"I have never seen that for Deaf people's content:" Deaf and Hard-of-Hearing User Experiences with Misinformation, Moderation, and Debunking on Social Media in the US

Research Background and Issues

• What problems or challenges did the authors identify?
  This study focuses on the misinformation issues faced by Deaf and Hard-of-Hearing (DHH) users in social media engagement. The authors found that existing misinformation filtering and correction strategies designed for general social media users (e.g., platform-driven warning labels, user-driven community notes, and expert-driven video clarifications) often fail to meet the needs of DHH users. Specifically, topics related to DHH (such as American Sign Language and Deaf culture) lack targeted misinformation intervention methods.

• Why is this issue important?
  DHH users' participation and information access on social media are closely tied to their cultural identity. However, misinformation and inappropriate content moderation can reinforce stereotypes and lead to social exclusion of the DHH community. Additionally, due to language or literacy barriers caused by hearing loss, DHH users may be particularly susceptible to misinformation, necessitating solutions that are visually oriented and culturally adapted.

• Research Motivation and Related Work
  Although the impact of misinformation on different social media user groups has been widely studied, systematic research on DHH users is severely lacking. The authors aim to fill this gap by uncovering the real experiences of DHH social media users and providing design guidelines for improving information filtering and mediation methods.

Solutions

• What methods or solutions did the authors propose?
  Through semi-structured interviews with 15 DHH users, the study investigated how they perceive and handle misinformation, collected feedback on existing mediation methods, and explored new design suggestions tailored to this group.

• What is innovative about this solution?

1. Emphasized the necessity of representation-led debunking, a method that suggests involving DHH users or certified DHH representatives in actively clarifying and mediating misinformation.
2. Proposed leveraging artificial intelligence (AI) to generate American Sign Language (ASL) video warnings and translated content.
3. Suggested optimizing existing methods (e.g., TikTok's "Stitch" and "Duet" features) to better serve the DHH community.

• What are the implementation steps and key technologies used?

1. Adjust platform-driven and user-driven mediation features to include simpler, more readable text or support ASL-translated content to meet the literacy and language needs of DHH individuals.
2. Incorporate accurate subtitles in expert-driven video clarification features, ensuring subtitles are not obstructed and standardizing their format, color contrast, and display duration.
3. Proposed developing AI-supported ASL translation or subtitle generation technologies to help DHH users more efficiently create and consume content related to misinformation.

Research Outcomes

• What specific outcomes were achieved?

1. DHH users frequently encounter both general and DHH-specific misinformation, but there is a near absence of moderation and handling methods for such content.
2. Current platform moderation tools (e.g., warning labels, community notes) have low appeal and usability for DHH users due to a lack of visual prominence and overly complex language.
3. Most DHH users prefer video-based clarification methods (e.g., TikTok's Stitch feature), but their actual use often lacks adequate subtitle support.

• What advantages does it have compared to existing solutions?

1. Unlike filtering mechanisms designed solely for general users, this study highlights the specific cultural and cognitive needs of DHH users, particularly in terms of visual presentation, language simplification, and trust.
2. Introduced the concept of representation-led debunking, which helps enhance the sense of participation within the DHH community and reduces cultural misinterpretations.
3. Emphasized the potential application of artificial intelligence to make video content creation and verification more efficient and scalable.

• What were the experimental or evaluation results?
  Through interviews, DHH users indicated that existing text-based filtering mechanisms fail to effectively convey the risks of misinformation and do not prioritize DHH-related content. Their feedback highlighted that improving visual tools and ASL support could significantly enhance usability and acceptance.

• Limitations and Future Directions

1. This study is limited to English-speaking and ASL-using users in the United States, and its findings may not be generalizable to DHH communities in other cultural and linguistic contexts.
2. The proposed design suggestions and AI technology developments still face technical limitations, such as inconsistent quality in ASL auto-generation.
3. Future research could further explore methods to verify the accuracy of user-generated content and design misinformation mediation tools in a culturally sensitive manner.

Through the findings and recommendations of this study, the authors contribute a new perspective to broader social media accessibility improvements and inclusive technology design, while providing concrete design guidelines and technical implementation pathways for enhancing the digital participation of the DHH community.