Measuring International Online Human Values with Word Embeddings

As the Internet grows in number of users and in the diversity of services, it becomes more influential on peoples lives. It has the potential of constructing or modifying the opinion, the mental perception, and the values of individuals. What is being created and published online is a reflection of people’s values and beliefs. As a global platform, the Internet is a great source of information for researching the online culture of many different countries. In this work we develop a methodology for measuring data from textual online sources using word embedding models, to create a country-based online human values index that captures cultural traits and values worldwide. Our methodology is applied with a dataset of 1.7 billion tweets, and then we identify their location among 59 countries. We create a list of 22 Online Values Inquiries (OVI) , each one capturing different questions from the World Values Survey, related to several values such as religion, science, and abortion. We observe that our methodology is indeed capable of capturing human values online for different counties and different topics. We also show that some online values are highly correlated (up to c = 0.69, p < 0.05) with the corresponding offline values, especially religion-related ones. Our method is generic, and we believe it is useful for social sciences specialists, such as demographers and sociologists, that can use their domain knowledge and expertise to create their own Online Values Inquiries, allowing them to analyze human values in the online environment.

Platform Stress in Hospitality – Focusing on the Technology dimension

For the emergence of platform business, it is important to manage the stress that hosts receive from the business. Considering the characteristics of the platform business, stressors arise from social and technology dimension. In the global platform business (e.g., Airbnb), social dimension stressors mostly arise from the relationships among stakeholders, and technology dimension stressors arise from the system they utilize. This research aims to define this combination of social and technology stress as “Platform Stress.” Especially focusing on the technology dimension, this research empirically verified the relations of techno-stressors, burnout, and switching intention. Further, mediating effects of burnout in between the relationships demonstrates the importance of investigating the hosts’ stress. Exploring the platform stress from the technology-usage perspective, this research provides theoretical and managerial implications.

Middleware Patterns for Cloud Platforms

This chapter explores how traditional system architectures are being affected by the emergence of ‘Uber’ style platform models that provide business services with huge global reach. The specific demands and characteristics of such platforms are discussed which in turn dictate their technical requirements. The chapter will explain how middleware technologies have evolved to support today’s requirements for such massively scalable platform solutions. The latest preferred architectural paradigms dictate the use of micro-services and APIs are central to the design of such platforms. Similarly, event based architectures are another key paradigm that must be supported. The role of modern middleware and cloud technologies to support these newly dominant paradigms will be explained. Key architectural patterns pertinent to global platform solutions are illustrated. The role of modern middleware in fulfilling these patterns is highlighted using real-world examples from the field of open finance.

After a Global Platform Leaves: Understanding the Heterogeneity of Gig Workers through Capital Mobility

We know a great deal about global capital mobility in traditional industries, such as manufacturing, but very little about emerging capital mobility in the gig economy. Using the case of Canadian Foodora, a multinational platform that left Canada in 2020, I situate global capital mobility in the local labour market. Drawing upon interview data with former Foodora couriers and ethnographic data collected from a gig workers’ union, I investigate the social, economic and political subjectivities of gig workers activated by a global platform’s capital mobility. My findings reveal unexpected parallel effects caused by capital mobility in the gig economy and traditional industries. My research highlights how heterogeneity is salient for understanding divergent worker subjectivities. The economic and social impacts upon financially dependent gig workers and the emotional connections of devoted and organized gig workers challenge the dominant discourse that gig workers are simply part-timers and hence free from work commitments.

Importance of Security in Big Data Log Files on Cloud

Today cloud computing is a very popular technology, and many people use this technology in many ways. it's important to have it safe. This technology was primarily used to keep data safer and safer in the cloud, so in this article we suggest a security framework for large data logs in the cloud. There are many and many risks that threaten the integrity of this information in the great information. Therefore, in line with the development of technology, the level of security has also increased significantly over the years. Various technology techniques access several online activities, such as interaction with different internet sites and services, making the web more accessible to their plug-ins. As a result, these activities have created a global platform for malicious activities to add these devices that expose large data logs harmful attacks. Sky system is an online platform that requires proper security integration. In addition, the current state of online security threatens high data in the cloud, which has affected the performance and service model.

Boundary Control as Gatekeeping in Facebook Groups

Facebook groups host user-created communities on Facebook’s global platform, and their administrative structure consists of members, volunteer moderators, and governance mechanisms developed by the platform itself. This study presents the viewpoints of volunteers who moderate groups on Facebook that are dedicated to political discussion. It sheds light on how they enact their day-to-day moderation work, from platform administration to group membership, while acknowledging the demands that come from both these tasks. As volunteer moderators make key decisions about content, their work significantly shapes public discussion in their groups. Using data obtained from 15 face-to-face interviews, this qualitative study sheds light on volunteer moderation as a means of media control in complex digital networks. The findings show that moderation concerns not just the removal of content or contacts but, most importantly, it is about protecting group norms by controlling who has the access to the group. Facebook’s volunteer moderators have power not only to guide discussion but, above all, to decide who can participate in it, which makes them important gatekeepers of the digital public sphere.

Provocation: Why I want to talk television with global platform representatives

Interviews with industry workers and decision-makers are a critical method in television studies. Yet, one group of informants proves particularly hard to access – representatives from global media platforms. Why is it so hard to get interviews with global platform representatives, and what does the lack of access do to our research and scholarly debate?

Towards scalable online machine learning collaborations with OpenML

Is massively collaborative machine learning possible? Can we share and organize our collective knowledge of machine learning to solve ever more challenging problems? In a way, yes: as a community, we are already very successful at developing high-quality open-source machine learning libraries, thanks to frictionless collaboration platforms for software development. However, code is only one aspect. The answer is much less clear when we also consider the data that goes into these algorithms and the exact models that are produced. A tremendous amount of work and experience goes into the collection, cleaning, and preprocessing of data and the design, evaluation, and finetuning of models, yet very little of this is shared and organized in a way so that others can easily build on it. Suppose one had a global platform for sharing machine learning datasets, models, and reproducible experiments in a frictionless way so that anybody could chip in at any time to share a good model, add or improve data, or suggest an idea. OpenML is an open-source initiative to create such a platform. It allows anyone to share datasets, machine learning pipelines, and full experiments, organizes all of it online with rich metadata, and enables anyone to reuse and build on them in novel and unexpected ways. All data is open and accessible through APIs, and it is readily integrated into popular machine learning tools to allow easy sharing of models and experiments. This openness also allows a budding ecosystem of automated processes to scale up machine learning further, such as discovering similar datasets, creating systematic benchmarks, or learning from all collected results how to build the best machine learning models and even automatically doing so for any new dataset. We welcome all of you to become a part of it.