Helge Reelfs

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.

A 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.

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 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.