Hello, my name is Shagun Jhaver and in this video I'm going to present a work on designing word filter tools for creator-led comment moderation. This work was done in collaboration with my wonderful co-authors Quanze Chen, Detlef Knauss and Amy Zhang.
Before I begin, I want to caution that some of the slides in the talk contain offensive language.
This research is focused on serving content creators on platforms like YouTube, TikTok and Twitch. Creators on these platforms not only how to write produce and edit that content, they are also responsible for removing inappropriate comments posted on the channels.
Creators, especially those below belonging to marginalized groups, often experience, stalking harassment and online threats and feel a greater need to engage in comment moderation.
However, as the channels grow, it becomes difficult for creators to review all the comments by themselves, and some automation becomes necessary. As a result, creators have long requested for more powerful content moderation tools.
As creators, one of the most commonly available tools to automate moderation is word filters. These are tools in which users can configure a list of phrases and comments containing any of those phrases are automatically removed.
While these word filter tools are available on many platforms, they usually present very rudimentary features.
So in this study, we first identified the moderation needs of creators, who use word filter tools.
For this, we interviewed 19 content creators, who use word filters on YouTube twitch or TikTok and analyzed our interview data.
And after identifying creators' needs, we designed a new system called FilterBuddy that augments word filter tools with features to support better authoring, maintenance and auditing of what filters.
Our participants told us that instead of a single box to configure all their word filter, they wanted to separately group different categories of inappropriate comments because it would lead to better organization of their word filters and allow an easier review and search for them
To address this need, filter buddy allows creators to organize their word filters in several different categories.
Participants, especially those belonging to marginalized groups, wanted FilterBuddy to have a few curated categories available by default, because they wanted to avoid creating commonly needed categories like sexism and racism from scratch.
To address this need, users can clone one of the built-in categories that FilterBuddy provides such as homophobia, physical violence or sexual violence.
We have created filters for these categories ourselves based on prior literature. Here, we are seeing how a user imports the built-in homophobia category.
On importing, users are redirected to a new homophobia category that is already populated with many carefully curated homophobic keywords. Users can subsequently edit this category to match their preferences.
We also found that participants wanted to see examples of matching comments about configuring the word filters, because they felt it would improve their understanding of how each word filter would perform if they configure it.
To address this need, users can enter a phrase in the add new phrase box. This automatically populates all the comments previously posted on these users channel that contain this phrase.
Reviewing these comments allows users to make an informed decision on whether they want to add this phrase as a word filter.
Users can choose whether they want the system to catch the spelling variants of each word filter. They can also decide what actions they want to take on comments caught by each word filter.
Participants wanted to get an overview of word filters’ actions. They especially wanted to review the temporal performance of their configured categories.
So on each category page, we present a graph that tells users how different phases configured in that category catch comments on their channel over the last 30 days.
We also show the users a preview table of all the comments caught by that category.
Additionally, on the overview page, we should the users, a graph of all the comments caught by each category for the last 30 days.
We evaluated filter buddy by conducting a user study with eight YouTube creators.
We asked participants to explore filter buddy and author filters over their comments while providing us qualitative feedback.
Through this study, we found that users are appreciative of greater automation that filter buddy enables.
When we asked participants to compare word filters to something machine learning based, they showed resistance to replacing the rule based configurations of this tool with solely machine learning based approaches.
We were also surprised to find that participants deviced many use cases for this tool that went beyond mere common removals such as finding collaborators, filtering for positive feedback, and better understanding their audiences.
Now let me highlight some of the key takeaways of this work.
We found that readers are deeply invested in retaining control over the moderation operations. Considering this, we argue that sensible design defaults can help us achieve this goal while minimizing the manual effort of setting up granular filters.
We also examine how other stakeholders can assist content creators with moderating their comments.
Platforms like YouTube and Twitch could go beyond providing API access to their data and dedicate technical and human resources to foster an ecosystem of third party tools such as FilterBuddy.
Minority support groups can use their domain expertise and influence to curate and publicize appropriate lexicons for tools like filter buddy.
And finally, policymakers can empower content creators by mandating that large platforms like YouTube, Twitch and TikTok must provide creators with moderation mechanisms that let them efficiently manage their channel's comments.
Thank you and we invite you to use FilterBuddy to manage your YouTube channel's comments.