BACK TO ALL BLOGS Why We Worked with Parler to Implement Effective Content Moderation HiveMay 17, 2021March 5, 2025 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.
BACK TO ALL BLOGS Series D Funding: Hive Announces $85M in New Capital and $2B Valuation HiveApril 21, 2021March 4, 2025 Today, I’m excited to announce that Hive has raised $85M in new capital, inclusive of a $50M Series D financing round at a $2B valuation and a previously unannounced $35M Series C financing round which closed last year at a $1B valuation. Our Series D round was led by Glynn Capital, with participation from our other major existing investors, including General Catalyst, Tomales Bay Capital, Bain & Company, and Jericho Capital. This funding is an important milestone for the company, accelerating our ambition to be the leading provider of enterprise AI solutions to power the next wave of intelligent automation. Disruption triggered by the COVID-19 pandemic has pushed companies across industries to rethink how work is done, and this has resulted in an increased ambition and accelerated roadmap for the use of AI in enterprise automation. This capital infusion gives us the ability to meet the near-term market opportunity without constraints. The use of AI in enterprise automation is still at its infancy. In recent years, companies across industries have embraced robotic process automation (RPA) to realize efficiencies from automating repetitive and lower-value tasks. A November 2020 Deloitte survey reported current use of RPA by 78% of companies and expected use of the technology by 94% of companies within the next three years. While RPA has delivered significant value across industries (and to the providers enabling these solutions), there is a ceiling on the types of activities that can be addressed with that technology‒generally limited to highly transactional activities such as application log in, data extraction, and form filling. We aim to unlock the full potential of enterprise automation, spanning a set of more “intelligent” manual processes and new processes not feasible to scale with manual labor. Deep learning, the discipline of AI that we focus on at Hive, enables human-like interpretation of video, image, text, and audio‒newly enabling a next wave of intelligent automation. The market is ready. That same Deloitte study found that only 34% of companies report use of AI for automation today, but an incremental 52% of companies now plan to implement it over the next three years. While the potential of AI models is endless, making them effective in a production setting is a separate matter altogether. We experienced this ourselves viscerally 4 years ago‒at the time as consumer app developers struggling to find out-of-the-box AI models accurate enough to handle processes such as content moderation. Forced to develop these models ourselves, we realized that other companies likely faced a similar dearth of high quality, publicly accessible models for common problems. From this challenge, the vision for Hive was born: performant, cloud-based and pre-trained AI models that are accessible via a simple API. Over the past three and a half years, we have earned the trust of now more than 100 enterprise customers through an obsessive focus on accuracy‒rooted in pre-trained models that consistently and significantly outperform comparable solutions. The foundation of our model accuracy is predicated upon a belief that vast amounts of high quality training data is the most important factor of a performant model. We put such importance on this that we took the unusual step of building out our own distributed human workforce for the sole purpose of generating annotations for machine learning datasets at scale. Our Hive workforce has grown to be one of the largest in the world, with over 2.5M contributors and more than 4B human judgments generated. What began as a purely internal tool quickly became a valuable product in its own right, and today many of the world’s largest and most innovative companies rely on our platform to source and label raw data for developing their own AI models. Being our own largest data annotation customer, we have the luxury of building AI models trained on unprecedented amounts of data. For instance, our visual content moderation model alone is trained on more than 600M judgments across 30+ classes; this is several orders of magnitude larger than any open source data set available. The resulting best-in-class model accuracy across our portfolio of solutions has driven significant growth in all our core company metrics. Over the past year, we’ve increased our customer base and revenue by more than 300% and are now processing billions of API calls a month. Since Q1 2018, we’ve increased API calls by nearly 60x: What Makes Us Different Our differentiation is in our industry-leading accuracy, enabled by the combination of our best-in-class ML technology with training data produced by our distributed labeling workforce of 2M+ contributors Specifically, content moderation has been a key driver of our growth in the past year. This suite of models is now trusted by more than 75 customers globally, including content platforms such as Reddit, Yubo, Chatroulette, Omegle, Tango, and more, as well as leading dating sites, gaming platforms, verification services, and more. Our models provide real-time inference of video, image, text, and audio content, enabling clients to automatically identify and remove prohibited content across more than 40 classes, including sexual content, violence, gore, drugs, and hate speech. Companies that have integrated with Hive’s content moderation APIs have consistently increased the amount of content proactively reviewed and significantly reduced the level of human exposure to sensitive content, across both moderators and users. Our portfolio of AI solutions for the media & entertainment industry has been another key driver of our recent growth, earning the trust of major media companies including NBCUniversal, large media agencies including Interpublic Group, and established brands including Walmart and Anheuser-Busch InBev. Our suite of AI models for the media space are collectively trained on more than 1B pieces of hand-labeled data and bring transformative new capabilities to areas such as contextual advertising and brand safety, advertising intelligence, measurement of sponsorship and branded content, and more. Finally, we’re fortunate to have established a diverse set of marquee partnerships to accelerate our go-to-market on a global scale. Over the past year, we expanded our partnership with Bain & Company, which was also an investor in our Series C and Series D financing, to support a broader set of use cases across the firm’s practice areas. We partnered with Cognizant, one of the world’s leading professional services companies, to expand Cognizant’s use of automation across a diverse set of use cases across industries. And earlier this year, we announced a partnership with Comscore, a leading media measurement and analytics company, that will integrate Hive’s technology into Comscore’s product portfolio, including the launch of a reinvented branded content measurement solution enhanced with next-day, AI-powered data from Hive. These partners have complemented our internal sales team in driving revenue for our business, but more importantly, they have also provided valuable guidance that has influenced our product roadmap and priorities. We look forward to announcing several other partnerships in the coming months. Despite everything we’ve built over the past few years and all of the early commercial successes, we’re still scratching the surface of what’s possible. Like other major technology shifts in the past, AI will continue to permeate virtually all aspects of our lives, and all companies will have to adopt an AI strategy sooner than they would expect. While this additional capital gives us the resources to accelerate our growth to full potential, we will ultimately measure our success not by our funding figures‒or even our revenue‒but rather by the transformative impact that our clients’ products will have on the world. If you’d like to join us on this mission, please check out our open roles here: thehive.ai/careers Learn more about Hive Try our models Key product pages: Content ModerationContextual Advertising & Brand SafetyLogo & Brand ExposureData Sourcing & Annotation
BACK TO ALL BLOGS Hive at Advertising Week 2020: Practical Enterprise AI Solutions for Media and Marketing HiveSeptember 28, 2020July 4, 2024 Hive is excited to participate in Advertising Week 2020, kicking off Tuesday, September 29th. Watch our promotional video to learn more about how Hive is working with media owners, brands, agencies, and others in the media ecosystem.
BACK TO ALL BLOGS Hive Adds Hate Model to Fully-Automated Content Moderation Suite HiveJune 25, 2020July 4, 2024 Social media platforms increasingly play a pivotal role in both spreading and combating hate speech and discrimination today. Now integrated into Hive’s content moderation suite, Hive’s hate model enables more proactive and comprehensive visual and textual moderation of hate speech online. Year over year, our content moderation suite has emerged as the preeminent AI-powered solution to both help platforms keep their environments protected from harmful content, and to dramatically reduce the exposure of human moderators to sensitive content. Hive’s content moderation models have consistently and significantly outperformed comparable models, and we are proud to currently work with more than 30 of the world’s largest and fastest-growing social networks and digital video platforms. Today we are excited to officially integrate our hate model into our content moderation product suite, helping our current and future clients combat racism and hate speech online. We believe that blending our best-in-class models with the significant scale of our clients’ platforms can result in real step-change impact. Detecting hate speech is a unique challenge that is dynamic and evolving rapidly. Context and subtle nuances vary widely across cultures, languages, and regions. Additionally, hate speech itself isn’t always explicit. Models must be able to recognize subtleties quickly and proactively. Hive is committed to taking on that challenge and, over the past months, we have partnered with several of our clients to ready our hate model for today’s launch. How We Help Hate speech can occur both visually and textually with a large percentage occurring in photos and videos. Powered by our distributed global workforce of more than 2 million registered contributors, Hive’s hate model is trained on more than 25 million human judgments and supports both visual classification models and text moderation models. Our visual classification models classify entire images into different categories by assigning a confidence score for each class. These models can be multi-headed, where each group of mutually exclusive model classes belongs to a single model head. Within our hate model, some examples of heads include the Nazi and KKK symbols, and other terrorist or white supremacist propaganda. Results from our model are actioned according to platform rules. Many posts are automatically actioned as safe or restricted; others are routed for manual review of edge cases where a symbol may be present but not in a prohibited use. Our visual hate models will typically achieve >98% recall and a <0.1% false positive rate. View our full documentation here. Our text content moderation model is a multi-head classifier that will now include hate speech. This model automatically detects “hateful language” – defined, with input from our clients, as any language, expression, writing, or speech that expresses / incites violence against, attacks, degrades, or insults a particular group or an individual in a particular group. These specific groups are based on protected attributes such as race, ethnicity, national origin, gender, sex, sexual orientation, disability, and religion. Hateful language includes but is not limited to hate speech, hateful ideology, racial / ethnic slurs, and racism. View our full documentation here. We are also breaking ground on solving the particularly challenging problem of multimodal relationships between the visual and textual content, and expect to be adding multi-modal capabilities over the next weeks. Multimodal learning allows our models to understand the relationship between both text and visual content in the same setting. This type of learning is important to better understand the meaning of language and the context in which it is used. Accurate multimodal systems can avoid flagging cases where the visual content on its own may be considered hateful, but the presence of counterspeech text — where individuals speak out against the hateful content — negates the hateful signal in the visual content. Similarly, multimodal systems can help flag cases where the visual and textual content independently are not considered to be hateful, but in the context of one another are in fact hateful, such as hateful memes. Over time, we expect this capability to further reduce the need for human reviews of edge cases. What’s Next? Today’s release is a milestone we are proud of, but merely the first step in a multi-year commitment to helping platforms filter hate speech from their environments. We will continue to expand and enhance model classification with further input from additional moderation clients and industry groups.
BACK TO ALL BLOGS Back to School? HiveJune 16, 2020July 4, 2024 TV advertising has been arguably more dynamic than ever during the COVID-19 pandemic. How will the lingering uncertainty around the 2020-2021 school year impact how marketers approach Back-to-School campaigns? Research using Mensio, a media analytics platform developed by Hive and Bain Media Lab, highlights how advertisers approached Back-to-School campaigns in 2019 and what to expect in 2020. Trends to watch for Back-to-School 2020 Back to Advertising?Many specialty retailers typically active with Back-to-School campaigns have stepped down TV ad spend while stores have been closed; Back-to-School campaigns may bring those brands back to TVNew to School?Potential for “classrooms at home” may drive new advertisers and messages focused on home office furniture and technologyGeographical FlexibilityBrands may increase the use of digital platforms and /or local advertising to better align messages with geographic variance in timing and conditions for the re-opening of schools
BACK TO ALL BLOGS 2020 NFL Draft: TV’s Newest Tentpole This year’s NFL Draft stole headlines with record ratings. Viewers saw not only a new format with cameras in homes, but also significant changes in the brands who advertised. HiveApril 30, 2020July 4, 2024 At a Glance: The 2020 NFL Draft shattered ratings records and was an unqualified success for viewers, brands, and the airing networks – all starved for live sports contentAmidst an altered TV advertising landscape, viewers were exposed to a different mix of brands compared to the 2019 event – across the three airing networks, only 61 of 150 advertisers also had spots in last year’s eventAds from travel & leisure, automotive, media & entertainment, and retail advertisers decreased most significantly year-over-year; ads from quick service restaurants, insurers, tech companies, and streaming video platforms increased the mostCoronavirus-related messages made up more than one-third of all ad spots across networks, almost twice as frequent as the 19 percent of all national TV airings focused on similar messagesAI-powered measurement of brand exposure in content highlighted the long tail of college football sponsorship values. ESPN’s presenting sponsor, Lowe’s, achieved the most equivalent media value from logo exposure within the programming; however, Nike was next most with swooshes visible in highlights, and four other on-field apparel brands ranked in the top 15 of brands with in-content exposure With reported year-over-year viewership gains of roughly forty percent, the 2020 NFL Draft was an unqualified success for viewers, brands, and the airing networks – all starved for live sports content as the COVID-19 disruption persists. For those who watched in 2019, this year’s event looked different. Notably, more than 600 camera feeds enabled a “virtual draft” format featuring teams, players, and commentators connecting from their homes. Beyond the altered format, viewers were also exposed to a much different set of brands. Of the 150 advertisers who ran spots across ESPN, ABC, and NFL Network during this year’s event, only 61 also advertised in the 2019 NFL Draft (see Figure 1). This overlapping group was led by brands including Verizon, Lowe’s, Pizza Hut, Taco Bell, and State Farm. Several major automakers including GMC, Nissan, and Honda were among the 96 brands sitting out this year’s draft after being active in the 2019 event. Filling the gaps were 89 brands not active in last year’s event, with IBM, BMW, John Deere, and DoorDash among the new-to-Draft brands with heavy presence. The carousel of brands impacted category-level share of voice as well. Last year, ads from travel & leisure, automotive, media & entertainment, and retail advertisers made up 43 percent of airings across ESPN, ABC, and NFL Network; this year, that number decreased to 27 percent (see Figure 2). Growing share of voice year-over-year were quick service restaurants, insurers, tech companies, and streaming video platforms. Over the past month, Hive and Bain Media Lab have monitored the continued increase in TV ad campaigns related to COVID-19, tracking the flighting and messaging of the more than 160 brands who have released bespoke campaigns. Across the TV universe, these campaigns made up 19 percent of all airings during the past week. This concentration was significantly higher during the NFL Draft, with 35 percent of all spots featuring coronavirus-related campaigns (see Figure 3). This peaked at 41 percent of national TV ad airings on the NFL Network feeds, 34 percent on ABC, and 29 percent on ESPN. A host of league and broadcast sponsors achieved additional exposure in-content with logo presence in the telecast. In total across the three days, 33 brands received 15 or more seconds of in-content logo exposure, excluding league, team, and network logos (see Figure 4). This analysis was completed using Hive’s logo model, using a computer vision model trained with more than two million manhours of human-labeled training data and able to automatically detect and value the presence of logos from more than 5,000 brands. Visible brands included Lowe’s as the presenting sponsor and other broadcast sponsors scattered into the broadcasts (e.g. “Autotrader Trade Alerts”). League sponsors including Microsoft and Gatorade had visible product placement in the telecast, as did Bose which monopolized the headphones and earphones used by players and teams during the event (albeit without camera-visible branding). Interestingly, many of the top brands visible within the 2020 NFL Draft programming earned their exposure primarily through highlight footage from past events. This included Nike, which had the second-most total exposure, as well as four other on-field apparel brands which ranked among the top 15. College bowl sponsors including Chick-Fil-A and Allstate also made the list, making the case for always-on measurement of sponsorship exposures. While the NFL Draft is one example of this, separate research from Hive has found that shoulder programming and highlights consistently amplify valuations for sports sponsorships – sometimes by as much as twice the value of whistle-to-whistle measurement. With the intrigue of draft selections passed, the key question in the sports world now returns to when and how games will resume. If this weekend was any indication, changes to the format shouldn’t have any negative impact on the demand from viewers or brands. Note: Published Bain Media Lab research relies solely on third-party data sources and is independent of any data or input from clients of Bain & Company