Search arXivSearch

arXiv · 2210.14962

Identifying Diversity, Equity, Inclusion, and Accessibility (DEIA) Indicators for Transportation Systems using Social Media Data: The Case of New York City during Covid-19 Pandemic

Abstract

The adoption of transportation policies that prioritized highway expansion over public transportation has disproportionately impacted minorities and low-income people by restricting their access to social and economic opportunities and thus resulting in residential segregation. Policymakers, transportation researchers, planners, and practitioners have started acknowledging the need to build a diverse, equitable, inclusive, and accessible (DEIA) transportation system. Traditionally, this has been done through survey-based approaches that are time-consuming and expensive. While there is recent attention on leveraging social media data in transportation, the literature is inconclusive regarding the use of social media data as a viable alternative to traditional sources to identify the latent DEIA indicators based on public reactions and perspectives on social media. This study utilized large-scale Twitter data covering eight counties around the New York City (NYC) area during the initial phase of the Covid-19 lockdown to address this research gap. Natural language processing techniques were used to identify transportation-related major DEIA issues for residents living around NYC by analyzing their relevant tweet conversations. The study revealed that citizens, who had negative sentiments toward the DEIA of their local transportation system, broadly discussed racism, income, unemployment, gender, ride dependency, transportation modes, and dependent groups. Analyzing the socio-demographic information based on census tracts, the study also observed that areas with a higher percentage of low-income, female, Hispanic, and Latino populations share more concerns about transportation DEIA on Twitter.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Fariha Nazneen Rista, Khondhaker Al Momin, Arif Mohaimin Sadri. 2022-10-26. Identifying Diversity, Equity, Inclusion, and Accessibility (DEIA) Indicators for Transportation Systems using Social Media Data: The Case of New York City during Covid-19 Pandemic. https://arxiv.org/abs/2210.14962

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Empirical Trajectory Sparsity Biases Mobility-Informed Epidemic Modeling

GPS mobility data are increasingly used in epidemic modeling, allowing the construction of co-location networks or population flows. These trajectories typically exhibit high temporal sparsity because data collection is opportunistic and tied to phone use. Despite growing awareness of this limitation, the analysis and treatment of biases derived from it have been largely overlooked in existing epidemic modeling studies, raising concerns about the robustness of downstream inferences. We introduce a principled framework to quantify the impact of trajectory sparsity on key epidemic modeling outcomes across different levels of data missingness. Our approach leverages a highly complete dataset that exhibits both near-complete and sparse GPS trajectories. Near-complete trajectories provide baseline epidemic outcomes, while sparse trajectories provide realistic missingness patterns that we impose on the baseline to measure bias. In this way, we show how missing records can result in substantial underestimation of key measures of epidemic intensity, explained not only by the amount of missing data, but by more complex features of data missingness that should be taken into account when designing correction methods. Finally, we propose and evaluate a correction based on inverse probability weighting of the contact network before epidemic model calibration, which is shown to reduce bias and parameter misspecification. We also demonstrate this correction on a separate anonymized sample from a commercial GPS mobility dataset and report on its effect. Together, our findings provide a first rigorous quantification of trajectory-sparsity bias in epidemic modeling, offering initial guidance on the treatment of this issue.

cs.SI

Threat Amplified, Blame Restrained: LLM-Assisted Media Framing Analysis of the 2026 Bangladesh Measles Outbreak

How news media frame and emotionally code a public health emergency shapes public risk perception and trust, yet outbreak-coverage dynamics remain understudied for low- and middle-income countries (LMICs). We examine sentiment and stance in English-language Bangladeshi coverage of the 2026 measles outbreak -- the country's most severe in two decades, with over 97,000 suspected cases and 600 deaths across 61 of 64 districts, unfolding after the 2024 change of government and a 2024-2025 vaccine stockout. Using the Internet Archive, we build a reproducible corpus of 403 headlines from seven national outlets (396 in-window in 2026), label them for binary sentiment and four-way stance via a large language model under a locked codebook, and validate against a two-coder human-adjudicated gold standard (n=153; Cohen's kappa=0.89 stance, 0.75 sentiment). Aligned to the DGHS epidemic curve, coverage grew significantly more negative (56% to 88% negative; Cochran-Armitage z=4.12, p<.001) and risk-amplification framing intensified (44% to 84%; z=4.15, p<.001). Media negativity lagged incidence, tracking cumulative mortality. Contrary to the political backdrop, blame remained a minority frame (~9% overall) and was overwhelmingly systemic (32 of 37, 86%) rather than directed at named actors. The pipeline offers a scalable, transparent method for LMIC outbreak-media analysis; Bangladeshi coverage amplified threat far more than it assigned political blame.

cs.SI

OranSim: Simulating Social Media Marketing

Social simulation studies how individual behavior and social interaction produce collective outcomes. In social media marketing, campaign actions shape which consumers encounter the content and how they respond; these responses then spread through the population. We propose OranSim, a social simulation framework that connects creative, creator, targeting, and budget choices to this process. Heterogeneous consumers receive exposure according to content matching and platform allocation and generate initial responses, which propagate among 60 population segments. Candidate campaigns share the initial population and aligned random numbers, making their response trajectories comparable under action changes. In a controlled synthetic campaign, doubling the budget approximately doubles reach while lowering mean content match and engagement probability among the reached consumers; mean 14-day cumulative simulated response mass rises to 1.96 times the baseline. LightGBM predictors fitted to 39,000 historical RedNote notes estimate platform engagement with log-scale $R^2$ of 0.56--0.62 in five-fold cross-validation; a separate 12,154-note corpus supplies temporal, unseen-creator, and held-out-niche test splits. Public-data experiments evaluate policy value and audience ranking, and paired synthetic outcomes test counterfactual scoring. Together, scenario trajectories and engagement estimates support campaign selection according to a prespecified marketing objective. Code is available at https://github.com/OranAi-Ltd/oransim.

cs.SI