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High packet rates at ≥ 10 GBit/s challenge the packet processing performance of network stacks. A common solution is to offload (parts of) the user-space packet processing to other execution environments, e.g., into the device driver (kernel-space), the NIC or even from virtual machines into the host operating system (OS), or any combination of those. While common wisdom states that offloading optimizes performance, neither benefits nor negative effects are comprehensively studied. In this paper, we aim to shed light on the benefits and shortcomings of eBPF/XDP-based offloading from the user-space to i) the kernel-space or ii) a smart NIC-including VM virtualization. We show that offloading can indeed optimize packet processing, but only if the task is small and optimized for the target environment. Otherwise, offloading can even lead to detrimental performance.
Corona-Warn-App: Tracing the Start of the Official COVID-19 Exposure Notification App for Germany
(2020)
On June 16, 2020, Germany launched an open-source smartphone contact tracing app ("Corona-Warn-App") to help tracing SARS-CoV-2 (coronavirus) infection chains. It uses a decentralized, privacy-preserving design based on the Exposure Notification APIs in which a centralized server is only used to distribute a list of keys of SARS-CoV-2 infected users that is fetched by the app once per day. Its success, however, depends on its adoption. In this poster, we characterize the early adoption of the app using Netflow traces captured directly at its hosting infrastructure. We show that the app generated traffic from allover Germany---already on the first day. We further observe that local COVID-19 outbreaks do not result in noticeable traffic increases.
We train word-emoji embeddings on large scale messagingdata obtained from the Jodel online social network. Our dataset contains more than 40 million sentences, of which 11 million sentences are annotated with a subset of the Unicode13.0 standard Emoji list. We explore semantic emoji associations contained in this embedding by analyzing associations between emojis, between emojis and text, and betweentext and emojis. Our investigations demonstrate anecdotallythat word-emoji embeddings trained on large scale messaging data can reflect real-world semantic associations. To enable further research we release the Jodel Emoji EmbeddingDataset (JEED1488) containing 1488 emojis and their embeddings along 300 dimensions.
In this work, we predict the user lifetime within the anonymous and location-based social network Jodel in the Kingdom of Saudi Arabia. Jodel's location-based nature yields to the establishment of disjoint communities country-wide and enables for the first time the study of user lifetime in the case of a large set of disjoint communities. A user's lifetime is an important measurement for evaluating and steering customer bases as it can be leveraged to predict churn and possibly apply suitable methods to circumvent potential user losses. We train and test off the shelf machine learning techniques with 5-fold crossvalidation to predict user lifetime as a regression and classification problem; identifying the Random Forest to provide very strong results. Discussing model complexity and quality trade-offs, we also dive deep into a time-dependent feature subset analysis, which does not work very well; Easing up the classification problem into a binary decision (lifetime longer than timespan ) enables a practical lifetime predictor with very good performance. We identify implicit similarities across community models according to strong correlations in feature importance. A single countrywide model generalizes the problem and works equally well for any tested community; the overall model internally works similar to others also indicated by its feature importances.
Characterizing the country-wide adoption and evolution of the Jodel messaging app in Saudi Arabia
(2022)
Social media is subject to constant growth and evolution, yet little is known about their early phases of adoption. To shed light on this aspect, this paper empirically characterizes the initial and country-wide adoption of a new type of social media in Saudi Arabia that happened in 2017. Unlike established social media, the studied network Jodel is anonymous and location-based to form hundreds of independent communities country-wide whose adoption pattern we compare. We take a
detailed and full view from the operators perspective on the temporal and geographical dimension on the evolution of these different communities—from their very first the first months of establishment to saturation. This way, we make the early adoption of a new type of social media visible, a process that is often invisible due to the lack of data covering the first days of a new network.
Reviewing War: Unconventional User Reviews as a Side Channel to Circumvent Information Controls
(2023)
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 business available on Google Maps or
Tripadvisor. This paper provides a first analysis of this new phenomenon by analyzing the creative strategies to avoid state censorship.
Specifically, we analyze reviews posted on these platforms from
the beginning of the conflict to September 2022. We measure the
channeling of war messages through user reviews in Tripadvisor
and Google Maps, as well as in VK, a popular Russian social network.
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.
Geographic Differences in Social Media Interactions Exist Between Western and Middle-East Countries
(2022)
In this paper, we empirically analyze two examples of a Western (DE) versus Middle-East (SA) Online Social Messaging App. By focusing on the system interactions over time in comparison, we identify inherent differences in user engagement. We take a deep dive and shed light onto differences in user attention shifts and showcase their structural implications to the user experience. Our main findings show that in comparison to the German counterparts, the Saudi communities prefer creating content in longer conversations, while voting more conservative.
We study the extent to which emoji can be used to add interpretability to embeddings of text and emoji. To do so, we extend the POLAR-framework that transforms word embeddings to interpretable counterparts and apply it to word-emoji embeddings trained on four years of messaging data from the Jodel social network. We devise a crowdsourced human judgement experiment to study six usecases, evaluating against words only, what role emoji can play in adding interpretability to word embeddings. That is, we use a revised POLAR approach interpreting words and emoji with words, emoji or both according to human judgement. We find statistically significant trends demonstrating that emoji can be used to interpret other emoji very well.