Protecting brand reputation with AI
We just learned something quite distressing – that one in 10 videos out in the online jungle we call the internet – can contain something potentially ‘harmful’ and ‘damaging’.
What we mean by this is that some videos contain certain elements that may not be accurately reflected by the title or tags associated with it. This poses a problem for brands who may not want to be associated with adult themes or extreme violence.
To find out what this means for brands and what options they have, we spoke to Kartik Mehta, Chief Revenue Officer, SilverPush. With their new product Mirrors Safe, the brand offers an AI-Powered context-relevant brand suitability platform to help prevent unwanted associations.
In order to build this product and understand they extent of the issue, Silverpush reviewed 15 million videos across the largest video hosting and sharing platforms in the SEA region using Mirrors Safe. With one in 10 or 10% containing images or negative associations (according to Silverpush criteria), there is definitely a need for a solution.
Congrats on the launch of Mirrors Safe. How do you see this new product helping brands and their advertisements?
Brands today are faced with different types of risks – financial risks, legal risks, and I guess the most important part of it is the reputation risk which could have larger concerns and probably a long-lasting impact on the overall brand equity. The existing brand safety measures like blocklists and whitelists are primarily focused on the principle of exclusion, which does protect brands to an extent but can also lead to one of the most pressing brand safety related concerns of over-blocking. Which can lead to brands missing the opportunity of engaging with the audiences across the right kind of content.
Whereas Mirrors Safe’s computer vision powered in-video context detection identifies faces, actions, scenes, emotions, on-screen-text in a streaming video to detect content that features violence, smoking, nudity, arms & guns and more. It detects these contexts only when they feature in a video, and not just by relying on keywords used to describe the video – which often times are misleading and can result both in unsafe placements as well as over blocking.
For instance, a video featuring smoking or violence might not be described so in its title, description or meta tags. There is no way for keyword-based solutions to identify these damaging contexts to filter out this video. Which can lead to household brands advertising across content which is highly unsuitable for their brand image. On the other hand, keywords like shoot, kill, crash, and even gun (some of the most blocked keywords) can easily be used within perfectly safe contexts, (like movies and songs).
Moreover, Mirrors Safe’s context detection makes it possible to offer brand suitability that can be customized for each brand or each category without following the blanket exclusion principles.
According to your research, 1 in 10 videos are deemed to be associated with dangerous or damaging content. How were you able to analyze around 15 million videos to generate this data?
Silverpush churned approximately 15 million videos across the largest video hosting and sharing platforms in the SEA region using Mirrors Safe. We used a randomly chosen inventory across platforms from our existing database – previously used to run campaigns using our video advertising platform Mirrors.
It was found that nearly 8-9% of analyzed content to be deemed brand unsafe. This means these videos featured one or more unsafe contexts like nudity, smoking, violence, arms and guns, and more.
However, a bigger discovery was the difference between the results found by exclusion through traditional methods like keyword lists vs. Mirror Safe’s in-video context detection technology.
For instance, when we used both methods to identify unsafe videos for one of the top brand unsafe categories – nudity and adult content, Mirrors Safe (through its frame-by-frame parsing) identified 300% more video content featuring unsafe context in this category, compared to exclusion through keyword lists.
A single damaging ad placement can harm brand perception in the consumer’s mind. This discovery highlights the potential harm that existing traditional measures are unable to detect. This has been witnessed time and time again, with some of the largest video advertising platforms being unable to keep brands safe from damaging content.
Have you been able to measure or estimate the negative impact of these associations for the brand?
There is already a plethora of information available on how even a single ad placement across harmful content can irreparably damage brand perception for a long time in the consumers mind. A 2019 study Trustworthy Accountability Group & Brand Safety Institute found that 80% consumers will stop or reduce buying products advertised against extreme or violent content. And, 70% believe advertiser and the agency are most responsible for a brand’s ad placements.
With our platform Mirrors, we have been serving contextually targeted video advertising across platforms since 2018. And we identified the challenge posed by traditional brand safety measures while serving our clients. We realized that ensuring brand safety is even more of a challenge across video formats, as NLP based technologies that work for other formats are ineffective in gauging the right context featured in video content.
Conversations and feedback from partners first led us to introduce a safety feature in Mirrors, where brands working with us did not report a single unsafe exposure since the launch of the feature. This further led us to launch Mirrors Safe, which can be deployed as a standalone context relevant suitability platform.
How has this solution helped brands so far? Do you have any initial test cases or beta usage that you can share?
I will start with the most interesting use case, that is highly relevant today.
Helped brands navigate the extreme over-blocking of COVID-19 related content
As brands and platforms rapidly add terms associated with COVID-19 to their keyword block, Coronavirus has become one of the most blocked keywords today. We have found advertisers looking to avoid unsafe brand exposure around this sensitive topic are forced to exclude news entirely from their list of targeted channels and publishers. However, excluding news and related channels entirely from advertising strategies across platforms is killing reach for brands.
One of the key factors behind extreme COVID-19 related over-blocking is the inability to detect if the COVID-19 related stories are informative Vs. stories that can harm brands – leading to blanket exclusions. Mirrors Safe identified what the video content features to differentiate the stories, in the following ways:
- On-screen text recognition: Mirrors Safe identifies and filters out videos that have related text written on the screen (e.g. Coronavirus or COVID-19). And can help differentiate between morbidity related stories Vs. more positive stories around for instance precautions.
- Object and action detection: the system can identify objects like masks, stretchers, and actions like coughing and sneezing and understand the concentration of this content within a single video through frame by frame parsing.
- Faces: with this outbreak certain public figures are also on brands’ blocklists (yes, Trump). In-video context detection can accurately identify faces to filter out related content.
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