Abstract: Systems and methods for evaluating online videos. One method includes receiving a URL; determining a URL type; detecting whether the URL includes one or more videos; determining at least one of a size of the video, a position of the video on a web page of the web page URL, whether the video is set to autoplay, and whether the video is set to mute; computing a score based on one or more of the size of the video, the position of the video on the web page of the web page URL, whether the video is set to autoplay, and whether the video is set to mute; obtaining at least two frames of at least part of the video, wherein each frame is obtained at one or more predetermined intervals during playback of the video; and classifying each detected video based on the at least two frames.

Abstract: Systems and methods for evaluating online videos. One method includes receiving a URL; determining a URL type; detecting whether the URL includes one or more videos; determining at least one of a size of the video, a position of the video on a web page of the web page URL, whether the video is set to autoplay, and whether the video is set to mute; computing a score based on one or more of the size of the video, the position of the video on the web page of the web page URL, whether the video is set to autoplay, and whether the video is set to mute; obtaining at least two frames of at least part of the video, wherein each frame is obtained at one or more predetermined intervals during playback of the video; and classifying each detected video based on the at least two frames.

Abstract: Systems and methods for evaluating online videos. One method includes receiving a URL; determining a URL type; detecting whether the URL includes one or more videos; determining at least one of a size of the video, a position of the video on a web page of the web page URL, whether the video is set to autoplay, and whether the video is set to mute; computing a score based on one or more of the size of the video, the position of the video on the web page of the web page URL, whether the video is set to autoplay, and whether the video is set to mute; obtaining at least two frames of at least part of the video, wherein each frame is obtained at one or more predetermined intervals during playback of the video; and classifying each detected video based on the at least two frames.

Abstract: Methods, systems, and media for identifying errors in predictive models using annotators are provided. In some embodiments, a method for evaluating predictive models in classification systems is provided, the method comprising: causing an input region to be presented to a user, where the input region receives an instance from the user that corresponds to a predictive model; retrieving a classification conducted by the predictive model for the received instance and a confidence value associated with the classification; determining whether the received instance has been incorrectly classified by the predictive model; determining a reward associated with the incorrect classification made by the predictive model in response to determining that the received instance has been incorrectly classified by the predictive model, where the reward is based on the confidence value associated with the classification of the received instance; and providing the reward to the user.

Abstract: Methods, systems, and media for review content traffic are provided. In accordance with some embodiments of the present invention, a method, implemented on a processor, for reviewing advertisement placement on webpages is provided.

Abstract: Systems, methods, and media for rating websites for safe advertising are provided. In accordance with some embodiments of the disclosed subject matter, the method comprises: extracting one or more features from a piece of web content; applying a plurality of statistical models to the extracted features to generate a plurality of ordinomial estimates, wherein each ordinomial estimate represents a probability that the web content is a member of one of a plurality of severity groups; determining a posterior ordinomial estimate for the web content by combining the plurality of ordinomial estimates; generating a risk rating that encodes severity and confidence based on the determined posterior ordinomial estimate, wherein the risk rating identifies whether the web content is likely to contain objectionable content of a given category; and providing the risk rating for determining whether an advertisement should be associated with the web content.

Abstract: Content management systems, methods, and media using an application level firewall are provided.