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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 society. While traditional platforms implement user profiles enabling social credit, the landscape also includes anonymity. Yet, a new type of application 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, we provide a first data-driven holistic view on Jodel that combines both properties. We leverage unbiased complete ground truth information to dissect a plethora of communities across two different countries: Germany and 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 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 the very same platform ingredients may yield vastly different outcomes on the messaging app Jodel. While any online platform builds upon 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 a different society, identifying 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 and 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.
Within the last decades, the number of social networks is growing fast. The competition of retaining the customers to grow their platform and increase their profitability is rising. That is why companies need to detect possible churners to retain these. The problem of predicting the users’ lifetime, churning users, and the reasons for churning can be tackled by using machine learning.
The goal of this bachelor thesis is to build machine learning models to predict user churn and the user lifetime within the social network Jodel, a location-based anonymous messaging application for Android and iOS.
To get the best possible prediction results, we have started with extensive literature research, whose approaches we have tested and added to a machine learning pipeline to build predictive models. With these models, we have investigated the performance after different observation time windows and have finally compared the strongest models to detect similarities and understand the insights to learn their behaviour.
The results of this thesis are machine learning models for a selected representative set of communities varying in size within the Kingdom of Saudi Arabia and a country model leveraging all data. These models are used for a regression task by predicting the lifetime of a user and a multi-label classification of a user into six different churn classes. Additionally, we have also given models for a binary classification, where the model will predict if the user will churn within a given time or not. These models have shown general strong predictive power, which is shrinking when limiting the observation time window. Especially the binary classification yielded high accuracy of over 99%.
The best models have been used for predicting user churn within other communities to detect communities with possible similar behaviour. These similarities then have been determined by features’ importance, where the most important features have got fed back into empirics. This has shown statistically significant differences between user groups with a different active time but as of today no clear trends were visible that had led us to define the communities’ behaviours.
Since the competition of social networks is still growing, the retaining of users will stay a core marketing strategy, which will need to be tackled by machine learning and artificial intelligence. The created models could be useful for predicting churning users within the platform Jodel to detect these customers that will churn within a given time.
Researches did not focus much on anonymous and location-based messaging. That is why the results of this thesis on the anonymous messaging application Jodel opens a variety of possible tasks for the future in this context.