Concurrent Bursty Behavior of Social Sensors in Sporting Events

The advent of social media expands our ability to transmit information and connect with others instantly, which enables us to behave as "social sensors." Here, we studied concurrent bursty behavior of Twitter users during major sporting events to determine their function as social sensors. We show that the degree of concurrent bursts in tweets (posts) and retweets (re-posts) works as a strong indicator of winning or losing a game. More specifically, our simple tweet analysis of Japanese professional baseball games in 2013 revealed that social sensors can immediately react to positive and negative events through bursts of tweets, but that positive events are more likely to induce a subsequent burst of retweets. We also show that these findings hold true across cultures by analyzing tweets related to Major League Baseball games in 2015. Furthermore, we demonstrate active interactions among social sensors by constructing retweet networks during a baseball game. The resulting networks commonly exhibited user clusters depending on the baseball team, with a scale-free connectedness that is indicative of a substantial difference in user popularity as an information source. While previous studies have mainly focused on bursts of tweets as a simple indicator of a real-world event, the temporal correlation between tweets and retweets implies unique aspects of social sensors, offering new insights into human behavior in a highly connected world.Comment: 17 pages, 8 figure

Domain-based user embedding for competing events on social media

Online social networks offer vast opportunities for computational social science, 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 as posts and reposts. However, these measures may not capture the complex characteristics of social media users. In this study, we propose a user embedding method based on the URL domain co-occurrence network, which is simple but effective for representing social media users in competing events. We assessed the performance of this method in binary classification tasks using benchmark datasets that included Twitter users related to COVID-19 infodemic topics (QAnon, Biden, Ivermectin). Our results revealed that user embeddings generated directly from the retweet network, and those based on language, performed below expectations. In contrast, our domain-based embeddings outperformed these methods while reducing computation time. These findings suggest that the domain-based user embedding can serve as an effective tool to characterize social media users participating in competing events, such as political campaigns and public health crises.Comment: Computational social science applicatio

Detecting directional forces in the evolution of grammar: A case study of the English perfect using EEBO, COHA, and Google Books

Languages have diverse characteristics that have emerged through evolution. In modern English grammar, the perfect is formed with \textit{have}+PP (past participle), but in earlier English the \textit{be}+PP form also existed. It is widely recognised that the auxiliary verb BE was replaced by HAVE throughout evolution, except for some special cases. However, whether this evolution was caused by natural selection or random drift is still unclear. Here we examined directional forces in the evolution of the English perfect by combining three large-scale data sources: EEBO (Early English Books Online), COHA (Corpus of Historical American English), and Google Books. We found that most intransitive verbs exhibited an apparent transition from \textit{be}+PP to \textit{have}+PP, most of which were classified as `selection' by a deep neural network-based model. These results suggest that the English perfect could have evolved through natural selection rather than random drift, and provide insights into the cultural evolution of grammar.Comment: 22 pages, 3 figures, 4 tables, with S

Development and Validation of the Japanese Moral Foundations Dictionary

The Moral Foundations Dictionary (MFD) is a useful tool for applying the conceptual framework developed in Moral Foundations Theory and quantifying the moral meanings implicated in the linguistic information people convey. However, the applicability of the MFD is limited because it is available only in English. Translated versions of the MFD are therefore needed to study morality across various cultures, including non-Western cultures. The contribution of this paper is two-fold. We developed the first Japanese version of the MFD (referred to as the J-MFD) by introducing a semi-automated method---this serves as a reference when translating the MFD into other languages. We next tested the validity of the J-MFD by analyzing open-ended written texts about the situations that Japanese participants thought followed and violated the five moral foundations. We found that the J-MFD correctly categorized the Japanese participants' descriptions into the corresponding moral foundations, and that the Moral Foundations Questionnaire (MFQ) scores were correlated with the frequency of situations, of total words, and of J-MFD words in the participants' descriptions for the Harm and Fairness foundations. The J-MFD can be used to study morality unique to the Japanese and cultural differences in moral behavior.Comment: 14 pages, 2 figures, 2 table