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Brands not active during last year’s Super Bowl made up 51% of the airtime for nationally-televised ads during this year’s Big Game.
Meet My Famous Friends
Celebrity integration into Super Bowl commercials has become a cornerstone of the creative decisions for many brands. This year was no different, with 60% of ads featuring at least 1 celebrity, an increase from 50% last year. Actors and actresses continue to be the most common type of celebrity to be cast in Super Bowl commercials.
EVs Unplugged
This year’s Super Bowl featured the lowest count (1) and lowest percentage of auto ads (50%) referencing electric vehicles since at least 2020.
A Part Of The Game
27 brands earned more than 5 seconds of screen time within the game and postgame telecast (excluding pregame and commercials), totaling almost two hours of cumulative screentime worth $247.8M in equivalent media value.
Hive is excited to announce that we are releasing a new API: Text Moderation Explanations! This API helps customers understand why our Text Moderation model assigns text strings particular scores.
The Need For Explainability
Hive’s Text Moderation API scans a text-string or message, interprets it, and returns to our users a score from 0-3 mapping to a severity level across a number of top level classes and dozens of languages. Today, hundreds of customers send billions of text strings each month through this API to protect their online communities.
A top feature request has been explanations for why our model assigns the scores it does, especially for foreign languages. While some moderation scores may be clear, there also may be ambiguity around edge cases for why a string was scored the way it was.
This is where our new Text Moderation Explanations API comes in—delivering additional context and visibility into moderation results in a scalable way. With Text Moderation Explanations, human moderators can quickly interpret results and utilize the additional information to take appropriate action.
A Supplement to Our Text Moderation Model
Our Text Moderation classes are ordered by severity, ranging from level 3 (most severe) to level 0 (benign). These classes correspond to the possible scores Text Moderation can give a text string. For example: If a text string falls under the “sexual” head and contains sexually explicit language, it would be given a score of 3.
The Text Moderation Explanations API takes in three inputs: a text string, its class label (either “sexual”, “bullying”, “hate”, or “violence”), and the score it was assigned (either 3, 2, 1, or 0). The output is a text string that explains why the original input text was given that score relative to its class. It should be noted that Explanations is only supported for select multilevel heads (corresponding to the class labels listed previously).
To develop the Explanations model, we used a supervised fine-tuning process. We used labeled data—which we internally labeled at Hive using native speakers—to fine-tune the original model for this specialized process. This process allows us to support a number of languages apart from English.
Comprehensive Language Support
We have built our Text Moderation Explanation API with broad initial language support. Language support solves the crucial issue of understanding why a text string (in one’s non-native language) was scored a certain way.
We currently support eight different languages for Text Moderation Explanations and four top level classes:
Text Moderation Explanations are now included at no additional cost as part of our Moderation Dashboard product, as shown below:
Additionally, customers can also access the Text Moderation Explanations model through an API (refer to the documentation).
In future releases, we anticipate adding further language and top level class support. If you’re interested in learning more or gaining test access to the Text Moderation Explanations model, please reach out to our sales team (sales@thehive.ai) or contact us here for further questions.
