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Yoshihisa Kashima

Publications and source records attributed to Yoshihisa Kashima.

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An agent-based model of the formation and evolution of common ground

The existence of a communal common ground is vital for collective action and coordination in a population, but the micro-level cognitive and social processes that lead to the formation and evolution of common ground at the macro-level are undertheorised and have not been rigorously explored. In this work, we adopt a formal approach and develop an agent-based model that describes repeated grounding attempts between agents interacting on a network, with an explicit distinction between a sender agent and a receiver agent during an interaction involving sharing information. Several key novel features enable us to capture a range of different interaction contexts: we allow for the interaction to result in either acceptance or rejection, the receiver's response may be lost to the sender, and the sender can interpret this lack of response as either acceptance or rejection (or even something in between). A campaign of Monte Carlo simulations reveals how different interaction contexts, as well as the available information for sharing, result in different emergent phenomena, such as a global communal common ground, fragmentation into multiple clusters of differing common ground, and even the total loss of any shared common ground. This work highlights the potential for using mathematical models to study micro-macro links in cultural dynamics, including identifying ways to facilitate interactions to foster the emergence of a global communal common ground.

physics.soc-ph

To be a pro-vax or not, the COVID-19 vaccine conundrum on Twitter

The most surprising observation reported by the study in (arXiv:2208.13523), involving stance detection of COVID-19 vaccine related tweets during the first year of pandemic, is the presence of a significant number of users (~2 million) who posted tweets with both anti-vax and pro-vax stances. This is a sizable cohort even when the stance detection noise is considered. In this paper, we tried to get deeper understanding of this 'dual-stance' group. Out of this group, 60% of users have more pro-vax tweets than anti-vax tweets and 17% have the same number of tweets in both classes. The rest have more anti-vax tweets, and they were highly active in expressing concerns about mandate and safety of a fast-tracked vaccine, while also tweeted some updates about vaccine development. The leaning pro-vax group have opposite composition: more vaccine updates and some posts about concerns. It is important to note that vaccine concerns were not always genuine and had a large dose of misinformation. 43% of the balanced group have only tweeted one tweet of each type during our study period and are the less active participants in the vaccine discourse. Our temporal study also shows that the change-of-stance behaviour became really significant once the trial results of COVID-19 vaccine were announced to the public, and it appears as the change of stance towards pro-vax is a reaction to people changing their opinion towards anti-vax. Our study finished at Mar 23, 2021 when the conundrum was still going strong. The dilemma might be a reflection of the uncertain and stressful times, but it also highlights the importance of building public trust to combat prevalent misinformation.

cs.SI

Demystifying the COVID-19 vaccine discourse on Twitter

Developing an understanding of the public discourse on COVID-19 vaccination on social media is important not only for addressing the current COVID-19 pandemic, but also for future pathogen outbreaks. We examine a Twitter dataset containing 75 million English tweets discussing COVID-19 vaccination from March 2020 to March 2021. We train a stance detection algorithm using natural language processing (NLP) techniques to classify tweets as `anti-vax' or `pro-vax', and examine the main topics of discourse using topic modelling techniques. While pro-vax tweets (37 million) far outnumbered anti-vax tweets (10 million), a majority of tweets from both stances (63% anti-vax and 53% pro-vax tweets) came from dual-stance users who posted both pro- and anti-vax tweets during the observation period. Pro-vax tweets focused mostly on vaccine development, while anti-vax tweets covered a wide range of topics, some of which included genuine concerns, though there was a large dose of falsehoods. A number of topics were common to both stances, though pro- and anti-vax tweets discussed them from opposite viewpoints. Memes and jokes were amongst the most retweeted messages. Whereas concerns about polarisation and online prevalence of anti-vax discourse are unfounded, targeted countering of falsehoods is important.

cs.SI