arXiv subjects

Yukie Sano

Publications and source records attributed to Yukie Sano.

14 recordsLinked to original sources

arXiv2025

Linking Opinion Dynamics and Emotional Expression in Online Communities: A Case Study of COVID-19 Vaccination Discourse in Japan

Social media discourse on COVID-19 vaccination provides a valuable context for studying opinion formation, emotional expression, and social influence during a global crisis. While prior studies have examined emotional strategies within communities and the link between emotions and vaccine hesitancy, few have investigated dynamic emotion changes across collective, community, and individual levels. In this study, we address this gap by conducting an integrated analysis of the evolving collective emotions, community affiliations, and individual emotion changes associated with opinion shifts. Our results show that collective emotions exhibit distinct trends in response to vaccination progress. Emotional compositions differ across communities and respond dynamically to changing pandemic circumstances, potentially reflecting the communities' influence on users' opinions. At the individual level, users shifting to pro-vaccine opinions display markedly different emotional changes compared to those shifting toward anti-vaccine opinions. Together, these findings highlight the central role of emotions in shaping users' vaccination opinions.

cs.SI↗

arXiv2025

Stable Emotional Co-occurrence Patterns Revealed by Network Analysis of Social Media

Examining emotion interactions as an emotion network in social media offers key insights into human psychology, yet few studies have explored how fluctuations in such emotion network evolve during crises and normal times. This study proposes a novel computational approach grounded in network theory, leveraging large-scale Japanese social media data spanning varied crisis events (earthquakes and COVID-19 vaccination) and non-crisis periods over the past decade. Our analysis identifies and evaluates links between emotions through the co-occurrence of emotion-related concepts (words), revealing a stable structure of emotion network across situations and over time at the population level. We find that some emotion links (represented as link strength) such as emotion links associated with Tension are significantly strengthened during earthquake and pre-vaccination periods. However, the rank of emotion links remains highly intact. These findings challenge the assumption that emotion co-occurrence is context-based and offer a deeper understanding of emotions' intrinsic structure. Moreover, our network-based framework offers a systematic, scalable method for analyzing emotion co-occurrence dynamics, opening new avenues for psychological research using large-scale textual data.

cs.SI↗

arXiv2025

Dissecting the gender divide: Authorship and acknowledgment in scientific publications

The issue of gender bias in scientific publications has been a topic of ongoing debate. One aspect of this debate concerns whether women receive equal credit for their contributions compared to men. Conventional wisdom suggests that women are more likely to be acknowledged than listed as co-authors. In this study, we analyze data from over 20,000 authors and 60,000 acknowledged individuals across nine disciplines in open-access journals. Our results confirm persistent gender disparities: women are more frequently acknowledged than credited as co-authors, especially in roles involving investigation and analysis. To account for status and disciplinary effects, we examined collaboration pair composed of highly cited and less-cited scholars. In collaborations, highly cited scholars are more likely to be listed as an author regardless of gender. Notably, highly cited women in such pairs are even more likely to be co-authors than their men counterparts. Our findings suggest that power dynamics and perceived success heavily influence the distribution of credit in scientific publishing. These results underscore the role of status dynamics in shaping authorship and call for a more nuanced understanding of how gender, power, and recognition interact in scientific publishing. Our findings offer valuable insights for scholars, editors, and funding committed to advancing equity in science.

cs.DL↗

arXiv2025

Analyzing the Safety of Japanese Large Language Models in Stereotype-Triggering Prompts

In recent years, Large Language Models have attracted growing interest for their significant potential, though concerns have rapidly emerged regarding unsafe behaviors stemming from inherent stereotypes and biases. Most research on stereotypes in LLMs has primarily relied on indirect evaluation setups, in which models are prompted to select between pairs of sentences associated with particular social groups. Recently, direct evaluation methods have emerged, examining open-ended model responses to overcome limitations of previous approaches, such as annotator biases. Most existing studies have focused on English-centric LLMs, whereas research on non-English models, particularly Japanese, remains sparse, despite the growing development and adoption of these models. This study examines the safety of Japanese LLMs when responding to stereotype-triggering prompts in direct setups. We constructed 3,612 prompts by combining 301 social group terms, categorized by age, gender, and other attributes, with 12 stereotype-inducing templates in Japanese. Responses were analyzed from three foundational models trained respectively on Japanese, English, and Chinese language. Our findings reveal that LLM-jp, a Japanese native model, exhibits the lowest refusal rate and is more likely to generate toxic and negative responses compared to other models. Additionally, prompt format significantly influence the output of all models, and the generated responses include exaggerated reactions toward specific social groups, varying across models. These findings underscore the insufficient ethical safety mechanisms in Japanese LLMs and demonstrate that even high-accuracy models can produce biased outputs when processing Japanese-language prompts. We advocate for improving safety mechanisms and bias mitigation strategies in Japanese LLMs, contributing to ongoing discussions on AI ethics beyond linguistic boundaries.

cs.CL↗

arXiv2025

Quantifying Collective Emotions: Japan's Societal Trends Through Enhanced Sentiment Index Using POMS2 and SNS

In this study, we constructed an emotion index that quantitatively represents the collective emotions present in the Japanese web space by utilizing Social Networking Service (SNS) post data. Building upon previous research that used blog data and the Profile of Mood States (POMS), we restructured the methodology using posts from X (formerly Twitter) and updated the model by adding the ``Friendliness" indicator from the POMS2 metrics. Through periodic and trend analyses of the emotional indicators derived from X's post data, we found that the extension is consistent with results previously reported using blog data. This suggests that our methodology effectively captures typical emotional fluctuations in Japanese society, independent of specific SNS platforms, and is expected to serve as an index to visualize societal trends.

cs.SI↗

arXiv2022

Burstiness of human physical activities and their characterization

Human behaviour is heterogeneous and temporally fluctuates. Many studies have focused on inter-event time (IET) fluctuations and have reported that the IET distributions have a long-tailed distribution, which cannot be explained by a stationary Poisson point process. Such phenomenon observed in IET distributions are known as burstiness. Burstiness has also been reported for human physical activity, but the mechanism underlying it has not been clarified. In this study, we collected human physical activity data while specifying the age of the subjects and their situations (for example, children's play and adults' housework), and we analysed their event time-series data. We confirmed the burstiness in both children and adults. For the first time, burstiness studied in physical activities of children between the ages 2 and 5. We also confirmed that the characteristics of the IET distribution are unique to each activity situation. Our results may be critical in the identification of the burstiness mechanisms in human physical activity.

physics.soc-ph↗

arXiv2022

A two-phase model of collective memory decay with a dynamical switching point

Public memories of significant events shared within societies and groups have been conceptualized and studied as collective memory since the 1920s. Thanks to the recent advancement in digitization of public-domain knowledge and online user behaviors, collective memory has now become a subject of rigorous quantitative investigation using large-scale empirical data. Earlier studies, however, typically considered only one dynamical process applied to data obtained in just one specific event category. Here we propose a two-phase mathematical model of collective memory decay that combines exponential and power-law phases, which represent fast (linear) and slow (nonlinear) decay dynamics, respectively. We applied the proposed model to the Wikipedia page view data for articles on significant events in five categories: earthquakes, deaths of notable persons, aviation accidents, mass murder incidents, and terrorist attacks. Results showed that the proposed two-phase model compared favorably with other existing models of collective memory decay in most of the event categories. The estimated model parameters were found to be similar across all the event categories. The proposed model also allowed for detection of a dynamical switching point when the dominant decay dynamics exhibit a phase shift from exponential to power-law. Such decay phase shifts typically occurred about 10 to 11 days after the peak in all of the five event categories.

physics.soc-ph↗

arXiv2021

Citations and gender diversity in reciprocal acknowledgement networks

Acknowledgements in scientific articles suggest not only gratitude, but also the interactions among scientists. In this study, we examine the acknowledgement interactions employing data from open-access journals (PLOS series). We built an acknowledgement network where the nodes represent authors and acknowledged people, while the links correspond to being mentioned in acknowledgements. Employing motif analysis, we showed how acknowledgement networks have developed, and how reciprocal relationships tend to emerge. To better understand these reciprocal relationships, we analysed the reciprocal sub-graphs of acknowledgement from two perspectives: citations and gender diversity. Firstly, we counted the number of citations, from both reciprocal and non-reciprocal authors. We found that reciprocal authors predominantly tend to cite other reciprocal authors rather than non-reciprocal ones. For gender diversity, we found that reciprocal pairs that include females tend to emerge more than male-male pairs of reciprocity in various fields, despite the fewer number of females.

physics.soc-ph↗

arXiv2019

Fake news propagate differently from real news even at early stages of spreading

Social media can be a double-edged sword for society, either as a convenient channel exchanging ideas or as an unexpected conduit circulating fake news through a large population. While existing studies of fake news focus on theoretical modeling of propagation or identification methods based on machine learning, it is important to understand the realistic mechanisms between theoretical models and black-box methods. Here we track large databases of fake news and real news in both, Weibo in China and Twitter in Japan from different culture, which include their complete traces of re-postings. We find in both online social networks that fake news spreads distinctively from real news even at early stages of propagation, e.g. five hours after the first re-postings. Our finding demonstrates collective structural signals that help to understand the different propagation evolution of fake news and real news. Different from earlier studies, identifying the topological properties of the information propagation at early stages may offer novel features for early detection of fake news in social media.

physics.soc-ph↗

arXiv2019

Identifying long-term periodic cycles and memories of collective emotion in online social media

Collective emotion has been traditionally evaluated by questionnaire survey on a limited number of people. Recently, big data of written texts on the Internet has been available for analyzing collective emotion for very large scales. Although short-term reflection between collective emotion and real social phenomena has been widely studied, long-term dynamics of collective emotion has not been studied so far due to the lack of long persistent data sets. In this study, we extracted collective emotion over a 10-year period from 3.6 billion Japanese blog articles. Firstly, we find that collective emotion shows clear periodic cycles, i.e., weekly and seasonal behaviors, accompanied with pulses caused by natural disasters. For example, April is represented by high Tension, probably due to starting school in Japan. We also identified long-term memory in the collective emotion that is characterized by the power-law decay of the autocorrelation function over several months.

cs.SI↗

arXiv2018

Configuration model for correlation matrices preserving the node strength

Correlation matrices are a major type of multivariate data. To examine properties of a given correlation matrix, a common practice is to compare the same quantity between the original correlation matrix and reference correlation matrices, such as those derived from random matrix theory, that partially preserve properties of the original matrix. We propose a model to generate such reference correlation and covariance matrices for the given matrix. Correlation matrices are often analysed as networks, which are heterogeneous across nodes in terms of the total connectivity to other nodes for each node. Given this background, the present algorithm generates random networks that preserve the expectation of total connectivity of each node to other nodes, akin to configuration models for conventional networks. Our algorithm is derived from the maximum entropy principle. We will apply the proposed algorithm to measurement of clustering coefficients and community detection, both of which require a null model to assess the statistical significance of the obtained results.

physics.soc-ph↗

arXiv2016

Statistical properties of fluctuations of time series representing the appearance of words in nationwide blog data and their applications: An example of observations and the modelling of fluctuation scalings of nonstationary time series

To elucidate the non-trivial empirical statistical properties of fluctuations of a typical non-steady time series representing the appearance of words in blogs, we investigated approximately five billion Japanese blogs over a period of six years and analyse some corresponding mathematical models. First, we introduce a solvable non-steady extension of the random diffusion model, which can be deduced by modelling the behaviour of heterogeneous random bloggers. Next, we deduce theoretical expressions for both the temporal and ensemble fluctuation scalings of this model, and demonstrate that these expressions can reproduce all empirical scalings over eight orders of magnitude. Furthermore, we show that the model can reproduce other statistical properties of time series representing the appearance of words in blogs, such as functional forms of the probability density and correlations in the total number of blogs. As an application, we quantify the abnormality of special nationwide events by measuring the fluctuation scalings of 1771 basic adjectives.

physics.soc-ph↗

arXiv2012

Empirical analysis of collective human behavior for extraordinary events in blogosphere

To uncover underlying mechanism of collective human dynamics, we survey more than 1.8 billion blog entries and observe the statistical properties of word appearances. We focus on words that show dynamic growth and decay with a tendency to diverge on a certain day. After careful pretreatment and fitting method, we found power laws generally approximate the functional forms of growth and decay with various exponents values between -0.1 and -2.5. We also observe news words whose frequency increase suddenly and decay following power laws. In order to explain these dynamics, we propose a simple model of posting blogs involving a keyword, and its validity is checked directly from the data. The model suggests that bloggers are not only responding to the latest number of blogs but also suffering deadline pressure from the divergence day. Our empirical results can be used for predicting the number of blogs in advance and for estimating the period to return to the normal fluctuation level.

physics.soc-ph↗

arXiv2010

Macroscopic and microscopic statistical properties observed in blog entries

We observe the statistical properties of blogs that are expected to reflect social human interaction. Firstly, we introduce a basic normalization preprocess that enables us to evaluate the genuine word frequency in blogs that are independent of external factors such as spam blogs, server-breakdowns, increase in the population of bloggers, and periodic weekly behaviors. After this process, we can confirm that small frequency words clearly follow an independent Poisson process as theoretically expected. Secondly, we focus on each blogger's basic behaviors. It is found that there are two kinds of behaviors of bloggers. Further, Zipf's law on word frequency is confirmed to be universally independent of individual activity types.

physics.soc-ph↗