Category: Company

Over the past few years, Hive’s cloud-based APIs for moderating image, video, text, and audio content have been adopted by hundreds of content platforms, from small communities to the world’s largest and most well-known platforms like Reddit.  

However, not every platform has the resources or interest in building their own software on top of Hive’s APIs to manage their internal moderation workflows.  And since the need for software like this is shared by many platforms, it made sense to build a robust, accessible solution to fill the gap.

Today, we’re announcing the Moderation Dashboard, a no-code interface for your Trust & Safety team to design and execute custom-built moderation workflows on top of Hive’s best-in-class AI models.  For the first time, platforms can access a full-stack, turnkey content moderation solution that’s deployable in hours and accessible via an all-in-one flexible seat-based subscription model.

We’ve spent the last month beta testing the Moderation Dashboard and have received overwhelmingly positive feedback.  Here are a few highlights:

• “Super simple integration”: customizable actions define how the Moderation Dashboard communicates with your platform
• “Effortless enforcement”: automating moderation rules in the Moderation Dashboard UI requires zero internal development effort
• “Streamlined human reviews”: granular policy enforcement settings for borderline content significantly reduced need for human intervention
• “Flexible” and “Scalable”: easy to add seat licenses as your content or team needs grow, with a stable monthly fee you can plan for

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We’re excited by the Moderation Dashboard’s potential to bring industry-leading moderation to more platforms that need it, and look forward to continuing to improve it with updates and new features based on your feedback.

If you want to learn more, the post below highlights how our favorite features work.  You can also read additional technical documentation here.

Easily Connect Moderation Dashboard to Your Application

Moderation Dashboard connects seamlessly to your application’s APIs, allowing you to create custom enforcement actions that can be triggered on posts or users – either manually by a moderator or automatically if content matches your defined rules.

You can create actions within the Moderation Dashboard interface specifying callback URLs that tell the Dashboard API how to communicate with your platform.  When an action triggers, the Moderation Dashboard will ping your callback server with the required metadata so that you can successfully execute the action on the correct user or post within your platform.

Implement Custom Content Moderation Rules

At Hive, we understand that platforms have different content policies and community guidelines. Moderation Dashboard enables you to set up custom rules according to your particular content policies in order to automatically take action on problematic content using Hive model results. 

Moderation Dashboard currently supports access to both our visual moderation model and our text moderation model – you can configure which of over 50 model classes to use for moderation and at what level directly through the dashboard interface. You can easily define sets of classification conditions and specify which of your actions – such as removing a post or banning a user – to take in response, all from within the Moderation Dashboard UI. 

Once configured, Moderation Dashboard can communicate directly with your platform to implement the moderation policy laid out in your rule set. The Dashboard API will automatically trigger the enforcement actions you’ve specified on any submitted content that violates these rules.

Another feature unique to Moderation Dashboard: we keep track of (anonymized) user identifiers to give you insight into high-risk users. You can design rules that account for a user’s post history to take automatic action on problematic users. For example, platforms can identify and ban users with a certain number of flagged posts in a set time period, or with a certain proportion of flagged posts relative to clean content – all according to rules you set in the interface.

Intuitive Adjustment of Model Classification Thresholds

Moderation Dashboard allows you to configure model classification thresholds directly within the interface. You can easily set confidence score cutoffs (for visual) and severity score cutoffs (for text) that tells Hive how to classify content according to your sensitivity around precision and recall.

Streamline Human Review

Hive’s API solutions were generally designed with an eye towards automated content moderation. Historically, this has required our customers to expend some internal development effort to build tools that also allow for human review. Moderation Dashboard closes this loop by allowing custom rules that route certain content to a Review Feed accessible by your human moderation team.

One workflow we expect to see frequently: automating moderation of content that our models classify as clearly harmful, while sending posts with less confident model results to human review. By limiting human review to borderline content and edge cases, platforms can significantly reduce the burden on moderators while also protecting them from viewing the worst content.

Setting Human Review Thresholds

To do this, Moderation Dashboard administrators can set custom score ranges that trigger human review for both visual and text moderation. Content scoring in these ranges will be automatically diverted to the Review Feed for human confirmation. This way, you can focus review from your moderation team on trickier cases, while leaving content that is clearly allowable and clearly harmful to your automated rules. Here’s an example rule that sends text content scored as “controversial” (severity scores of 1 or 2) to the review feed but auto-moderates the most severe cases.

Review Feed Interface for Human Moderators

When your human review rules trigger, Moderation Dashboard will route the post to the Review Feed of one of your moderators, where they can quickly visualize the post and see Hive model predictions to inform a final decision.

For each post, your moderators can select from the moderation actions you’ve set up to implement your content policy. Moderation Dashboard will then ping your callback server with the required information to execute that action, enabling your moderators to take quick action directly within the interface.

Additionally, Moderation Dashboard makes it simple for your Trust & Safety team administrators to onboard and grant review access to additional moderators. Platforms can easily scale their content moderation capabilities to keep up with growth.

Access Clear Intel on Your Content and Users

Beyond individual posts, Moderation Dashboard includes a User Feed that allows your moderators to see detailed post histories of each user that has submitted unsafe content. 

Here, your moderators can access an overview of each user including their total number of posts and the proportion of those posts that triggered your moderation rules. The User Feed also shows each of that user’s posts along with corresponding moderation categories and any corresponding action taken. 

Similarly, Moderation Dashboard makes quality control easy with a Content Feed that displays all posts moderated automatically or through human review. The Content Feed allows you to see your moderation rules in action, including detailed metrics on how Hive models classified each post. From here, administrators supervise human moderation teams for simple QA or further refine thresholds for automated moderation rules.

Effortless Moderation of Spam and Promotions

In addition to model classifications, Moderation Dashboard will also filter incoming text for spam entities – including URLs and personal information such as emails and phone numbers. The Spam Manager interface will aggregate all posts containing the same spam text into a single action item that can be allowed or denied with one click.

With Spam Manager, administrators can also define custom whitelists and blacklists for specific domains and URLs and then set up rules to automatically moderate spam entities in these lists. Finally, Spam Manager provides detailed histories of users that post spam entities for quick identification of bots and promotional accounts, making it easy to keep your platform free of junk content. 

Final Thoughts: The Future of Content Moderation

We’re optimistic that Moderation Dashboard can help platforms of all sizes meet their obligations to keep online environments safe and inclusive. With Moderation Dashboard as a supplement to (or replacement for) internal moderation infrastructure, it’s never been easier for our customers to leverage our top-performing AI models to automate their content policies and increase efficiency of human review. 

Moderation Dashboard is an exciting shift in how we deliver our AI solutions, and this is just the beginning. We’ll be quickly adding additional features and functionality based on customer feedback, so please stay tuned for future announcements.

If you’d like to learn more about Moderation Dashboard or schedule a personal demo, please feel free to contact sales@thehive.ai. 

Earlier today, The Washington Post published a feature detailing Hive’s work with social network Parler, and the role our content moderation solutions have played in protecting their community from harmful content and, as a result, earning their app reinstatement in Apple’s App Store.

We are proud of this very public endorsement on the quality of our content moderation solutions, but also know that with such a high-profile client use case there may be questions beyond what could be addressed in the article itself about why we decided to work with Parler and what role we play in their solution. For detailed answers to those questions, please see below.

Why did Hive decide to work with Parler?

We believe that every company should have access to best-in-class content moderation capabilities to create a safe environment for their users. While vendors earlier this year terminated their relationships with Parler after believing their services were enabling a toxic environment, we believe our work addresses the core challenge Parler faced and enables a safe community for Parler’s users to engage.

As outlined in our recent Series D funding announcement, our founders’ precursor to Hive was a consumer app business that itself confronted the challenge of moderating content at scale as the platform quickly grew. The lack of available enterprise-grade, pre-trained AI models to support this content moderation use case (and others) eventually inspired an ambitious repositioning of the company around building a portfolio of cloud-based enterprise AI solutions.

Our founders were not alone. Content moderation has since emerged as a key area of growth in Hive’s business, now powering automated content moderation solutions for more than 75 platforms globally, including prominent dating services, video chat applications, verification services, and more. A December 2020 WIRED article detailed the impact of our work with iconic random chat platform Chatroulette.

When Parler approached us for help in implementing a content moderation solution for their community, we did not take the decision lightly. However, after discussion, we aligned on having built this product to provide democratized access to best-in-class content moderation technology. From our founders’ personal experience, we know it is not feasible for most companies to build effective moderation solutions internally, and we therefore believe we have a responsibility to help any and all companies keep their communities safe from harmful content.

What is Hive’s role in content moderation relative to Parler (or Hive’s other moderation clients)?

Hive provides automated content moderation across video, image, text, and audio, spanning more than 40 classes (i.e., granular definitions of potentially harmful content classifications such as male nudity, gun in hand, or illegal injectables).

Our standard API provides a confidence score for every content submission against all our 40+ model classes. In the instance of Parler, model flagged instances of hate speech or incitement in text are additionally reviewed by members of Hive’s 2.5 million plus distributed workforce (additional details below).

Our clients map our responses to their individual content policies – both in terms of what categories they look to identify, how sensitive content is treated (i.e., blocked or filtered), and the tradeoff between recall (i.e., the percentage of total instances identified by our model) and precision (i.e., the corresponding percentage of identifications where our model is accurate). Hive partners with clients during onboarding as well as on an ongoing basis to provide guidance on setting class-specific thresholds based on client objectives and the desired tradeoffs between recall and precision.

It is the responsibility of companies like Apple to then determine whether the way our clients choose to implement our technology is sufficient to be distributed in their app stores, which in the case of Parler, Apple now has.

What percentage of content is moderated, and how fast?

100% of posts on Parler are processed through Hive’s models at the point of upload, with latency of automated responses in under 1 second.

Parler uses Hive’s visual moderation model to identify nudity, violence, and gore. Any harmful content identified is immediately placed behind a sensitive content filter at the point of upload (notifying users of sensitive content before they view).

Parler also uses Hive’s text moderation model to identify hate speech and incitement. Any potentially harmful content is routed for manual review. Posts deemed safe by Hive’s models are immediately posted to the site, whereas flagged posts are not displayed until model results are validated by a consensus of human workers. It typically takes 1-3 minutes for a flagged post to be validated. Posts containing incitement are blocked from appearing on the platform; posts containing hate speech are placed behind a sensitive content filter. Human review is completed using thousands of workers within Hive’s distributed workforce of more than 2.5 million registered contributors who have opted into and are specifically trained on and qualified to complete the Parler jobs.

In addition to the automated workflow, any user-reported content is automatically routed to Hive’s distributed workforce for additional review and Parler independently maintains a separate jury of internal moderators that handle appeals and other reviews.

This process is illustrated in the graphic below.

How effective is Hive’s moderation of content for Parler, and how does that compare to moderation solutions in place on other social networks?

We have run ongoing tests since launch to evaluate the effectiveness of our models specific to Parler’s content. While we believe that these benchmarks demonstrate best-in-class moderation, there will always be some level of false negatives. However, the models continue to learn from their mistakes, which will further improve the accuracy over time.

Within visual moderation, our tests suggest the incidence rate of adult nudity and sexual activity content not placed behind a sensitive content filter is less than 1 in 10,000 posts. In Facebook’s Q4 2020 Transparency Report (which, separately, we think is a great step forward for the industry and something all platforms should publish), it was reported that the prevalence of adult nudity and sexual activity content on Facebook was ~3 to 4 views per 10,000 views. These numbers can be seen as generally comparable with the assumption that views of posts with sensitive content roughly average the same as all other posts.

Within text moderation, our tests suggest the incidence rate of hate speech (defined as text hateful towards another person or group based on protected attributes, such as religion, nationality, race, sexual orientation, gender, etc.) not placed behind a sensitive content filter was roughly 2 of 10,000 posts. In Q4 2020, Facebook reported the prevalence of hate speech was 7 to 8 views per 10,000 views on their platform.

Our incidence rate of incitement (defined as text that incites or promotes acts of violence) not removed from the platform was roughly 1 in 10,000 posts. This category is not reported by Facebook for the purposes of benchmarking.

Does Hive’s solution prevent the spread of misinformation?

Hive’s scope of support to Parler does not currently support the identification of misinformation or manipulated media (i.e., deepfakes).

We hope the details above are helpful in further increasing understanding of how we work with social networking sites such as Parler and the role we play in keeping their environment (and others) safe from harmful content.

Learn more at https://thehive.ai/ and follow us on Linkedin

Press with additional questions? Please contact press@thehive.ai to request an interview or additional statements.

Note: All data specific to Parler above was shared with explicit permission from Parler.