No Arabic abstract
Shared e-scooters have become a familiar sight in many cities around the world. Yet the role they play in the mobility space is still poorly understood. This paper presents a study of the use of Bird e-scooters in the city of Atlanta. Starting with raw data which contains the location of available Birds over time, the study identifies trips and leverages the Google Places API to associate each trip origin and destination with a Point of Interest (POI). The resulting trip data is then used to understand the role of e-scooters in mobility by clustering trips using 10 collections of POIs, including business, food and recreation, parking, transit, health, and residential. The trips between these POI clusters reveal some surprising, albeit sensible, findings about the role of e-scooters in mobility, as well as the time of the day where they are most popular.
Web 3.0 promises to have a significant effect in users and businesses. It will change how people work and play, how companies use information to market and sell their products, as well as operate their businesses. The basic shift occurring in Web 3.0 is from information-centric to knowledge-centric patterns of computing. Web 3.0 will enable people and machines to connect, evolve, share and use knowledge on an unprecedented scale and in new ways that make our experience of the Internet better. Additionally, semantic technologies have the potential to drive significant improvements in capabilities and life cycle economics through cost reductions, improved efficiencies, enhanced effectiveness, and new functionalities that were not possible or economically feasible before. In this paper we look to the semantic web and Web 3.0 technologies as enablers for the creation of value and appearance of new business models. For that, we analyze the role and impact of Web 3.0 in business and we identify nine potential business models, based in direct and undirected revenue sources, which have emerged with the appearance of semantic web technologies.
The micromobility is shaping first- and last-mile travels in urban areas. Recently, shared dockless electric scooters (e-scooters) have emerged as a daily alternative to driving for short-distance commuters in large cities due to the affordability, easy accessibility via an app, and zero emissions. Meanwhile, e-scooters come with challenges in city management, such as traffic rules, public safety, parking regulations, and liability issues. In this paper, we collected and investigated 5.8 million scooter-tagged tweets and 144,197 images, generated by 2.7 million users from October 2018 to March 2020, to take a closer look at shared e-scooters via crowdsourcing data analytics. We profiled e-scooter usages from spatial-temporal perspectives, explored different business roles (i.e., riders, gig workers, and ridesharing companies), examined operation patterns (e.g., injury types, and parking behaviors), and conducted sentiment analysis. To our best knowledge, this paper is the first large-scale systematic study on shared e-scooters using big social data.
Recently, messaging applications, such as WhatsApp, have been reportedly abused by misinformation campaigns, especially in Brazil and India. A notable form of abuse in WhatsApp relies on several manipulated images and memes containing all kinds of fake stories. In this work, we performed an extensive data collection from a large set of WhatsApp publicly accessible groups and fact-checking agency websites. This paper opens a novel dataset to the research community containing fact-checked fake images shared through WhatsApp for two distinct scenarios known for the spread of fake news on the platform: the 2018 Brazilian elections and the 2019 Indian elections.
Algorithms that favor popular items are used to help us select among many choices, from engaging articles on a social media news feed to songs and books that others have purchased, and from top-raked search engine results to highly-cited scientific papers. The goal of these algorithms is to identify high-quality items such as reliable news, beautiful movies, prestigious information sources, and important discoveries --- in short, high-quality content should rank at the top. Prior work has shown that choosing what is popular may amplify random fluctuations and ultimately lead to sub-optimal rankings. Nonetheless, it is often assumed that recommending what is popular will help high-quality content bubble up in practice. Here we identify the conditions in which popularity may be a viable proxy for quality content by studying a simple model of cultural market endowed with an intrinsic notion of quality. A parameter representing the cognitive cost of exploration controls the critical trade-off between quality and popularity. We find a regime of intermediate exploration cost where an optimal balance exists, such that choosing what is popular actually promotes high-quality items to the top. Outside of these limits, however, popularity bias is more likely to hinder quality. These findings clarify the effects of algorithmic popularity bias on quality outcomes, and may inform the design of more principled mechanisms for techno-social cultural markets.
This study presents longitudinal evidence on the dissension of Management and Business Research (MBR) in Latin America and the Caribbean (LAC). It looks after intellectual bridges linking clusters among such dissension. It was implemented a coword network analysis to a sample of 12,000+ articles published by authors from LAC during 1998-2017. Structural network scores showed an increasing number of keywords and mean degree but decreasing modularity and density. The intellectual bridges were those of the cluster formed by disciplines/fields that tend toward consensus (e.g., mathematical models) and not by core MBR subjects (e.g., strategic planning).