TY - GEN A1 - Reelfs, Jens Helge A1 - Mohaupt, Timon A1 - Hohlfeld, Oliver A1 - Henckell, Niklas T1 - Hashtag Usage in a Geographically-Local Microblogging App T2 - Proceedings of the 2019 World Wide Web Conference (WWW '19 Companion), 9th International Workshop on Location and the Web (LocWeb '19), May 13–17, 2019, San Francisco, CA, USA Y1 - 2019 UR - https://arxiv.org/abs/1903.04272 SN - 978-1-4503-6675-5 U6 - https://doi.org/10.1145/3308560.3316537 SP - 919 EP - 927 PB - ACM CY - New York ER - TY - GEN A1 - Reelfs, Jens Helge A1 - Hohlfeld, Oliver A1 - Strohmaier, Markus A1 - Henckell, Niklas T1 - Word-Emoji embeddings from large scale messaging data reflect real world semantic associations of expressive icons T2 - Conference: ICWSM Workshop on Emoji Understanding and Applications in Social Media 2020 N2 - 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. Y1 - 2020 UR - http://workshop-proceedings.icwsm.org/pdf/2020_02.pdf ER - TY - GEN A1 - Reelfs, Jens Helge A1 - Bergmann, Max A1 - Hohlfeld, Oliver A1 - Henckell, Niklas T1 - Understanding & Predicting User Lifetime with Machine Learning in an Anonymous Location-Based Social Network T2 - Companion Proceedings of the Web Conference 2021 N2 - 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. Y1 - 2021 UR - https://arxiv.org/abs/2103.01300 SN - 978-1-4503-8313-4 U6 - https://doi.org/10.1145/3442442.3451887 VL - 2021 PB - ACM CY - Ljubljana, Slovenia ER - TY - GEN A1 - Reelfs, Jens Helge A1 - Hohlfeld, Oliver A1 - Henckell, Niklas T1 - Geographic Differences in Social Media Interactions Exist Between Western and Middle-East Countries T2 - Passive and Active Measurement Conference 2022 N2 - 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. Y1 - 2022 SN - 978-3-030-98785-5 SN - 978-3-030-98784-8 U6 - https://doi.org/10.1007/978-3-030-98785-5_18 SP - 411 EP - 425 PB - Springer CY - Cham ER - TY - GEN A1 - Reelfs, Jens Helge A1 - Hohlfeld, Oliver A1 - Henckell, Niklas T1 - Anonymous Hyperlocal Communities: What do they talk about? T2 - 12th International Workshop on Location and the Web (LocWeb'22) N2 - In 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. Y1 - 2022 UR - https://arxiv.org/abs/2203.05657 U6 - https://doi.org/10.48550/arXiv.2203.05657 ER - TY - GEN A1 - Reelfs, Jens Helge A1 - Hohlfeld, Oliver A1 - Strohmaier, Markus A1 - Henckell, Niklas T1 - Characterizing the country-wide adoption and evolution of the Jodel messaging app in Saudi Arabia T2 - arXiv N2 - 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. Y1 - 2022 UR - https://arxiv.org/abs/2205.04544 U6 - https://doi.org/10.48550/arXiv.2205.04544 ER -