MAdLens: Investigating Into Android In-App Ad Practice at API Granularity

摘要

In-app advertising has served as the major revenue source for millions of app developers in the mobile Internet ecosystem. Ad networks play an important role in app monetization by providing third-party libraries for developers to choose and embed into their apps. Various ad mediations help developers manage all of the ad libraries used in apps to show the best available ad among received ads from different ad network servers. However, developers lack guidelines on how to choose from hundreds of ad networks or ad mediations and various ad features to maximize their revenues without hurting the user experience of their apps. Our work aims to provide app developers guidelines on the selection of ad networks, ad mediations, and ad placement by observing current common practices. To this end, we investigate 838 unique APIs from 207 ad networks which are extracted from 277,616 Android apps, develop a methodology of ad type classification based on UI interaction and behavior, and perform a large scale measurement study of in-app ads with static analysis techniques at the API granularity. We found that developers have more choices about ad networks than several years before. Most developers are conservative about ad placement and about 77 percent of the apps contain at most one ad library. Besides, the likeliness of an app containing ads depends on the app category to which it belongs. Furthermore, we propose a terminology and classify mobile ads into five ad types: Embedded, Popup, Notification, Offerwall, and Floating. Also, our research shows that it is a better solution for developers to integrate ad libraries with ad mediation feature in their apps because it may avoid bad ratings and improve user experience. And in our findings, more than 95 percent of embedded, popup, notification, and offer ads locate in the zero activity (main activity), the first activity and the second activity of Android apps. More interestingly, developers tend to put high aggressive ads on activities which need deeper user interaction. Our research is the first to reveal the preference of both developers and users for ad networks, ad mediation feature and ad types.