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Online social networks offer vast opportunities for computational socialscience, but effective user embedding is crucial for downstream tasks.Traditionally, researchers have used pre-defined network-based user features,such as degree, and centrality measures, and/or content-based features, such asposts and reposts. However, these measures may not capture the complexcharacteristics of social media users. In this study, we propose a userembedding method based on the URL domain co-occurrence network, which is simplebut effective for representing social media users in competing events. Weassessed the performance of this method in binary classification tasks usingbenchmark datasets that included Twitter users related to COVID-19 infodemictopics (QAnon, Biden, Ivermectin). Our results revealed that user embeddingsgenerated directly from the retweet network, and those based on language,performed below expectations. In contrast, our domain-based embeddingsoutperformed these methods while reducing computation time. These findingssuggest that the domain-based user embedding can serve as an effective tool tocharacterize social media users participating in competing events, such aspolitical campaigns and public health crises.

Domain-based user embedding for competing events on social media