our actions are reasonable, proportionate and effective. Our content moderation systems blend automated and human review paired with a robust appeals system that enables our users to quickly raise potential moderation anomalies or mistakes.

Human resources dedicated to Content Moderation

Today, we have 1486 people working in content moderation. Our teams work on both initial reports as well as on complaints of initial decisions across the world (and are not specifically designated to only work on EU matters).

Linguistics Expertise of our Content Moderation Team

X’s scaled operations team possesses a variety of skills, experiences, and tools that allow them to effectively review and take action on reports across all of our Rules and policies. X has analysed which languages are most commonly found in reports reviewed by our content moderators, and has hired content moderation specialists who have professional proficiency in these commonly spoken languages. The following table is a summary of the number of people in our content moderation team who possess professional proficiency in the languages that are most commonly contained in reported content in the EU on our platform:

In addition to the primary language support, we have also have people supporting additional languages. The following is the list of secondary EU language support:

Please note that the numbers included in the secondary language support are not separate or distinct from the numbers included in the primary language support data. Additionally, the English language is not indicated as a secondary language category in the table above, since all agents with different primary language capability also speak English as well.

Qualifications of our Content Moderation Team

The above table includes all moderators who support EU member state languages as of March 2025. The content moderation team collectively provides linguistic capacity in multiple languages. In situations where we need additional language support, we use translation services and/or machine translation tools, to investigate and address challenges in additional languages. Additionally, content moderators will leverage playbooks that contain colloquial terms and phrases that are consistently being updated to reflect various EU languages spoken within the region and trends.

Moderators are recruited using a standard job description that includes a language requirement which states that the candidate should be able to demonstrate written and spoken fluency in the language and have at least one year of work experience for entry-level positions. In the interview and application process, each agent candidate must meet certain linguistic standards to be considered “language qualified”. This determination is made through multiple tests (i.e. written, oral, etc.) of the candidate’s respective language, to determine their respective proficiency level. Candidates must also meet the educational and background requirements in order to be considered, as well as demonstrate an understanding of current events for the country or region of content moderation they will support.

Organisation, Team Resources, Expertise, Training and Support of our Team that Reviews and Responds to Reports of Illegal Content

Description of the team

X has built a specialised team made up of individuals who have received specific training in order to assess and take action on illegal content that X becomes aware of via reports or other processes on our own initiative. This team consists of different tier groups, with higher tiers consisting of more senior, or more specialised, individuals.

When handling a report of illegal content or a complaint against a previous decision, content and senior content reviewers first assess the content under X’s Rules and policies. If no violation of X’s Rules and policies is determined warranting a global removal of the content, the content moderators will assess the content for potential illegality under Local Laws. If more detailed investigation is required, content moderators can escalate reports to experienced policy and/or legal request specialists who have also undergone in-depth training and/or have language expertise in the respective case’s language. These individuals take appropriate action after carefully reviewing the report and/or complaint in close detail. In cases where the specialist team cannot determine a final decision or action on a case, regarding the potential illegality of the reported content, the report will be discussed with in-house legal counsel. Everyone involved in this process works closely together with daily exchanges through meetings and other channels to ensure the timely and accurate handling of reports. Additionally, in the instance that a case warrants in-house legal counsel review, the lessons learned and actions made on this case will be disseminated to all relevant content moderator parties to ensure consistency in review and an understanding of best practices made by the agent, if a similar case is encountered in the future.

Furthermore, all teams involved in solving these reports closely collaborate with a variety of other policy teams at X who focus on X Rules and policies. This cross-team effort is particularly important in the aftermath of tragic events, such as violent attacks, to ensure alignment, swift consistency in review, and the same potential remediation actions if the content is found violative.

Content moderators are supported by team leads, subject matter experts, quality auditors and trainers. We hire people with diverse backgrounds in fields such as law, political science, psychology, communications, sociology and cultural studies, and languages.

Training and support of persons processing legal requests

All team members, i.e. all employees hired by X as well as vendor partners working on these reports, are trained and retrained regularly on our tools, processes, Rules and policies, including special sessions on cultural and historical context. Initially when joining the team at X, each individual follows an onboarding program and receives individual mentoring during this period, as well as thereafter through our Quality Assurance (QA) program , in house and external counsels (for internal employees).

All team members have direct access to robust training and workflow documentation for the entirety of their employment, and are able to seek guidance at any time from trainers, leads, and internal specialist legal and policy teams as outlined above, as well as managerial support.

Updates about significant current events or Rules and policy changes are shared with all content reviewers in real time, to give guidance and facilitate balanced and informed decision making. In the case of Rules and policy changes, all training materials and related documentation is updated. Calibration sessions are carried out frequently during the reporting period. These sessions aim to increase collective understanding and focus on the needs of the content reviewers in their day-to-day work, by allowing content moderators to ask questions and discuss aspects of recently reviewed cases, X’s Rules and policies, and/or local laws.

The entire team also participates in obligatory X Rules and policies refresher training as the need arises or whenever Rules and policies are updated. These trainings are delivered by the relevant policy specialists who were directly involved in the development of the Rules and policy change. For these sessions we also employ the “train the trainer” method to ensure timely training delivery to the whole team across all of the shifts. All team members use the same training materials to ensure consistency.

QA is a critical measure to the business to help ensure that we are delivering a consistent service at the desired level of quality to our key stakeholders, both externally and internally as it pertains to our case work. We have a dedicated QA team within our vendor team to help us identify areas of opportunity for training and potential defect detection in our workflow or Rules and policies. The QA specialists perform quality checks of reports to ensure that content is actioned appropriately.

The standards and procedures within the QA team ensure the team’s QA is assessed equally, objectively, efficiently and transparently. In case of any mis-alignments, additional training is scheduled, to ensure the team understands the issues and can handle reports accurately.

In addition, given the nature and sensitivity of their work, the entire team has access to online resources and regular onsite group and individual sessions related to resilience and well-being. These are provided by mental health professionals. Content reviewers also participate in resilience, self-care, and vicarious trauma training as part of our mandatory wellness plan during the reporting period.

Training and Support provided to those Persons performing Content Moderation Activities for our XIUC Terms of Service and Rules

Training is a critical component of how X maintains the health and safety of the public conversation through enabling content moderators to accurately and efficiently moderate content posted on our platform. Training at X aims to improve the content moderators’ enforcement performance and quality scores by enhancing content moderators’ understanding and application of X Rules through robust training and quality programs and a continuous monitoring of quality scores.

Training Process

There is a robust training program and system in place for every workflow to provide content moderators with the adequate work skills and job knowledge required for processing user cases. All content moderators must be trained in their assigned workflows. These focus areas ensure that content moderators are set up for success before and during the content moderation lifecycle, which includes:

• Training analysis/design focused on agent and learning needs;
• Classroom training with expert trainers;
• Nesting period to apply new skills;
• Cross-skilling opportunities;
• Upskilling opportunities;
• Refresher programs;
• New launch/update roll-outs process; and
• Remediation plans.

Training Analysis and Design

Before commencing design work on any content moderators program or resource, a rigorous learner analysis is conducted in close collaboration with training specialists and quality analysts to identify performance gaps and learning needs. Each program is designed with key stakeholder engagement and alignment. The design objective is to adhere to visual and learning design principles to maximise learning outcomes and ensure that agents can perform their tasks with accuracy and efficiency. This is achieved by making sure that the content is:

• Easy to experience;
• Easy to understand; and
• Easy to apply.

X’s training programs and resources are designed based on needs, and a variety of modalities are employed to diversify the content moderators learning experience, including:

• Self-led learning: microlearning, scenario-based learning, e-learning modules, and gamification (where appropriate);
• Virtual live instructor-led trainings;
• Face-to-face classroom training; and
• Videos.

Classroom Training

Classroom training is delivered either virtually or face-to-face by expert trainers. Classroom training activities can include:

• Instructor-led policy training;
• Interactive e-learnings;
• Scenario-based learning sets;
• Shadowing sessions with seasoned agents;
• Guided casework sessions with trainers; and
• Knowledge checks, quizzes and assessments.

 Onboarding and Ramp Up

When content moderators successfully complete their classroom training program, they undergo an onboarding period. The onboarding phase includes case study by observation, demonstration and hands-on training on live cases. Onboarding activities include content moderator shadowing, guided case work, Question and Answer sessions with their trainer, coaching, feedback sessions, etc. Quality audits are conducted for each onboarding content moderator and content moderators must be coached for any mis-action spotted in their quality scores the same day that the case was reviewed. Trainers conduct needs assessment for each onboarding content moderator and prepare refresher training accordingly. After the onboarding period, content is evaluated on an ongoing basis with the QA team to identify gaps and address potential problem areas. There is a continuous feedback loop with quality analysts across the different workflows to identify challenges and opportunities to improve materials and address performance gaps.

Up-Skilling

When a content moderator needs to be upskilled they receive training of a specific workflow within the same pillar that the content moderator is currently working. The training includes a classroom training phase and onboarding phase which is specified above.

Refresher Sessions

Refresher sessions take place when a content moderator has previously been trained, has access to all the necessary tools, but would need a review of some or all topics. This may happen for content moderators who have been on prolonged leave, transferred temporarily to another content moderation policy workflow, or ones who have recurring errors in the quality scores. After a needs assessment, trainers are able to pinpoint what the content moderator needs and prepare a session targeting their needs and gaps.

New Launch/Update Roll-Outs

There are also processes that require new and/or specific product training and certification. These new launches and updates are identified by X and the knowledge is transferred to the content moderators.

Remediation Plans

There are remediation plans in place to support content moderators who do not pass the training or onboarding phase, or are not meeting quality requirements.

Relevant Data for the Reporting Period

Member States Orders to Act Against Illegal Content

Removal Orders Median Time To Acknowledge Receipt

X provides an automated acknowledgement of receipt of removal orders submitted by law enforcement through our Legal Request submission portal. As a consequence of this immediate acknowledgement of receipt, the median time was zero hours.

Important Notes about Removal Orders:

• To improve clarity, we've omitted countries and violation types with no legal requests from the tables above;
• “Removal Orders Median Handle Time” shows the category that we considered to fit best and under which we handled the order. This category might deviate from the information provided by the authority when submitting the order via the X online submission platform; and
• In fifteen cases from France and Spain, we asked the submitting authority to fulfil Article 9 information requirements but did not receive responses in the reporting period.
• For illegal content reports that were reported as “Defamation/Insult” by the user or reporter, the data found within this category and/or legal reason was combined with the data of the category of “Illegal and harmful speech” under the category of “Illegal and harmful speech”.

Information Request Median Time To Acknowledge Receipt

X provides an automated acknowledgement of receipt of information requests submitted by law enforcement through our Legal Request submission portal. As a consequence of this immediate acknowledgement of receipt, the median time is zero.

Important Notes about Information Requests:

• To improve clarity, we have omitted countries and violation types with no legal requests from the tables above;
• The content category for each request is determined by the information law enforcement provides when submitting requests through the X online submission platform. If law enforcement does not provide sufficient information during form submission, the category is determined based on the allegations provided in the legal process. Where multiple illegal content categories were provided, only the gravamen offence was included;
• The median handling time is the time between receiving the order and either: 1) disclosing information to law enforcement if the order is valid; or 2) pushing back due to legal issues. The median handling time does not include additional time where X pushes back due to legal issues, receives a valid order or additional information later, and disclosure is eventually made;
• The “Issue Unknown” category shows cases where the illegal content category could not be determined based on the information law enforcement provided during the submission process and/or in the legal process; and
• For illegal content reports that were reported as “Defamation/Insult” by the user or reporter, the data found within this category and/or legal reason was combined with the data of the category of “Illegal and harmful speech” under the category of “Illegal and harmful speech”.

Illegal Content Notices