Pokémon GO is a popular location-based mobile game that seeks to inspire players to be more active, socialize physically and virtually, and spend more time outside. With the onset of the COVID-19 pandemic, several game mechanics of Pokémon GO were changed to accommodate socially-distanced play. This research aims to understand the impacts of the pandemic and subsequent game adjustments on user perceptions of the game. We used an exploratory mixed-method approach, a machine learning technique (Latent Dirichlet Allocation) for topic modeling, and thematic analysis for qualitative coding of top-level Reddit comments to identify whether and how the social distancing approach changes the players' behaviors. The results demonstrate that players were less physically active, less eager to discover, and more interested in remote social practices. We discuss which players leverage social distancing changes and reflect on key game features that provide a better gaming experience in the age of remote play.
Generative Adversarial Networks (GANs) can accurately model complex multi-dimensional data and generate realistic samples. However, due to their implicit estimation of data distributions, their evaluation is a challenging task. The majority of research efforts associated with tackling this issue were validated by qualitative visual evaluation. Such approaches do not generalize well beyond the image domain. Since many of those evaluation metrics are proposed and bound to the vision domain, they are difficult to apply to other domains. Quantitative measures are necessary to better guide the training and comparison of different GANs models. In this work, we leverage Siamese neural networks to propose a domain-agnostic evaluation metric: (1) with a qualitative evaluation that is consistent with human evaluation, (2) that is robust relative to common GAN issues such as mode dropping and invention, and (3) does not require any pretrained classifier. The empirical results in this paper demonstrate the superiority of this method compared to the popular Inception Score and are competitive with the FID score.
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