Search arXivSearch

arXiv · 2007.09124

Local information sources received the most attention from Puerto Ricans during the aftermath of Hurricane María

Abstract

In September 2017, Hurricane María made landfall across the Caribbean region as a category 4 storm. In the aftermath, many residents of Puerto Rico were without power or clean running water for nearly a year. Using both English and Spanish tweets from September 16 to October 15 2017, we investigate discussion of María both on and off the island, constructing a proxy for the temporal network of communication between victims of the hurricane and others. We use information theoretic tools to compare the lexical divergence of different subgroups within the network. Lastly, we quantify temporal changes in user prominence throughout the event. We find at the global level that Spanish tweets more often contained messages of hope and a focus on those helping. At the local level, we find that information propagating among Puerto Ricans most often originated from sources local to the island, such as journalists and politicians. Critically, content from these accounts overshadows content from celebrities, global news networks, and the like for the large majority of the time period studied. Our findings reveal insight into ways social media campaigns could be deployed to disseminate relief information during similar events in the future.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Benjamin Freixas Emery, Meredith T. Niles, Christopher M. Danforth, Peter Sheridan Dodds. 2020-07-17. Local information sources received the most attention from Puerto Ricans during the aftermath of Hurricane María. https://arxiv.org/abs/2007.09124

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

KEEP EXPLORING

Related papers

Predicting Social Media Engagement using Machine Learning

Social media platforms are popular channels for disseminating information, owing to their large user bases and ease of access. Companies also use social media as an important aspect of the advertising process. By creating high-quality posts, companies can strengthen their engagement metrics and increase their follower count. While a growing body of research has examined social media engagement, fewer studies have jointly examined the visual, textual, and temporal features of image posts, even though these features collectively determine the performance of content on social media. To understand the important drivers of social media engagement, we collect image posts of furniture firms on Facebook and extract visual, temporal, and textual features from them using text and image analytics methods. We evaluate several machine learning models - including Random Forest, Light Gradient Boosting Machine (LightGBM), and eXtreme Gradient Boosting (XGBoost) - to assess the drivers and the prediction power of social media engagement using the features from our data. Our research quantifies the extent to which these features are associated with interactions and provides recommendations that organizations may consider.

cs.SI

netseg: a Python Package for Measuring Structural Polarization and Segregation in Social Networks

The study of structural polarization and segregation in social networks is an established line of research, and the quantification of both phenomena proceeds through a set of widely cited network indices. The code implementing those indices, however, is seldom released and almost never tested. We present netseg, a comprehensively documented Python package implementing these indices, most of them generalized to more than two groups and to directed as well as undirected input. It ports the R package of the same name and adds measures the R version lacks, among them Random Walk Controversy, Boundary Connectivity, Dipole Moment, and Moran's I. The package operates on igraph objects and performs the underlying graph operations (e.g., neighborhood queries and random-walk simulation) through igraph's Python interface, so that they execute in compiled code rather than in interpreted Python. For several of the indices this yields runtimes orders of magnitude below those of the available open-source implementations, which the documentation reports in benchmarks. Most of these indices are defined as a divergence from a null model, and published implementations fix that null model to a uniform random graph of matching density. netseg accepts an ensemble of graphs as a sample from an arbitrary null model, and distinguishes indices that already incorporate a baseline from those that do not, adjusting the comparison accordingly to avoid double subtraction. The documentation provides, for each index, a worked empirical example, its behaviour at the degenerate cases where it is undefined, a benchmark, and the procedure for substituting a custom null model. We report the behaviour of every index over a parameter sweep of a generative opinion model, and apply them to a county-level railroad network built from nineteenth-century operator records joined to full-count census data.

cs.SI

Higher-order Network phenomena of cascading failures in resilient cities

Modern urban resilience is threatened by cascading failures in multimodal transport networks, where localized shocks trigger widespread paralysis. Existing models, limited by their focus on pairwise interactions, often underestimate this systemic risk. To address this, we introduce a framework that confronts higher-order network theory with empirical evidence from a large-scale, real-world multimodal transport network. Our findings confirm a fundamental duality: network integration enhances static robustness metrics but simultaneously creates the structural pathways for catastrophic cascades. Crucially, we uncover the source of this paradox: a profound disconnect between static network structure and dynamic functional failure. We provide strong evidence that metrics derived from the network's static blueprint-encompassing both conventional low-order centrality and novel higher-order structural analyses-are fundamentally disconnected from and thus poor predictors of a system's dynamic functional resilience. This result highlights the inherent limitations of static analysis and underscores the need for a paradigm shift towards dynamic models to design and manage truly resilient urban systems.

cs.SI