Passionate Researcher & Data Scientist,
ALDI SÜD, International Data & Analytics.
AbstractDuring 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.
AbstractA subset of massively multiplayer online games (MMOG) feature long-term game rounds in which players interact for months or even years. The player experience of such long-term games cannot be entirely captured by current study methods, in particular not at scale assessing large player populations. To address this challenge, we posit that long-term, round based games such as Tribal Wars (browser-based) enable a data-driven perspective on long-term game dynamics and experience. In a preliminary study, we monitor and characterize the entire longitudinal game state of a Tribal Wars round that was played by 16k players for 1.5 years, enabling us to investigate behavioral patterns of all active players. We identify features that capture the in-game success and relate to the player experience. We show that only successful players keep up playing. We open source our dataset enabling reproducibility & future research.
AbstractWe 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 crowdsourced human judgement experiment to study six use-cases, 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.
AbstractSocial 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.
AbstractIn this paper, we study what users talk about in a plethora of independent hyperlocal and anonymous online communities in a single country: Saudi Arabia (KSA). We base this perspective on performing a content classification of the Jodel network in the KSA. To do so, we first contribute a content classification schema that assesses both the intent (why) and the topic (what) of posts. We use the schema to label 15k randomly sampled posts and further classify the top 1k hashtags. We observe a rich set of benign (yet at times controversial in conservative regimes) intents and topics that dominantly address information requests, entertainment, or dating/flirting. By comparing two large cities (Riyadh and Jeddah), we further show that hyperlocality leads to shifts in topic popularity between local communities. By evaluating votes (content appreciation) and replies (reactions), we show that the communities react differently to different topics; e.g. entertaining posts are much appreciated through votes, receiving the least replies, while beliefs & politics receive similarly few replies but are controversially voted.
AbstractOn June 16, 2020, Germany launched an open-source smartphone contact tracing app (“Corona-Warn-App”) to help tracing SARSCoV- 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 SARSCoV-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.
AbstractWe train word-emoji embeddings on large scale messaging data obtained from the Jodel online social network. Our data set contains more than 40 million sentences, of which 11 million sentences are annotated with a subset of the Unicode 13.0 standard Emoji list. We explore semantic emoji associations contained in this embedding by analyzing associations between emojis, between emojis and text, and between text and emojis. Our investigations demonstrate anecdotally that word-emoji embeddings trained on large scale messaging data can reflect real-world semantic associations. To enable further research we release the Jodel Emoji Embedding Dataset (JEED1488) containing 1488 emojis and their embeddings along 300 dimensions.
In recent years the study of social media communities has come into the focus of research. One open but central question is which properties stimulate user interaction within communities and thus contribute to community building. In this paper, we provide a first step towards answering this question by identifying features in the Jodel microblogging app that trigger user responses as one form of attention. Jodel is a geographically restricted app that allows users to post threads and comments anonymously. The absence of displayed user information on Jodel makes the posted content the only trigger for user interaction, making the language the one and only means for users to gather contextual implications about their discourse partners. This enhanced function of language promises a revealing baseline investigation into linguistic behavior on social media.
To approach this issue, we conducted a sequence of lexico-grammatical analyses and subjected the quantitative results to various statistical tests. While a Principal Component Analysis did not show a significant difference between the grammatical structure of original posts with and without answers, a negative binomial regression model focusing on the interpersonal meta-function yielded significant results. A further analysis of these features correlated to shorter or longer response times showed significant results for the interrogative mood. Additionally, keyword analyses revealed significant differences between posts with answers and without answers. Our study provides a promising first step towards understanding textual features triggering user interaction and thereby community building – an unresolved problem of practical relevance to social network operation.
AbstractHigh 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.
AbstractThis paper studies for the first time the usage and propagation of hashtags in a new and fundamentally different type of social media that is i) without profiles and ii) location-based to only show nearby posted content. Our study is based on analyzing the mobile-only Jodel microblogging app, which has an established user base in several European countries and Saudi Arabia. All posts are user to user anonymous (i.e., no displayed user handles) and are only displayed in the proximity of the user’s location (up to 20 km). It thereby forms local communities and opens the question of how information propagates within and between these communities. We tackle this question by applying established metrics for Twitter hashtags to a ground-truth data set of Jodel posts within Germany that spans three years. We find the usage of hashtags in Jodel to differ from Twitter; despite embracing local communication in its design, Jodel hashtags are mostly used country-wide.
AbstractAs network speed increases, servers struggle to serve all requests directed at them. This challenge is rooted in a partitioned data path where the split between the kernel space networking stack and user space applications induces overheads. To address this challenge, we propose Santa, a new architecture to optimize the data path by enabling server applications to partially offload packet processing to a generic rule processor. We exemplify Santa by showing how it can drastically accelerate kernel-based packet processing - a currently neglected domain. Our evaluation of a broad class of applications, namely DNS, Memcached, and HTTP, highlights that Santa can substantially improve the server performance by a factor of 5.5, 2.1, and 2.5, respectively.
AbstractAs network speed increases, servers struggle to serve all requests directed at them. This challenge is rooted in a partitioned data path where the split between the kernel space networking stack and user space applications induces overheads. To address this challenge, we propose Santa, an architecture to optimize the data path by enabling server applications to (partially) offload packet processing to a generic rule processor. We exemplify Santa by showing how it can drastically accelerate UDP packet processing in the Linux kernel—a currently neglected domain. Our evaluation focuses on accelerating DNS traffic for which we find a performance increase by a factor of 5.5 on realworld request pattern.
The Internet of Things (IoT) permeates our everyday life, e.g., in the area of health monitoring, wearables, industry, and home automation. It comprises devices that provide only limited resources, operate in challenging network conditions, and are often battery-powered. To embed these devices into the Internet, they are envisioned to operate standard protocols. Yet, these protocols occupy the majority of limited program memory resources. Thus, devices can neither add application logic nor apply security updates or adopt optimizations for efficiency. This problem will further exacerbate in the future as the further ongoing permeation of smart devices in our environment demands for more and more functionality.
To overcome limited functionality due to resource constraints, we show that not all functionality is required in parallel, and thus can be SPLIT in a feasible manner. This enables on-demand loading of functionality outsourced as (multiple) modules to the significantly lesser constrained flash storage of devices. We exemplify efficient modularization of DTLS and show that SPLIT enables operation of large protocol stacks while it incurs reasonable, tunable performance trade-offs. Our use case specific results show an initial runtime overhead of 23.34 % and 4.9 % for subsequent protocol executions.
AbstractVarious Internet of Things and Industry 4.0 use cases such as city-wide monitoring, Smart Grid control, or machine control, require low-latency distributed processing of continuous data streams. This fact has boosted research on making Stream Processing Frameworks (SPFs) IoT-ready, meaning that their cloud and IoT service management mechanisms (e.g., task placement, load balancing, algorithm selection) need to consider new requirements derived from IoT-specific characteristics, i.e., ultra low latency due to physical interactions. Although various extensions have appeared to optimize such SPF-provided mechanisms, they still lack the modules, data models, and algorithms to properly handle algorithm selection in IoT deployments. The algorithm selection problem refers to selecting dynamically which internal logic a deployed streaming task should use in case of various alternatives. To the best of our knowledge, this work is the first solution that adds this capability to SPFs. Our solution is based on i) architectural extensions of typical SPF middleware, ii) a new schema for characterizing algorithmic performance in the targeted context, and iii) a streaming-specific optimization problem formulation. We implemented our solution as an extension to Apache Storm and demonstrate how it can reduce stream processing latency by up to a factor of 2.9 in the tested scenarios.
Database architectures have fundamentally advanced (in-memory, parallelization, distribution) to support faster query execution and to manage higher workloads. Due to these advances, networking overheads have now become a new performance challenge, which is currently only tackled by Remote Direct Memory Access (RDMA). Fortunately, networking has recently made considerable latency and throughput improvements via kernel optimizations or by employing new architectures, e.g., kernel bypassing or specialized hardware.
We show that improved networking architectures indeed offer substantial—and currently unexplored—potential for database performance improvements regarding throughput, CPU-load, end-to-end and tail latency. To prove this potential, we used Santa, a packet processing offloading engine exemplarily implemented in the Linux kernel. Our results clearly remark reduced end-to-end latencies and throughput increases for both Memcached and MySQL by a factor of up to 1.5 and 3.3, respectively.