Users may interact with various types of media objects comprising a wide range of information. In an example, a user may view a beach image shared by another user through an image sharing service. In another example, the user may read a videogame console article published through a videogame website. In another example, the user may watch a live video stream of a soccer game through a video streaming app. In another example, the user may listen to a podcast through an online radio station. Many content providers may spend substantial amounts of time and computing resources attempting to combine content (e.g., a message, a link, an image, an interactive game, etc.) and media objects (e.g., an image, video, and/or audio). For example, a website may play an initial video before playing a requested video (e.g., a videogame website may force users to watch a video about a brief history of the website before being able to watch a videogame review show). In another example, a news website may display an image overlaying a news article. Unfortunately, the combination of content and media objects may be arbitrary, random (e.g., subject matter of the image may have little to do with subject matter of the article, such as a weather image overlaying an article about a corrupt politician), and/or annoying (e.g., the image may obscure part of the article). Thus, manual and human intervention are required to improve the combination of content and media objects so that they better match.