The socialization of the Web changes the ways we behave both online and offline, leading to a novel emergent phenomenon called ``collective attention'' in which people's attention is suddenly concentrated on a particular real-life event. Visualizing collective attention is fundamental to understand human behavior in the digital age. Here we propose ``association networks'' to visualize usage-based, term-association patterns in a large dataset of tweets (short text messages) during collective attention events. First, we train the word2vec model to obtain vector representations of terms (words) based on semantic similarities, and then construct association networks: given some terms as seeds, the associated terms are linked with each other using the trained word2vec model, and considering the resulting terms as new seeds, the same procedure is repeated. Using two sets of Twitter data---the 2011 Japan earthquake and the 2011 FIFA Women's World Cup---we demonstrate how association networks visualize collective attention on these events. Provided the Japan earthquake dataset, the association networks that emerged from the most frequently used terms, such as earthquake and tsunami, exhibit distinct network structure related to people's attention during the earthquake, whereas one that emerged from emotion-related terms, such as great and terrible, shows a large connected cluster of negative terms and small clusters of positive terms. Furthermore, we compare association networks in different datasets, using the same seed terms. These results indicate the proposed method to be a useful tool for visualizing the implicit nature of collective attention that is otherwise invisible.
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