arXiv subjects

Julia Koltai

Publications and source records attributed to Julia Koltai.

4 recordsLinked to original sources

arXiv2026

Occupational Convergence or Divergence? Mapping Labor Market Structural Shifts Driven by AI Penetration

Artificial intelligence (AI) is rapidly becoming a defining feature of contemporary labor markets, yet it remains unclear whether its diffusion is producing a common set of competencies across occupations or deepening occupational divisions. We investigate how AI related skill demand is reshaping labor market structure using large scale online vacancy data from ten countries spanning the Global North and Global South. Combining natural language processing, a large language model, and multilevel bipartite network analysis, we map relationships between occupations, required skills, and career stages in the emerging AI economy. We find that AI demand is overwhelmingly concentrated within a narrow technical core, with approximately three quarters to four fifths of AI related vacancies located in STEM occupations across all countries. AI intensive jobs consistently emphasize Python, SQL, machine learning, and data analysis, generating convergence among highly exposed occupations. However, this convergence does not extend across the wider labor market. Instead, AI competencies remain largely confined to technical domains and are most strongly demanded at labor market entry. These findings reveal convergence within an AI exposed core but divergence between that core and the rest of the occupational structure. Rather than democratizing opportunities, AI appears to reinforce occupational stratification, raising barriers to entry and concentrating the benefits of AI adoption among workers and occupations with prior technological advantages.

physics.soc-ph↗

arXiv2026

Toward a Hybrid Digital Twin of Society: Quantifying Cognitive-Spatial Linkages Through Online-Offline Feedback Networks

Digital platforms increasingly shape how people experience and navigate cities, linking virtual information seeking with physical mobility. Despite this interdependence, online and offline activities are often studied separately in urban mobility research. This paper introduces the Feedback Network, a computational framework that captures interactions between cognitive activity in digital environments and behavior in physical space. Using Google Search and Location History data from the same individuals, collected through a data donation framework in Budapest, Hungary, between 2018 and 2022, we examine how online search patterns and offline visitation behavior co-evolve. We combine semantic and spatial analytical approaches. Radius of gyration is adapted to measure variation in geographic mobility and semantic exploration, enabling comparison between physical movement and online cognitive dispersion. A Feedback Network models transitions between search-related and location-related activity clusters and is evaluated using Concentration Entropy, which measures whether behavioral flows are concentrated around routine pathways or distributed across exploratory transitions. The results show that online exploration is more concentrated than offline mobility, suggesting narrower and more repetitive semantic interests, while physical movement remains relatively diverse. Persistent linkages between search and visitation activities related to retail and business services indicate stable cognitive-spatial behavioral loops. The COVID-19 pandemic disrupted spatial routines more strongly than cognitive exploration, widening the gap between digital engagement and realized movement. The findings demonstrate that urban mobility depends on the interaction between informational exposure and spatial encounter and provide a foundation for Hybrid Digital Twins of Society.

physics.soc-ph↗

arXiv2024

Toxic behavior silences online political conversations

Quantifying how individuals react to social influence is crucial for tackling collective political behavior online. While many studies of opinion in public forums focus on social feedback, they often overlook the potential for human interactions to result in self-censorship. Here, we investigate political deliberation in online spaces by exploring the hypothesis that individuals may refrain from expressing minority opinions publicly due to being exposed to toxic behavior. Analyzing conversations under YouTube videos from six prominent US news outlets around the 2020 US presidential elections, we observe patterns of self-censorship signaling the influence of peer toxicity on users' behavior. Using hidden Markov models, we identify a latent state consistent with toxicity-driven silence. Such state is characterized by reduced user activity and a higher likelihood of posting toxic content, indicating an environment where extreme and antisocial behaviors thrive. Our findings offer insights into the intricacies of online political deliberation and emphasize the importance of considering self-censorship dynamics to properly characterize ideological polarization in digital spheres.

cs.SI↗

arXiv2021

The presence of occupational structure in online texts based on word embedding NLP models

Research on social stratification is closely linked to analysing the prestige associated with different occupations. This research focuses on the positions of occupations in the semantic space represented by large amounts of textual data. The results are compared to standard results in social stratification to see whether the classical results are reproduced and if additional insights can be gained into the social positions of occupations. The paper gives an affirmative answer to both questions. The results show fundamental similarity of the occupational structure obtained from text analysis to the structure described by prestige and social distance scales. While our research reinforces many theories and empirical findings of the traditional body of literature on social stratification and, in particular, occupational hierarchy, it pointed to the importance of a factor not discussed in the main line of stratification literature so far: the power and organizational aspect.

cs.CL↗