Helge Reelfs

Detecting where traffic enters a network can enhance network operation, but poses a complex measurement problem that requires analyzing a continuous traffic stream from all border routers—an infeasible task for most ISPs in the absence of a scalable inference approach. To enable ISPs to perform Ingress Point Detection (IPD), we propose an efficient approach that accurately identifies traffic ingress points in ISPs of any size using flow-level traffic traces. IPD identifies the specific router and interface through which a particular segment of the Internet address space enters a network. IPD splits the address space into fine-grained ranges to identify the specific router and interface through which segments of the address space enter the network.We have deployed IPD for six years at a major tier-1 ISP with an international network that handles multi-digit Tbit/s traffic levels, and our experience shows that IPD can accurately identify ingress points and scale to high traffic loads on a single commodity server. IPD enabled the ISP to improve network operations by identifying performance issues and realizing advanced traffic engineering practices.

During the first days of the 2022 Russian invasion of Ukraine, Russia’s media regulator blocked access to many global social media platforms and news sites, including Twitter, Facebook, and the BBC. To bypass the information controls set by Russian authorities, pro-Ukrainian groups explored unconventional ways to reach out to the Russian population, such as posting war-related content in the user reviews of Russian businesses available on Google Maps or Tripadvisor. This paper provides a first analysis of this new phenomenon by analyzing the unconventional strategies used to avoid state censorship in the Russian Federation during the conflict. Specifically, we analyze reviews posted on these platforms from the beginning of the war to September 2022. We measure the channeling of war-related messages through user reviews on Tripadvisor and Google Maps. Our analysis of the content posted on these services reveals that users leveraged these platforms to seek and exchange humanitarian and travel advice, but also to disseminate disinformation and polarized messages. Finally, we analyze the response of platforms in terms of content moderation and their impact.

Nowadays day-to-day digital communication and social life has only fortified with the ongoing pandemic. People enjoy communication and information across various (direct) messaging platforms and accepted its ever-increasing impact on public discourse, and thus society. While most traditional platforms implement user profiles enabling social credit, the new landscape now also includes anonymity. Yet, a new type of applications combining anonymity with a strong spatial focus, hyperlocality, emerged over recent years. To this point, platform implications of both uniquely combined design properties largely remain unknown. In this thesis (with minor exceptions) we provide a first data-driven holistic view on Jodel that combines both properties. We leverage unbiased and complete ground truth information to dissect a plethora of communities across two different countries: Germany & the Kingdom of Saudi Arabia. This work follows a platform perspective identifying four major essentially important areas revolving around the individual. That is, we begin with a broad analysis of three User Adoption processes along three different applications. After discussing our measurements of the start and user base adoption of the German COVID-19 digital contact tracing application, we provide evidence of well-established platforms being re-purposed as a side channel to evade censorship in the ongoing Russo-Ukrainian hybrid war. We further showcase that even the very same platform ingredients may yield vastly different outcomes on the messaging app Jodel. While any online platform builds upon user User Interactions, we structurally characterize Jodel behavior across both countries. We discuss structural disparities and detail platform implications - solely induced by local user behavior. An in-depth look into the Saudi community landscape closes a research gap to platform usage in non-Western societies, identifying fundamental differences. Further, we discuss User Content analyzing information diffusion. Taking content to the next level, we developed a multidimensional classification scheme for intents (why) and topics (what) of social media messages and provide details of a crowdsourced campaign for Saudi Arabian contents. With neural word embeddings as a tool for making text tangible and the prevalence of emoji in social media communication, we discuss quantitative & qualitative insights to word-emoji embeddings reflecting semantics. Additionally, we make such embeddings interpretable and provide evidence that our method is well in line with human judgement. In terms of User Management, we detail insights to distributed moderation processes and model the threat of abusive content. In the long term, platforms need to establish a sustainable, preferably growing, environment. That is, we next discuss user lifetime and possibly early churn factors, while modeling user lifetime from metadata. We finish with a blueprint of data-driven long term quality of experience analyzes in a controlled massively multiplayer online game (MMOG) environment.