Discord has evolved from a gaming-focused communication tool into a versatile platform supporting diverse online communities. Despite its large user base and active public servers, academic research on Discord remains limited due to data accessibility challenges. This paper introduces Discord Unveiled: A Comprehensive Dataset of Public Communication (2015-2024), the most extensive Discord public server's data to date. The dataset comprises over 2.05 billion messages from 4.74 million users across 3,167 public servers, representing approximately 10% of servers listed in Discord's Discovery feature. Spanning from Discord's launch in 2015 to the end of 2024, it offers a robust temporal and thematic framework for analyzing decentralized moderation, community governance, information dissemination, and social dynamics. Data was collected through Discord's public API, adhering to ethical guidelines and privacy standards via anonymization techniques. Organized into structured JSON files, the dataset facilitates seamless integration with computational social science methodologies. Preliminary analyses reveal significant trends in user engagement, bot utilization, and linguistic diversity, with English predominating alongside substantial representations of Spanish, French, and Portuguese. Additionally, prevalent community themes such as social, art, music, and memes highlight Discord's expansion beyond its gaming origins.
Rohith Vaidyanathan, Srinath Srinivasa, Praseeda
et al.
Digital public infrastructures (DPIs) represent networks of open technology standards, applications, services, and digital assets made available for the public good. One of the key challenges in DPI design is to resolve complex issues of consent, scaled over large populations. While the primary objective of consent management is to empower the data owner, ownership itself can come with variegated morphological forms with different implications over consent. Questions of ownership in a public space also have several nuances where individual autonomy needs to be balanced with public well-being and national sovereignty. This requires consent management to be compliant with applicable regulations for data sharing. This paper addresses the question of representing modes of ownership of digital assets and their corresponding implications for consensual data flows in a DPI. It proposes a set of foundational abstractions to represent them. Our proposed architecture responds to the growing need for transparent, secure, and user-centric consent management within Digital Public Infrastructure (DPI). Incorporating a formalised data ownership model enables end-to-end traceability of consent, fine-grained control over data sharing, and alignment with evolving legal and regulatory frameworks.
Using panel data from the Household, Income and Labour Dynamics in Australia survey, this paper analyses the wage gap between the public and private sectors in Australia from 2001 to 2022. The analysis is conducted at both the national and state levels. We found that, since 2014, the public-sector wage premium (nationally) has increased for women but decreased for men, with women's outcomes driving current trends. Additionally, the public-sector wage premium varies significantly across states, indicating that state-level wage-setting forces are more influential than national ones. Our trend analysis reveals that the premium is neither consistently procyclical nor countercyclical. Furthermore, quantile analysis shows that the premium fluctuates across the wage distribution, though not in a uniform pattern over time.
Effective communication during health crises is critical, with social media serving as a key platform for public health experts (PHEs) to engage with the public. However, it also amplifies pseudo-experts promoting contrarian views. Despite its importance, the role of emotional and moral language in PHEs' communication during COVID-19 remains under explored. This study examines how PHEs and pseudo-experts communicated on Twitter during the pandemic, focusing on emotional and moral language and their engagement with political elites. Analyzing tweets from 489 PHEs and 356 pseudo-experts from January 2020 to January 2021, alongside public responses, we identified key priorities and differences in messaging strategy. PHEs prioritize masking, healthcare, education, and vaccines, using positive emotional language like optimism. In contrast, pseudo-experts discuss therapeutics and lockdowns more frequently, employing negative emotions like pessimism and disgust. Negative emotional and moral language tends to drive engagement, but positive language from PHEs fosters positivity in public responses. PHEs exhibit liberal partisanship, expressing more positivity towards liberals and negativity towards conservative elites, while pseudo-experts show conservative partisanship. These findings shed light on the polarization of COVID-19 discourse and underscore the importance of strategic use of emotional and moral language by experts to mitigate polarization and enhance public trust.
Pratiksha Thaker, Amrith Setlur, Zhiwei Steven Wu
et al.
Public pretraining is a promising approach to improve differentially private model training. However, recent work has noted that many positive research results studying this paradigm only consider in-distribution tasks, and may not apply to settings where there is distribution shift between the pretraining and finetuning data -- a scenario that is likely when finetuning private tasks due to the sensitive nature of the data. In this work, we show empirically across three tasks that even in settings with large distribution shift, where both zero-shot performance from public data and training from scratch with private data give unusably weak results, public features can in fact improve private training accuracy by up to 67\% over private training from scratch. We provide a theoretical explanation for this phenomenon, showing that if the public and private data share a low-dimensional representation, public representations can improve the sample complexity of private training even if it is impossible to learn the private task from the public data alone. Altogether, our results provide evidence that public data can indeed make private training practical in realistic settings of extreme distribution shift.
Randy Kuang, Maria Perepechaenko, Mahmoud Sayed
et al.
In their 2022 study, Kuang et al. introduced Multivariable Polynomial Public Key (MPPK) cryptography, leveraging the inversion relationship between multiplication and division for quantum-safe public key systems. They extended MPPK into Homomorphic Polynomial Public Key (HPPK), employing homomorphic encryption for large hidden ring operations. Originally designed for key encapsulation (KEM), HPPK's security relies on homomorphic encryption of public polynomials. This paper expands HPPK KEM to a digital signature scheme, facing challenges due to the distinct nature of verification compared to decryption. To adapt HPPK KEM to digital signatures, the authors introduce an extension of the Barrett reduction algorithm, transforming modular multiplications into divisions in the verification equation over a prime field. The extended algorithm non-linearly embeds the signature into public polynomial coefficients, addressing vulnerabilities in earlier MPPK DS schemes. Security analysis demonstrates exponential complexity for private key recovery and forged signature attacks, considering ring bit length twice that of the prime field size.
Low fertility rates and an aging society, growing long-term care needs, and workforce shortages in professional, industrial, and care sectors are emerging issues in South Korea and Taiwan. Both governments have pursued economic/industrial growth as productive welfare capitalism and enacted preferred selective migration policies to recruit white-collar migrant workers (MWs) as mobile elites, but they have also adopted regulations and limitations on blue-collar MWs through unfree labor relations, precarious employment, and temporary legal status to provide supplemental labor. In order to demonstrate how multiple policy regulations from a national level affect MWs’ precarity of labor in their receiving countries, which in turn affect MWs’ im/mobilities, this article presents the growing trends of transnational MWs, regardless of them being high- or low-skilled MWs, and it evaluates four dimensions of labor migration policies—MWs’ working and employment conditions, social protection, union rights and political participation, and access to permanent residency in both countries. We found that the rights and working conditions of low-skilled MWs in Korea and Taiwan are improving slowly, but still lag behind those of high-skilled MWs which also affects their public health and well-being. The significant difference identified here is that MWs in Taiwan can organize labor unions, which is strictly prohibited in Korea; pension protection also differs between the nations. Additionally, an application for permanent residency is easier for high-skilled migrant workers compared with low-skilled MWs and both the Korean and Taiwanese immigration policies differentiate the entry and resident status for low-skilled and professional MWs from dissimilar class backgrounds. Policy recommendations for both countries are also discussed.
The sports equipment business is one of the businesses that is currently starting to develop. Supported by the desire of the community to engage in sports, one of which is archery, Vieneth Archery Official, one of the archery equipment shops, takes advantage of this situation. The purpose of this study was to digitally determine the marketing communication of archery sports equipment on Instagram @vienetharcheryofficial. The method used in this research is Krippendorff content analysis which is carried out by collecting data on the Instagram account @vienetharcheryofficial from 1 May 2022 to 30 May 2022. The conclusion of this study shows that Vieneth Archery, as an archery equipment shop, carries out digital marketing communications through @vienetharcheryofficial Instagram posts dominated by public relations and publicity efforts.
In recent years, there have been many studies on quantum computing and the construction of quantum computers which are capable of breaking conventional number theory-based public key cryptosystems. Therefore, in the not-too-distant future, we need the public key cryptosystems that withstand against the attacks executed by quantum computers, so-called post-quantum cryptosystems. A public key cryptosystem based on polar codes (PKC-PC) has recently been introduced whose security depends on the difficulty of solving the general decoding problem of polar code. In this paper, we first implement the encryption, key generation and decryption algorithms of PKC-PC on Raspberry Pi3. Then, to evaluate its performance, we have measured several related parameters such as execution time, energy consumption, memory consumption and CPU utilization. All these metrics are investigated for encryption/decryption algorithms of PKC-PC with various parameters of polar codes. In the next step, the investigated parameters are compared to the implemented McEliece public key cryptosystem. Analyses of such results show that the execution time of encryption/decryption as well as the energy and memory consumption of PKC-PC is shorter than the McEliece cryptosystem.
Urban metro and tram networks are regularly subject to planned disruptions, including closures, resulting from the need to maintain and renew infrastructure. In this study, we first empirically analyse the passenger demand response to planned public transport disruptions based on individual passenger travel behaviour, based on which we infer generalised journey time and cost elasticities for different passenger groups and time periods of the day. Second, we develop a model which enables predicting public transport demand for individual origin-destination pairs affected by a closure. The model is trained based on the empirically observed travel behaviour. The proposed method is applied to a case study closure in Amsterdam, the Netherlands, based on which we empirically derive generalised journey time and generalised journey cost elasticities. Our results suggest that passengers demand response is lower for frequent users of the public transport network, as well as during weekdays, especially during the peak periods. Arguably, this stems from a higher share of captive passengers with a mandatory journey purpose in these segments, who will continue making their journey nevertheless. During weekends, with typically higher shares of leisure related journeys, a much more pronounced demand response is found. The estimated neural network regression model is able to predict passenger demand during public transport closures with a high level of accuracy. This provides public transport agencies more precise insights into the impact of closures on their revenue losses and on the potential need for resources reallocation.
This article analyzes how engagement in legitimation politics in Australia and New Zealand has enabled unions to influence the industrial relations policy process. It demonstrates how enhanced moral legitimacy with the wider public positively impacts unions’ pragmatic legitimacy with governing political parties. Drawing on Grant’s insider–outsider typology, we show how enhanced legitimacy can increase unions’ power resources as insider groups with center‐left and, to a lesser extent, center‐right governing parties, which can enable greater influence over industrial relations policy.
Within the context of an accelerating climate emergency, the introduction frames the strategies and actions adopted by labour and unions to reduce carbon emissions that are presented in the articles contributing to this special issue. Industrial relations scholarship, which has been slow to address the climate emergency, has focussed on the jobs versus environment dilemma, the role of unions, technical innovation versus social unionism, and just transition approaches. While labour and union approaches in different sectors across Europe are largely confined to variants of ecological modernization, a more proactive transformative strategy opening up an alternative eco-socialist vision for the future is emerging. The issue highlights the contradictions in union strategies, the drivers of change and the way forward in pursuance of a green economy through a focus on the roles of government and the public sector, the organization of labour and the labour process, and education and training.
This paper studies how to implement a privacy friendly form of ticketing for public transport in practice. The protocols described are inspired by current (privacy invasive) public transport ticketing systems used around the world. The first protocol emulates paper based tickets. The second protocol implements a pay-as-you-go approach, with fares determined when users check-in and check-out. Both protocols assume the use of a smart phone as the main user device to store tickets or travel credit. We see this research as a step towards investigating how to design commonly used infrastructure in a privacy friendly manner in practice, paying particular attention to how to deal with failures.
With the increasing trend in the topic of migration in Europe, the public is now more engaged in expressing their opinions through various platforms such as Twitter. Understanding the online discourses is therefore essential to capture the public opinion. The goal of this study is the analysis of social media platform to quantify public attitudes towards migrations and the identification of different factors causing these attitudes. The tweets spanning from 2013 to Jul-2021 in the European countries which are hosts to immigrants are collected, pre-processed, and filtered using advanced topic modeling technique. BERT-based entity linking and sentiment analysis, and attention-based hate speech detection are performed to annotate the curated tweets. Moreover, the external databases are used to identify the potential social and economic factors causing negative attitudes of the people about migration. To further promote research in the interdisciplinary fields of social science and computer science, the outcomes are integrated into a Knowledge Base (KB), i.e., MigrationsKB which significantly extends the existing models to take into account the public attitudes towards migrations and the economic indicators. This KB is made public using FAIR principles, which can be queried through SPARQL endpoint. Data dumps are made available on Zenodo.
Public transportation plays a critical role in people's daily life. It has been proven that public transportation is more environmentally sustainable, efficient, and economical than any other forms of travel. However, due to the increasing expansion of transportation networks and more complex travel situations, people are having difficulties in efficiently finding the most preferred route from one place to another through public transportation systems. To this end, in this paper, we present Polestar, a data-driven engine for intelligent and efficient public transportation routing. Specifically, we first propose a novel Public Transportation Graph (PTG) to model public transportation system in terms of various travel costs, such as time or distance. Then, we introduce a general route search algorithm coupled with an efficient station binding method for efficient route candidate generation. After that, we propose a two-pass route candidate ranking module to capture user preferences under dynamic travel situations. Finally, experiments on two real-world data sets demonstrate the advantages of Polestar in terms of both efficiency and effectiveness. Indeed, in early 2019, Polestar has been deployed on Baidu Maps, one of the world's largest map services. To date, Polestar is servicing over 330 cities, answers over a hundred millions of queries each day, and achieves substantial improvement of user click ratio.
Many powerful computing technologies rely on implicit and explicit data contributions from the public. This dependency suggests a potential source of leverage for the public in its relationship with technology companies: by reducing, stopping, redirecting, or otherwise manipulating data contributions, the public can reduce the effectiveness of many lucrative technologies. In this paper, we synthesize emerging research that seeks to better understand and help people action this \textit{data leverage}. Drawing on prior work in areas including machine learning, human-computer interaction, and fairness and accountability in computing, we present a framework for understanding data leverage that highlights new opportunities to change technology company behavior related to privacy, economic inequality, content moderation and other areas of societal concern. Our framework also points towards ways that policymakers can bolster data leverage as a means of changing the balance of power between the public and tech companies.
Part I: Introduction to marketing. Marketing defined. Differences between marketing products and hospitality/travel services. The hospitality and travel marketing system. Part II: Planning: research and analysis. Customer behavior in the hospitality and travel industry. Analyzing market opportunities. Marketing research. Part III: Planning: strategy, positioning and objectives. Marketing segmentation and trends. The marketing plan and the eight "P's" of hospitality marketing. Part IV: Implementing the marketing mix. The product/service mix and people. Packaging and promotion. The distribution mix and the travel trade. Communications and the promotional mix. Advertising. Sales promotion and merchandising. Personal selling and sales management. Public relations and publicity. Pricing. Part V: Controlling, measuring and evaluating the plan. Marketing management, evaluation and control.
Traditionally, publicly available repositories of certificates offer the usual response to the problem of public key distribution. After issuing a public-key certificate a certification authority (CA) - in the frame of a particular public-key infrastructure (PKI) - will store and publish that certificate in a repository so that, at a later moment, end-users can search, find and retrieve public-key certificates. A known and still persisting drawback of this approach is that these repositories are not interconnected between each other on an Internet scale, therefore the search and retrieving of certificates on a wider scale turns out to be very difficult. In this scenario, end-users are supposed to know the Internet location of the repository before actually starting the procedure of search and retrieval. Currently, there are no means to perform automatic discovery of authoritative repositories for a particular certificate using as a search-key some information identifying an Internet entity. In this paper, we try to describe a different approach for solving the key distribution problem. This solution takes into account an already existing Internet-wide infrastructure: the domain name system (DNS).
Sebastián Pinto, Federico Albanese, Claudio O. Dorso
et al.
The mass media plays a fundamental role in the formation of public opinion, either by defining the topics of discussion or by making an emphasis on certain issues. Directly or indirectly, people get informed by consuming news from the media. Naturally, two questions appear: What are the dynamics of the agenda and how the people become interested in their different topics? These questions cannot be answered without proper quantitative measures of agenda dynamics and public attention. In this work we study the agenda of newspapers in comparison with public interests by performing topic detection over the news. We define Media Agenda as the distribution of topic's coverage by the newspapers and Public Agenda as the distribution of public interest in the same topic space. We measure agenda diversity as a function of time using the Shannon entropy and differences between agendas using the Jensen-Shannon distance. We found that the Public Agenda is less diverse than the Media Agenda, especially when there is a very attractive topic and the audience naturally focuses only on this one. Using the same methodology we detect coverage bias in newspapers. Finally, it was possible to identify a complex agenda-setting dynamics within a given topic where the least sold newspaper triggered a public debate via a positive feedback mechanism with social networks discussions which install the issue in the Media Agenda.