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Wang Ngai Yeung

Publications and source records attributed to Wang Ngai Yeung.

6 recordsLinked to original sources

Popularity Trajectories and Mixing Patterns of Urban Venues in Two European Cities

Urban venues such as restaurants, shops, bars and parks host the daily life of a city, and their collective rise and fall signals how vibrant a neighbourhood is. The usual assumption is that a venue's fortunes follow its neighbourhood's, so that how wealthy an area is and how many people live there will decide a venue's performance. Yet whether there is a common performance trajectory shared by venues over time is underexplored. In this study, we use 5.5 years of Foursquare data for about 10^4 venues in London and Barcelona. We found six distinguishable shapes recur in both cities, that are (1) Fall, (2) Rise, (3) Rise & Fall, (4) Steady, (5) Ephemeral (an early hit that loses popularity fast) and (6) Underdogs (a slow starter that gains popularity late). However, no recovery shape appears, that is neither city yields a U-shaped fall-and-rise group. Additionally, we found that deprivation and population density are not indicative which shape a venue follows. We also discovered that how early a shape can be recognized depends on when its distinctive segment occurs, not a shape's complexity. Proximity between venues, however, does predict how well-performing a venue is. We uncovered a mixing pattern where venues of the same shape tend to locate together more often than chance within roughly 200 to 600 metres. Finally, our qualitative reading of two contrasting districts, one rising in London and one falling in Barcelona, show that comparable socioeconomic conditions but divergent gentrification histories can generate vastly different popularity trajectories. All in all, our results altogether point at that urban vibrancy emerges at the block- and street- level, and social and historical contexts matter in terms of the characteristics of such patterns.

physics.soc-ph↗

Needles in a haystack: using forensic network science to uncover insider trading

Although the automation and digitisation of anti-financial crime investigation has made significant progress in recent years, detecting insider trading remains a unique challenge, partly due to the limited availability of labelled data. To address this challenge, we propose using a data-driven networks approach that flags groups of corporate insiders who report coordinated transactions that are indicative of insider trading. Specifically, we leverage data on 2.9 million trades reported to the U.S. Securities and Exchange Commission (SEC) by company insiders (C-suite executives, board members and major shareholders) between 2014 and 2024. Our proposed algorithm constructs weighted edges between insiders based on the temporal similarity of their trades over the 10-year timeframe. Within this network we then uncover trends that indicate insider trading by focusing on central nodes and anomalous subgraphs. To highlight the validity of our approach we evaluate our findings with reference to two null models, generated by running our algorithm on synthetic empirically calibrated and shuffled datasets. The results indicate that our approach can be used to detect pairs or clusters of insiders whose behaviour suggests insider trading and/or market manipulation.

cs.SI↗

On Controlled Change: Generative AI's Impact on Professional Authority in Journalism

Using (generative) artificial intelligence tools and systems in journalism is expected to increase journalists' production rates, transform newsrooms' economic models, and further personalize the audience's news consumption practices. Since its release in 2022, OpenAI's ChatGPT and other large language models have raised the alarms inside news organizations, not only for bringing new challenges to news reporting and fact-checking but also for what these technologies would mean for journalists' professional authority in journalism. This paper examines how journalists in Dutch media manage the integration of AI technologies into their daily routines. Drawing from 13 interviews with editors, journalists, and innovation managers in different news outlets and media companies, we propose the concept of controlled change. as a heuristic to explain how journalists are proactively setting guidelines, experimenting with AI tools, and identifying their limitations and capabilities. Using professional authority as a theoretical framework, we argue that journalists anticipate and integrate AI technologies in a supervised manner and identify three primary mechanisms through which journalists manage this integration: (1) developing adaptive guidelines that align AI use with ethical codes, (2) experimenting with AI technologies to determine their necessity and fit, and (3) critically assessing the capabilities and limitations of AI systems.

cs.CY↗

Garbage in Garbage out: Impacts of data quality on criminal network intervention

Criminal networks such as human trafficking rings are threats to the rule of law, democracy and public safety in our global society. Network science provides invaluable tools to identify key players and design interventions for Law Enforcement Agencies (LEAs), e.g., to dismantle their organisation. However, poor data quality and the adaptiveness of criminal networks through self-organization make effective disruption extremely challenging. Although there exists a large body of work building and applying network scientific tools to attack criminal networks, these work often implicitly assume that the network measurements are accurate and complete. Moreover, there is thus far no comprehensive understanding of the impacts of data quality on the downstream effectiveness of interventions. This work investigates the relationship between data quality and intervention effectiveness based on classical graph theoretic and machine learning-based approaches. Decentralization emerges as a major factor in network robustness, particularly under conditions of incomplete data, which renders attack strategies largely ineffective. Moreover, the robustness of centralized networks can be boosted using simple heuristics, making targeted attack more infeasible. Consequently, we advocate for a more cautious application of network science in disrupting criminal networks, the continuous development of an interoperable intelligence ecosystem, and the creation of novel network inference techniques to address data quality challenges.

physics.soc-ph↗

The Impact of Knowledge Silos on Responsible AI Practices in Journalism

The effective adoption of responsible AI practices in journalism requires a concerted effort to bridge different perspectives, including technological, editorial, journalistic, and managerial. Among the many challenges that could impact information sharing around responsible AI inside news organizations are knowledge silos, where information is isolated within one part of the organization and not easily shared with others. This study aims to explore if, and if so, how, knowledge silos affect the adoption of responsible AI practices in journalism through a cross-case study of four major Dutch media outlets. We examine the individual and organizational barriers to AI knowledge sharing and the extent to which knowledge silos could impede the operationalization of responsible AI initiatives inside newsrooms. To address this question, we conducted 14 semi-structured interviews with editors, managers, and journalists at de Telegraaf, de Volkskrant, the Nederlandse Omroep Stichting (NOS), and RTL Nederland. The interviews aimed to uncover insights into the existence of knowledge silos, their effects on responsible AI practice adoption, and the organizational practices influencing these dynamics. Our results emphasize the importance of creating better structures for sharing information on AI across all layers of news organizations.

cs.CY↗

Automated Journalism

Developed as a response to the increasing popularity of data-driven journalism, automated journalism refers to the process of automating the collection, production, and distribution of news content and other data with the assistance of computer programs. Although the algorithmic technologies associated with automated journalism remain in the initial stage of development, early adopters have already praised the usefulness of automated journalism for generating routine news based on clean, structured data. Most noticeably, the Associated Press and The New York Times have been automating news content to cover financial and sports issues for over a decade. Nevertheless, research on automated journalism is also alerting to the dangers of using algorithms for news creation and distribution, including the possible bias behind AI systems or the human bias of those who develop computer programs. The popularization of automated news content also has important implications for the infrastructure of the newsroom, the role performance of journalists and other non-journalistic professionals, and the distribution of news content to a datafied audience.

cs.CY↗