Are there specific examples of manipulated media that Twitter has flagged in the past?

Yes, Twitter has flagged several high-profile examples of manipulated media over the years. These examples often involve altered or misleading content related to political events, public health, and social issues. Here are a few notable cases:

### **1. **Deepfake Videos and Altered Clips**

- **Example**: In 2018, a video of House Speaker Nancy Pelosi was edited to make her appear intoxicated. The video was manipulated to distort her speech and make it look as though she was slurring her words. Twitter flagged this content as manipulated media and added warnings to inform users that it was altered.

- **Example**: During the 2020 U.S. Presidential Election, a deepfake video that falsely portrayed Joe Biden making inappropriate comments was flagged by Twitter. The platform added labels to inform users of the video's manipulated nature and reduced its visibility.

### **2. **Misinformation During Public Health Crises**

- **Example**: During the COVID-19 pandemic, manipulated media that spread false information about the virus, vaccines, and treatments was frequently flagged. For instance, doctored videos and images claiming false cures or misrepresenting scientific data were labeled or removed to prevent the spread of misinformation.

- **Example**: In early 2021, manipulated images and videos falsely claiming that vaccines contained harmful substances or altered DNA were flagged by Twitter. The platform worked to provide accurate information and address misleading content.

### **3. **Political Misinformation**

- **Example**: During the 2020 U.S. Presidential Election, Twitter flagged manipulated media related to voter fraud and election results. This included doctored videos and images designed to mislead voters about the election process and outcomes.

- **Example**: In various elections globally, manipulated media aiming to discredit candidates or misrepresent political events has been flagged. For instance, altered videos or images depicting false activities or statements by political figures were reviewed and labeled.

### **4. **Social Media Hoaxes and False Claims**

- **Example**: A manipulated image purporting to show a protester holding a weapon was flagged during various social movements. The image was doctored to create a false narrative about the nature of the protests. Twitter labeled the image and provided context to clarify its manipulation.

- **Example**: During the Black Lives Matter protests, manipulated media that falsely portrayed violent or inflammatory actions by protesters was flagged. Twitter added context to these posts to prevent the spread of false information.

### **5. **International Incidents**

- **Example**: In the context of international conflicts, manipulated media often circulated to promote false narratives or incite tension. For example, during conflicts in Syria and Ukraine, manipulated images and videos were flagged to prevent the spread of misleading or inflammatory content.

- **Example**: Misinformation related to the Hong Kong protests included manipulated videos and images intended to mislead or provoke. Twitter flagged such content and provided additional context where possible.

### **6. **Content from State-Controlled Media**

- **Example**: In cases where state-controlled media from various countries spread manipulated content, Twitter flagged and labeled these posts. This included altered or misleading content that could influence public opinion or political stability.

### **Policy Application**

- **Labeling and Warnings**: In many cases, rather than removing manipulated media outright, Twitter has opted to label it with warnings or context to inform users of its altered nature. This approach helps maintain transparency while addressing potential misinformation.

- **Visibility Controls**: Manipulated media flagged by Twitter often faces reduced visibility to limit its spread and impact, especially if it is deemed likely to mislead or harm users.

These examples illustrate how Twitter's approach to handling manipulated media involves a combination of flagging, labeling, and context to mitigate the spread of misinformation and ensure users are informed about the nature of the content they encounter.