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This 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.

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 tradeoffs, 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 𝑥) 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. CCS CONCEPTS• Networks → Online social networks; Location based services; • Computing methodologies → Classification and regression trees.This paper is published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license. Authors reserve their rights to disseminate the work on their personal and corporate Web sites with the appropriate attribution.

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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.