arXiv subjects

Sukankana Chakraborty

Publications and source records attributed to Sukankana Chakraborty.

5 recordsLinked to original sources

arXiv2026

Spatial accessibility to food banks hinders food parcel uptake in England and Wales, particularly in rural areas

Food bank use in the UK has soared in recent years. The combination of a global pandemic, over-stretched and underfunded public services, and a cost-of-living crisis has meant that millions of people cannot afford basic essentials such as food, heating, housing, and baby supplies. Food bank use is driven by a complex range of factors, including poverty, health emergencies, income shocks, delays to universal credit payments, housing issues, and homelessness. In this study we identify an urban-rural divide in spatial accessibility to food banks. In cities, food banks tend to be highly accessible by public transport to deprived populations but, on average, have shorter opening hours. In rural areas, however, despite generally longer opening hours, food banks are typically not highly accessible except for the most deprived residents. This matters. We find that spatial accessibility to a Trussell food bank centre is a key predictor of food parcel uptake, with a significantly stronger relationship than factors emphasised in the literature such as disability and Universal Credit. Importantly, this relationship is markedly stronger for rural populations, suggesting an unmet need in deprived rural areas far from food banks. Our work has important implications for food bank policy, suggesting a need for improved public transport in rural areas, and optimising current food bank locations and delivery models.

econ.GN↗

arXiv2025

Who is driving the conversation? Analysing the nodality of British MPs and journalists on social media

With the rise of social media, political conversations now take place in more diffuse environments. In this context, it is not always clear why some actors, more than others, have greater influence on how discussions are shaped. To investigate the factors behind such influence, we build on nodality, a concept in political science which describes the capacity of an actor to exchange information within discourse networks. This concept goes beyond traditional network metrics that describe the position of an actor in the network to include exogenous drivers of influence (e.g. factors relating to organisational hierarchies). We study online discourse on Twitter (now X) in the UK to measure the relative nodality of two sets of policy actors - Members of Parliament (MPs) and accredited journalists - on four policy topics. We find that influence on the platform is driven by two key factors: (i) active nodality, derived from the actor's level of topic-related engagement, and (ii) inherent nodality, which is independent of the platform discourse and reflects the actor's institutional position. These findings significantly further our understanding of the origins of influence on social media platforms and suggest in which contexts influence is transferable across topics.

cs.SI↗

arXiv2025

When not to target negative ties? Studying competitive influence maximisation in signed networks

We explore the influence maximisation problem in networks with negative ties. Where prior work has focused on unsigned networks, we investigate the need to consider negative ties in networks while trying to maximise spread in a population - particularly under competitive conditions. Given a signed network we optimise the strategies of a focal controller, against competing influence in the network, using two approaches - either the focal controller uses a sign-agnostic approach or they factor in the sign of the edges while optimising their strategy. We compare the difference in vote-shares (or the share of population) obtained by both these methods to determine the need to navigate negative ties in these settings. More specifically, we study the impact of: (a) network topology, (b) resource conditions and (c) competitor strategies on the difference in vote shares obtained across both methodologies. We observe that gains are maximum when resources available to the focal controller are low and the competitor avoids negative edges in their strategy. Conversely, gains are insignificant irrespective of resource conditions when the competitor targets the network indiscriminately. Finally, we study the problem in a game-theoretic setting, where we simultaneously optimise the strategies of both competitors. Interestingly we observe that, strategising with the knowledge of negative ties can occasionally also lead to loss in vote-shares.

cs.SI↗

arXiv2024

MIMDE: Exploring the Use of Synthetic vs Human Data for Evaluating Multi-Insight Multi-Document Extraction Tasks

Large language models (LLMs) have demonstrated remarkable capabilities in text analysis tasks, yet their evaluation on complex, real-world applications remains challenging. We define a set of tasks, Multi-Insight Multi-Document Extraction (MIMDE) tasks, which involves extracting an optimal set of insights from a document corpus and mapping these insights back to their source documents. This task is fundamental to many practical applications, from analyzing survey responses to processing medical records, where identifying and tracing key insights across documents is crucial. We develop an evaluation framework for MIMDE and introduce a novel set of complementary human and synthetic datasets to examine the potential of synthetic data for LLM evaluation. After establishing optimal metrics for comparing extracted insights, we benchmark 20 state-of-the-art LLMs on both datasets. Our analysis reveals a strong correlation (0.71) between the ability of LLMs to extracts insights on our two datasets but synthetic data fails to capture the complexity of document-level analysis. These findings offer crucial guidance for the use of synthetic data in evaluating text analysis systems, highlighting both its potential and limitations.

cs.CL↗

arXiv2018

Dynamics of new strain emergence on a temporal network

Multi-strain competition on networks is observed in many contexts, including infectious disease ecology, information dissemination or behavioral adaptation to epidemics. Despite a substantial body of research has been developed considering static, time-aggregated networks, it remains a challenge to understand the transmission of concurrent strains when links of the network are created and destroyed over time. Here we analyze how network dynamics shapes the outcome of the competition between an initially endemic strain and an emerging one, when both strains follow a susceptible-infected-susceptible dynamics, and spread at time scales comparable with the network evolution one. Using time-resolved data of close-proximity interactions between patients admitted to a hospital and medical health care workers, we analyze the impact of temporal patterns and initial conditions on the dominance diagram and coexistence time. We find that strong variations in activity volume cause the probability that the emerging strain replaces the endemic one to be highly sensitive to the time of emergence. The temporal structure of the network shapes the dominance diagram, with significant variations in the replacement probability (for a given set of epidemiological parameters) observed from the empirical network and a randomized version of it. Our work contributes towards the description of the complex interplay between competing pathogens on temporal networks.

physics.soc-ph↗