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Auflistung nach Autor:in "Schiller, Fabian"

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  • Konferenzbeitrag
    A Corpus of Memes from Reddit: Acquisition, Preparation and First Case Studies
    (INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Schmidt, Thomas; Schiller, Fabian; Götz, Matthias; Wolff, Christian
    We present a corpus of memes and their textual components that were acquired from the popular meme platform r\memes, a subreddit of Reddit and one of the major outlets of online meme culture. The corpus consists of the most popular memes from 2013-2021 on the platform and we acquired 11,701 memes and 280,351 text tokens. We conduct several case studies focused on diachronic analysis to highlight the possibilities of the corpus for research in internet studies and online culture. We examine the general activity on the platform throughout the years and identify a significant increase in meme production beginning 2017. Results of sentiment analysis show a tendency towards memes with positively classified texts. The analysis of most frequent words per half-year spotlights the importance of certain cultural events for meme culture (e.g. the 2016 US election). Using the LIWC to analyze swear and sexual words shows an overall decrease in the usage of these words pointing to an increased moderation of the platform. The corpus is publicly available for the research community for further studies.
  • Konferenzbeitrag
    Using Artificial Neural Networks to Compensate Negative Effects of Latency in Commercial Real-Time Strategy Games
    (Mensch und Computer 2022 - Tagungsband, 2022) Halbhuber, David; Seewald, Maximilian; Schiller, Fabian; Götz, Mathias; Fehle, Jakob; Henze, Niels
    Cloud-based game streaming allows gamers to play Triple-A games on any device, anywhere, almost instantly. However, they entail one major disadvantage - latency. Latency, the time between input and output, worsens the players’ experience and performances. Reduc same game experience as in local gaming. Previous work demonstrates that deep learning-based techniques can compensate for a game’s latency if the artificial neural network has access to the game’s internal state during inference. However, it is unclear if deep learning can be used to compensate for the latency of unmodified commercial video games. Hence, this work investigates the use of deep learning-based latency compensation in commercial video games. In a first study, we collected data from 21 participants playing real-time strategy games. We used the data to train two artificial neural networks. In a second study with 12 participants, we compared three different scenarios: (1) playing without latency, (2) playing with 50 ms of controlled latency, and (3) playing with 50 ms latency fully compensated by our system. Our results show that players associated the gaming session with less negative feelings and were less tired when supported by our system. We conclude that deep learning-based latency compensation can compensate the latency of commercial video games without accessing the internal state of the game. Ultimately, our work enables cloud-based game streaming providers to offer gamers a better and more responsive gaming experience.