Designing Word Filter Tools for Creator-led Comment Moderation

Title of the Paper

Designing Word Filter Tools for Creator-led Comment Moderation

Paper Information

• Domain: Comment section management for content creators and the design of online content moderation tools
• Keywords: Platform governance, YouTube, online harassment, content moderation, content creators, human-computer interaction, FilterBuddy

Research Background and Problem

• Identified Problems or Challenges:

  1. Content creators on online social platforms face emotional labor pressures when dealing with large volumes of comments, especially those containing harassment or hate speech.
  2. Existing comment filtering tools (e.g., keyword filters) are overly simplistic and fail to meet creators' complex moderation needs.
  3. Creators struggle to effectively organize and manage filtering rules and lack visualization tools to evaluate the effectiveness of filters.

• Why This Problem Matters: Content creators are a vital part of social platforms, fostering communities through comment section management and audience engagement. However, hateful or harassing content in comment sections negatively impacts creators and their communities, degrading user experiences and potentially causing mental health issues, particularly for creators from marginalized groups.

• Research Motivation and Related Work: This study addresses the limitations of existing content moderation tools (e.g., keyword filters) by focusing on the needs of marginalized creators and exploring ways to improve these tools. Related work includes research on the unique challenges faced by content creators, studies on online harassment, and the shortcomings of current keyword filtering tools.

Proposed Solution

• Proposed Solution: The FilterBuddy Tool

  • Enables creators to create new filtering rules from scratch or import existing filters.
  • Provides organizational and categorization features for filtering rules (e.g., racism, sexism).
  • Offers intuitive visualization tools to display the effectiveness of filters and trends over time.

• Innovations:

  1. Enhances rule management usability through the organization and categorization of keyword filters.
  2. Adds a preview feature, allowing creators to view comments captured by specific keyword rules in real time.
  3. Supports automatic detection of spelling variations, simplifying the configuration process for creators.
  4. Includes built-in filter categories (e.g., anti-racism, anti-sexism) to reduce the difficulty of manual configuration.
  5. Introduces filter-sharing and collaboration mechanisms to support exchange and cooperation among creators.

• Implementation Steps and Key Technologies:

  1. After user login, the system retrieves comment data via the YouTube API.
  2. Based on the configured filtering rules, the system automatically performs actions such as deleting or flagging new comments for review.
  3. Provides a graphical interface to display trend analysis and operational statistics.
  4. Supports third-party developers in extending tool functionality through open-source code.

Research Outcomes

• Specific Outcomes:

  1. Designed and implemented the FilterBuddy tool, addressing the functional gaps in existing tools.
  2. Validated the tool's practicality and user acceptance through experiments with real content creators.

• Advantages:

  1. Reduces emotional labor and time investment for creators in comment management.
  2. Supports more efficient information organization and trend analysis.
  3. Promotes collective governance and collaboration within the creator community through sharing and cooperation mechanisms.

• Experimental or Evaluation Results: Participants praised the visual design and usability of FilterBuddy and suggested additional application scenarios. The tool was well-received by creators, particularly those from marginalized groups, who believed it could help alleviate the stress caused by online harassment.

• Limitations and Future Directions:

  1. The current tool only supports text matching and cannot handle multimodal content (e.g., images, videos).
  2. The tool is limited to the YouTube platform and needs to be extended to other social platforms.
  3. Further exploration is required for filter rule sharing and collaboration to ensure privacy and security.
  4. Large-scale user deployment experiments have not been conducted; future research should investigate in-the-wild applications.

Ethical Considerations

• The tool could be misused for excessive censorship or restricting legitimate content, necessitating careful design to balance freedom of speech with moderation needs.
• Post-launch monitoring is required to prevent misuse or the creation of inequalities.

Conclusion

This study addresses the challenges faced by content creators by designing the FilterBuddy tool based on user needs and validating its effectiveness. Future work and broader policy support could advance the development of more efficient and creator-friendly comment management mechanisms.