The Rise of an Anonymous Social Network

Social networks have become a popular Internet service and exist in various flavors. These flavors can be categorized by i) infrastructure (e.g., centralized vs. decentralized / P2P), ii) user profile (e.g., real names, pseudonyms, or anonymity), iii) geographic coverage (e.g., global vs. regional), or iv) functionality (e.g., microblogging on Twitter or questions on Stackoverflow). Most prominent networks are centralized, provide global coverage and usually do not permit anonymous use (e.g., Facebook). A large body of research has focused on understanding these networks and user behavior by analyzing underlying friendship/connection graphs, content, or usage pattern. Besides these well-known and well-studied networks, there exists a rising demand for both i) location-based/regional (e.g., Nextdoor) and ii) anonymous (e.g., Whisper) networks. Anonymous networks and location-based network have yet, however, not received much attention in scientific literature.

In this poster, we present initial results obtained by crawling an emerging anonymous and location-based social network for more than one year. This network is predominant in Germany and Scandinavia and is currently expanding across Europe and other continents. It enables users to post text content or images and to comment on posts within threads. It differs from other social networks by two aspects: 1) It is location based and only displays posts sent within close (e.g., 10km) geographic proximity; 2) All communication is anonymous by not displaying user handles. Users are only enumerated by posting order within a single discussion thread, to enable users referencing to each other within a discussion.

To prevent abuse, the network employs a community filtering and moderation system. This community moderation follows a simple filtering scheme which prefers mainstream content: posts may be voted up or down resulting in a cumulative score. When the per-post or answer score exceeds a negative score of -5, it is not being shown anymore. To increase user engagement and voting, the network applies gamification by awarding “Karma” points that users collect by either voting or posting content that is upvoted by others. Further, harmful or non-policy compliant content can be flagged for moderation. Such reported content is reviewed by volunteering community moderators (selected by properties like activity or Karma scores) and is kept or removed according to the moderators’ majority vote. For an anonymous social network, community moderation is a key success parameter to prevent harmful or abusive content. The recent downfall of the YikYak anonymous network highlighted that unsuccessfully preventing adverse content can seriously hurt the network.

To study the network, we retrieve posts by performing continuous crawls from about 200 cities for a period of more than one year summing up to about NNN posts. Based on this data, we present preliminary results on the network in terms of activity within different cities and posted content. We further present preliminary results from studying the networks community moderation system. By discussing our first results with the IMC community, we aim to shed light on an emerging and less studied type of social network.