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Abstract Consumer-generated media (CGM), including social live streaming service (SLSS), often incorporate gamification elements to engage users. Online virtual tips and gifts, such as “bits” in Twitch, exemplify this approach by fostering interactive dynamics between live streamers and their audience. However, issues remain in understanding the impact of (virtual) tips on user behavior and how platform commissions, which are partly collected from the tips provided by other users, influence the platform’s overall revenue and user behavioral strategies. This study considers a CGM/SLSS platform, such as Twitch, as a variation of the public goods game and proposes a game-theoretical model integrated with a platform-mediated tipping system with a certain commission rate. In experiments, the proposed model was applied to artificial and actual SLSS networks, demonstrating that the influence of a tipping system on user behavior is significant, impacting the motivation for live streaming and the effort to improve its quality. Depending on individual preferences for psychological or monetary reward, the commission rate is determined by the platform depending on the users’ network positions, such as the degree of connectivity, which corresponds to the number of followers/followees in SLSS networks. Our findings offer valuable insights into the design and management of SLSS platforms and further suggest that there may be an appropriate commission rate to maximize platform revenue.

Abstract Real-world networks often encounter disruptions that can have profound societal impacts. Therefore, enhancing network robustness against random failures and targeted attacks is crucial. Bypass rewiring, in which adjacent nodes are immediately reconnected following node removal, has been proposed as a promising method for enhancing network robustness. However, this method typically requires numerous bypass links and incurs significant costs. In this paper, we propose link-limited bypass rewiring, in which bypass links are reconnected stochastically. Additionally, we investigate the relationship between the number of bypass links and robustness improvement. Our findings, which are based on analytical and numerical results, reveal a tradeoff relationship between the number of added bypass links and robustness enhancement. Notably, preferentially reconnecting high-degree nodes was found to be effective for improving robustness. These insights can inform the design of resilient systems in real-world networks, offering strategies for enhancing robustness against node removal.

Abstract The Russian government has long since engaged in an information campaign of propaganda and disinformation as a major part of foreign policy. This has been taken to new heights since the invasion of Ukraine in February 2022. In this study, we investigate pro-Russian misinformation within the opening weeks of the invasion in 6 languages: English, Japanese, Spanish, French, German, and Korean. Using Twitter data, we apply a combination of network and language embedding models to identify popular topics of misinformation amongst users in each language. Despite English users forming the most dominant language base on Twitter, we find that the popularity of misinformation in Japanese regularly outstrips English for certain topics. Misinformation shared by Spanish users is also over-represented in proportion to its much smaller user base. Our results provide insight into the current state of misinformation in each language. While we discuss some of the possible drivers behind the factors such as language over-representation, our study also highlights the need for further cross-lingual misinformation research in order to better understand this phenomena in a truly global context.

On many online platforms, more items are being added every day, requiring users to select efficiently the desired item from among these candidates. In similar environments, recommender systems have been introduced, allowing users to select from among recommended items. While the immediate advantages of recommender systems, such as presenting users with pertinent information efficiently and boosting page views, are well recognized, concerns have been raised that these systems might constrict users’ choices and amplify echo chambers. Despite these concerns, the detrimental impacts of recommender systems on user behavior have not been thoroughly investigated. To address the societal challenges stemming from the limitation in the breadth of information to which users are exposed, a novel recommender system offering a wider array of choices is essential. In this study, we examine the user experience of the Nikkei Electronic Version, a major news delivery service in Japan, from the perspective of diversity. Using the language of article titles, we evaluate the diversity of recommended results and click logs presented to users. In addition to an analysis of the actual service, we conduct user experiments with participants recruited from among users of the service to investigate the effect of recommendation algorithms specialized in increasing diversity, separate from the actual service. We propose a recommender system based on the concept of extracting articles as far apart as possible within the language space. Through static experiments and user experiments, we show that the proposed recommender system is effective in diversifying the recommendation list. Furthermore, we show that when candidate articles that are likely to be clicked can be properly extracted, the proposed recommender system is effective in diversifying the articles that users click. Our research is aimed at improving long-term satisfaction by recommending content that users can enjoy in the short term while simultaneously ensuring diversity.

Abstract The spread of misinformation transgresses international boundaries, between languages and cultures. This is especially evident in times of global crises such as the Covid-19 pandemic. This study observes misinformation on Twitter in the Japanese and English languages regarding false claims that the drug Ivermectin is an effective treatment for Covid-19. Our exploratory cross-lingual analysis identifies key themes of discussion and influential users in both languages, finding English misinformation to be highly popular amongst Japanese users. Significantly, an analysis of the timing of retweets between languages reveals that Japanese users find and widely share English misinformation often before English users themselves. This contradicts expectations that users from other languages tend to pick up on popular misinformation in English. Instead, they seek out English language sources irrespective of their popularity to support their agenda. These results emphasise the importance of cross-lingual mitigation strategies for organizations trying to combat misinformation, and that they must look beyond their own language spheres.

The musculoskeletal network is a complex system of different types of nodes and edges interacting with each other. Although there is a wealth of knowledge about the anatomical components of the human body and the connections between them, the interdependence of these components as a system remains largely unexplored. This study aims to understand the structure of musculoskeletal networks by using hypergraphs as a model of the musculoskeletal system with many-to-many connections. We used both pairwise and hypergraph-based embedding methods to learn the connectivity of muscles. Experiments demonstrated the superiority of the proposed hypergraph-based method over pairwise methods in distinguishing the specific roles of the muscles connecting different body parts.

Highlighting minorities and crime survivors through public discourse is essential for their support and protection. However, advocating for minorities is challenging due to the fear of potential isolation from one's social circles. This reluctance contributes to the societal phenomenon known as the "spiral of silence," significantly impeding efforts to support socially vulnerable individuals. This study centers on a pivotal instance where the silence surrounding sexual abuse in the Japanese entertainment industry was disrupted, in which the late company president had allegedly abused idol trainees of the company for decades. Utilizing extensive data from news media and social media, the study probes the engagement dynamics of public attention to this scandal. Results indicate that users on social media provided earlier and greater coverage for this scandal compared to news media outlets. Furthermore, television demonstrated a significant delay in addressing this issue compared to other news media, such as tabloids, magazines, and online news. Regarding social media engagement, idol fans exhibited a more subdued response to the issue compared to the general public. Notably, fans more loyal to the company tended to be slower to mention the issue, with a higher likelihood of standing in defense of the perpetrators. Moreover, conflicting attitudes were observed within the fan communities, culminating in an observable "echo chamber" phenomenon. This paper presents a novel examination of the process of disruption of social silence and offers critical insights for aiding vulnerable individuals in environments dominated by an unacknowledged spiral of silence. This study is novel in that it suggests a reinterpretation of the "spiral of silence theory" in the age of social media, through a comprehensive analysis of relevant social media data and news media data. This contributes to the body of research that has analyzed the spiral of silence theory online.

Abstract Deep convolutional generative adversarial networks (GAN) allow for creating images from existing databases. We applied a modified light-weight GAN (FastGAN) algorithm to cerebral blood flow SPECTs and aimed to evaluate whether this technology can generate created images close to real patients. Investigating three anatomical levels (cerebellum, CER; basal ganglia, BG; cortex, COR), 551 normal (248 CER, 174 BG, 129 COR) and 387 pathological brain SPECTs using N-isopropyl p-I-123-iodoamphetamine (123I-IMP) were included. For the latter scans, cerebral ischemic disease comprised 291 uni- (66 CER, 116 BG, 109 COR) and 96 bilateral defect patterns (44 BG, 52 COR). Our model was trained using a three-compartment anatomical input (dataset ‘A’; including CER, BG, and COR), while for dataset ‘B’, only one anatomical region (COR) was included. Quantitative analyses provided mean counts (MC) and left/right (LR) hemisphere ratios, which were then compared to quantification from real images. For MC, ‘B’ was significantly different for normal and bilateral defect patterns (P

Abstract In complex networks, the role of a node is based on the aggregation of structural features and functions. However, in real networks, it has been observed that a single node can have multiple roles. Here, the roles of a node can be defined in a case-by-case manner, depending on the graph data mining task. Consequently, a significant obstacle to achieving multiple-role discovery in real networks is finding the best way to select datasets for pre-labeling. To meet this challenge, this study proposes a flexible framework that extends a single-role discovery method by using domain adversarial learning to discover multiple roles for nodes. Furthermore, we propose a method to assign sub-networks, derived through community extraction methods, to a source network and a validation network as training datasets. Experiments to evaluate accuracy conducted on real networks demonstrate that the proposed method can achieve higher accuracy and more stable results.

Abstract To examine conservative–liberal differences in the extent to which partisan tweets reach less partisan moderate users in a nonwestern context, we analyzed a network of retweets about former Japanese Prime Minister Shinzo Abe. The analyses consistently demonstrated that partisan tweets originating from the conservative cluster reach a wider range of moderate users than those from the liberal cluster. Network analyses revealed that while the conservative and the liberal clusters’ internal structures were similar, the conservative cluster reciprocated the follows from moderate accounts at a higher rate than the liberal cluster. In addition, moderate accounts reciprocated the conservative cluster’s following at a higher rate than they did for the liberal cluster. The analysis of tweet content showed no difference in the frequency of hashtag use between conservatives and liberals, but there were differences in the use of emotion words and linguistic expressions. In particular, emotion words related to the propagation of messages, such as those expressing “dislike”, were used more frequently by conservatives, while the use of adjectives by conservatives was closer to that of moderate users, indicating that conservative tweets are more palatable for moderate users than liberal tweets.

Abstract Despite intensive studies on the evolution of cooperation in spatial public goods games, there have been few investigations into locality effects in interaction games, adaptation, and punishment. Here we analyze locality effects using an agent-based model of a regular graph. Our simulation shows that a situation containing a local game, local punishment, and global adaptation leads to the most robustly cooperative regime. Further, we show an interesting feature in local punishment. Previous studies showed that a local game and global adaptation are likely to generate cooperation. However, they did not consider punishment. We show that if local punishment is introduced in spatial public goods games, a situation satisfying either local game or local adaptation is likely to generate cooperation. We thus propose two principles. One is if interactions in games can be restricted locally, it is likely to generate cooperation independent of the interaction situations on punishment and adaptation. The other is if the games must be played globally, a cooperative regime requires both local punishment and local adaptation.

When a movement occurs on the Internet, it often affects the real world. For example, “buzz,” as a positive phenomenon, and “flames,” as a negative one. Therefore, it is important to understand social phenomena on the Internet for social marketing. However, large scale diffusion events on the net may not reflect real-world phenomena; that is, there may be segregation between online opinions and actual opinions. Therefore, data analysis is needed to determine whether internet phenomena affect the real world. This paper presents a detailed analysis of the phenomenon of bursts in the Twitter demo over the Public Prosecutor’s Office Act. We found that 50% of tweets and retweets are posted by only 2% of users; that is, the volume of the tweets is overstated. However, the Twitter demo was supported by a diverse community; that is, the Twitter demo was not only a noisy minority phenomenon. The analysis presented in this paper can also be applied to other burst phenomena such as flames and buzz, and may be useful to analyze the effect of social marketing.

Abstract Discovering the node roles in a network helps to solve diverse social problems. Role discovery attempts to predict the node roles from a network structure, and this method has been extensively studied in various fields. Role discovery using transfer learning has many advantages, but methods using this approach face two kinds of problems: domain-shift problems and model selection. To address these problems, we propose a general framework that includes network representation learning, domain adversarial learning for suppressing domain-shift problems, and model selection without using target labels. As a result of computational experiments, we show on publicly available datasets that the proposed model outperforms conventional methods, the proposed model selection method performs well without using target labels, and the proposed method can be used in real-world datasets. Furthermore, we found that our framework suppressed domain-shift problems, worked well even with differences between networks, and could handle imbalanced classes.

The Olympic Games are a typical media event and are seen as a festive occasion that monopolizes people's attention through the mass media. The Games and their media coverage have a predetermined schedule that enhances the nation's sense of unity by placing a temporary truce on political conflicts. Governments, especially those of Olympic host countries, tend to take advantage of this effect to garner support for their own policies. However, the effects of such media events may be weakening owing to changes in the media environment and increasing political polarization. Examining the 2020 Tokyo Olympic Games, this case study analyzes a large amount of Twitter data to probe Japanese social media users' attitudes toward the Olympic Games and the relationship of these attitudinal changes with their attitudes toward the political leadership of the prime minister. The results showed that previously negative attitudes toward the Olympic Games improved as people enjoyed the event. However, this positive shift did not appear to be associated with their attitudes toward the prime minister. Users' political predispositions strongly determined their attitudes toward the Olympic Games, indicating that the Olympic Games as a media event had limited implications for support for the administration.

People are obtaining more and more information from social media and other online sources, but the spread of misinformation can lead to social disruption. In particular, social networking services (SNSs) can easily spread information of uncertain authenticity and factuality. Although many studies have proposed methods that addressed how to suppress the spread of misinformation on SNSs, few works have examined the impact on society of diffusing both misinformation and its corrective information. This study models the effects of effort to reduce misinformation and the diffusion of corrective information on social disruption, and it clarifies these effects. With the aim of reducing the impact on social disruption, we show that not only misinformation but also corrective information can cause social disruption, and we clarify how to control the spread of the latter to limit its impact. We analyzed the misinformation about a toilet-paper shortage and its correction as well as the social disruption this event caused in Japan during the COVID-19 pandemic in 2020. First, (1) we analyzed the extent to which misinformation and its corrections spread on SNS, and then (2) we created a model to estimate the impact of misinformation and its corrections on the world. Finally, (3) We used our model to analyze the change in this impact when the diffusion of the misinformation and its corrections changed. Based on our analysis results in (1), the corrective information spread much more widely than the misinformation. From the model developed in (2), the corrective information caused excessive purchasing behavior. The analysis results in (3) show that the amount of corrective information required to minimize the societal impact depends on the amount of misinformation diffusion. Most previous studies concentrated on the impact of corrective information on attitudes toward misinformation. On the other hand, the most significant contribution of this study is that it focuses on the impact of corrective information on society and clarifies the appropriate amount of it.

Inbound tourism plays an important role in local economies. To stimulate local economies, it is necessary to attract foreign tourists to various areas of a country. This research aims to develop a method of extracting the locations of tourist destinations in a country and to understand what characteristics foreign tourists expect of areas near tourist attractions compared with what domestic tourists expect. In this paper, a tourist destination is defined as a small area that has places of interests for tourists such as historic sites, theme parks, hotels, and restaurants. The methods proposed in this paper are applied to data acquired from Twitter and Foursquare in Japan. The proposed method successfully extracts the locations of tourist destinations and characterizes those locations based on the points of interest in the neighborhood. The results indicate that foreign tourists who come to Japan expect nightlife spots (bars, nightclubs, etc.) to be located in the neighborhood of tourist destinations, in contrast to the expectations of domestic tourists. The proposed methods are applicable to not only Japan, but to any country.

Although positive incentives for cooperators and/or negative incentives for free-riders in social dilemmas play an important role in maintaining cooperation, there is still the outstanding issue of who should pay the cost of incentives. The second-order free-rider problem, in which players who do not provide the incentives dominate in a game, is a well-known academic challenge. In order to meet this challenge, we devise and analyze a meta-incentive game that integrates positive incentives (rewards) and negative incentives (punishments) with second-order incentives, which are incentives for other players' incentives. The critical assumption of our model is that players who tend to provide incentives to other players for their cooperative or non-cooperative behavior also tend to provide incentives to their incentive behaviors. In this paper, we solve the replicator dynamics for a simple version of the game and analytically categorize the game types into four groups. We find that the second-order free-rider problem is completely resolved without any third-order or higher (meta) incentive under the assumption. To do so, a second-order costly incentive, which is given individually (peer-to-peer) after playing donation games, is needed. The paper concludes that (1) second-order incentives for first-order reward are necessary for cooperative regimes, (2) a system without first-order rewards cannot maintain a cooperative regime, (3) a system with first-order rewards and no incentives for rewards is the worst because it never reaches cooperation, and (4) a system with rewards for incentives is more likely to be a cooperative regime than a system with punishments for incentives when the cost-effect ratio of incentives is sufficiently large. This solution is general and strong in the sense that the game does not need any centralized institution or proactive system for incentives.