The rapid growth in user generated content on social media has resulted in a significant rise in demand for automated content moderation. Various methods and frameworks have been proposed for the tasks of hate speech detection and toxic comment classification. In this work, we combine common datasets to extend these tasks to brand safety. Brand safety aims to protect commercial branding by identifying contexts where advertisements should not appear and covers not only toxicity, but also other potentially harmful content. As these datasets contain different label sets, we approach the overall problem as a binary classification task. We demonstrate the need for building brand safety specific datasets via the application of common toxicity detection datasets to a subset of brand safety and empirically analyze the effects of weighted sampling strategies in text classification.

February 13, 2020 / BY IAS TEAM -- Making the shift from brand safety to suitability takes some decoding, but we've got you covered. Thanks to machine learning and AI with natural language understanding, IAS's Brand Safety and Suitability solutions are evolving to include more flexibility and coverage than ever. Our technology replaces binary strategies with dynamic and scalable solutions that allow for real-time customization. We power the evolution from simply protecting your brand to building brand equity by seeking appropriate and suitable environments that generate positive responses from consumers and amplify the impact of your campaigns. Fill in the blanks to decode your strategy, and take control of your media spend by working with IAS to make the shift from brand safety to suitability to make media waste a thing of the past.

We are pleased to introduce the ability to export high-resolution keyframes from Azure Media Service's Video Indexer. Whereas keyframes were previously exported in reduced resolution compared to the source video, high resolution keyframes extraction gives you original quality images and allows you to make use of the image-based artificial intelligence models provided by the Microsoft Computer Vision and Custom Vision services to gain even more insights from your video. This unlocks a wealth of pre-trained and custom model capabilities. You can use the keyframes extracted from Video Indexer, for example, to identify logos for monetization and brand safety needs, to add scene description for accessibility needs or to accurately identify very specific objects relevant for your organization, like identifying a type of car or a place. Let's look at some of the use cases we can enable with this new introduction.

We have seen lately how artificial intelligence is destined to change the future of society. It may become a powerful disruptive force with a huge potential to alter our relationship with work environments and jobs themselves. In the last few years, a growing fear has arisen among big brands and important advertisers and has had a cascade effect downwards in the industry: once you have spent millions advertising your products, the last thing you want happening to them is to be advertised in the wrong place. Digital Marketing has been fighting this issue for a long time and the efforts to assure the correct placement of online ads is known as "Brand Safety". Advertising Week defines the term Brand Safety as "the process of gaining full transparency into online media so that ads are not placed within or beside inappropriate or potentially harmful content".

Influencer marketing is growing, but no brand wants to be subjected to a Logan Paul fiasco. Agencies and brands are investigating ways to verify influencer followers and eliminate fraud, with some like Nike and HelloFresh going so far as to bring more of their influencer marketing in-house. Digiday spoke with Doug Rozen, chief digital and innovation officer at the Omnicom agency OMD, about how artificial intelligence can improve influencer marketing. Our conversation has been edited for clarity. Are clients spending more on influencer marketing?

The New Year is approaching fast and it's going to be an eventful one for marketers. Between ensuring readiness for the General Data Protection Regulation's (GDPR) imminent enforcement, exploring new opportunities created by ongoing industry consolidation and collaboration, and future proofing businesses for the age of AI, 2018 looks set to be a year of further transformation for the industry. We spoke to key representatives from the marketing and advertising technology sectors to discover how they expect the digital marketing landscape to evolve over the coming year. "In the ad industry, machine learning and AI are on every company's agenda, or should be. These technologies are fundamental to improving efficiencies by producing and delivering highly-targeted ad campaigns. "There's no disputing that AI and machine learning are already changing the way we work, and five to 10 years from now they may render our jobs unrecognisable from what they are today.

Imagine the Internet without Google? Trying to find anything would be unbelieva-bly painful – not to mention time-consuming. Well, by 2020, Cisco forecasts that there will be 65 trillion im-ages and 6 trillion videos uploaded to the web. This will result in over 80% of all in-ternet traffic being image or video-based in two years. Search engines like Google rely on human-tagged meta-data to carry out their searches. These tags are written and added when text-based content is created.

Key Points: – Each day, Rocket Fuel Inc. evaluates over 200 billion opportunities to determine when and where to place an ad on behalf of its brand clients. Rocket Fuel (NASDAQ: FUEL) is a predictive marketing software company that uses artificial intelligence to empower agencies and marketers to anticipate people's need for products and services. Rocket Fuel was founded in March 2008 with a vision of transforming the digital advertising industry through the use of big data and artificial intelligence. Most interactions between consumers and brands are now digital in nature. Every 60 seconds, there are millions of engagements generated and millions of moments for brands to connect with consumers.

In today's age of flying cars, robots and Elon Musk, if you haven't heard of artificial intelligence (AI) or machine learning (ML) then you must be avoiding all types of media. To most, these concepts seem futuristic and not applicable to everyday life, but when it comes to marketing technology, AI and ML actually touch everyone that consumes digital content. But how exactly are these being deployed for marketing technology and digital media? Outside of this sector, we hear about AI being applied in medical and military fields, but usually not something as commonplace as media. Utilizing these advanced technologies actually enables mar tech and ad tech companies to create highly personalized and custom digital content experiences across the web.

Google on Monday unveiled measures meant to help marketers track where advertisements appear across YouTube, in the wake of controversy over the company's placement of ads alongside videos with objectionable content. The tech giant, a unit of Alphabet Inc., GOOGL 1.06% told marketers and advertisers that it plans to allow third-party measurement companies to monitor where ads appear on YouTube, and to report back to marketers on the "brand safety" of its videos. Google already offers similar functionality allowing marketers to track whether their ads were "viewable" or not--meaning whether they actually appeared on users' screens. According to executives at ad agencies, Google also has promised to offer video-level reporting across YouTube by the third quarter of this year. That feature would give advertisers a full list of specific videos against which their ads appeared, and how many times their ads were displayed on each, perhaps making marketers more comfortable with advertising on the service.