arXiv subjects

Alexander Spangher

Publications and source records attributed to Alexander Spangher.

28 records · Page 2Linked to original sources

arXiv2022

NewsEdits: A Dataset of Revision Histories for News Articles (Technical Report: Data Processing)

News article revision histories have the potential to give us novel insights across varied fields of linguistics and social sciences. In this work, we present, to our knowledge, the first publicly available dataset of news article revision histories, or NewsEdits. Our dataset is multilingual; it contains 1,278,804 articles with 4,609,430 versions from over 22 English- and French-language newspaper sources based in three countries. Across version pairs, we count 10.9 million added sentences; 8.9 million changed sentences and 6.8 million removed sentences. Within the changed sentences, we derive 72 million atomic edits. NewsEdits is, to our knowledge, the largest corpus of revision histories of any domain.

cs.CL↗

arXiv2022

StateCensusLaws.org: A Web Application for Consuming and Annotating Legal Discourse Learning

In this work, we create a web application to highlight the output of NLP models trained to parse and label discourse segments in law text. Our system is built primarily with journalists and legal interpreters in mind, and we focus on state-level law that uses U.S. Census population numbers to allocate resources and organize government. Our system exposes a corpus we collect of 6,000 state-level laws that pertain to the U.S. census, using 25 scrapers we built to crawl state law websites, which we release. We also build a novel, flexible annotation framework that can handle span-tagging and relation tagging on an arbitrary input text document and be embedded simply into any webpage. This framework allows journalists and researchers to add to our annotation database by correcting and tagging new data.

cs.CL↗

arXiv2022

NewsEdits: A News Article Revision Dataset and a Document-Level Reasoning Challenge

News article revision histories provide clues to narrative and factual evolution in news articles. To facilitate analysis of this evolution, we present the first publicly available dataset of news revision histories, NewsEdits. Our dataset is large-scale and multilingual; it contains 1.2 million articles with 4.6 million versions from over 22 English- and French-language newspaper sources based in three countries, spanning 15 years of coverage (2006-2021). We define article-level edit actions: Addition, Deletion, Edit and Refactor, and develop a high-accuracy extraction algorithm to identify these actions. To underscore the factual nature of many edit actions, we conduct analyses showing that added and deleted sentences are more likely to contain updating events, main content and quotes than unchanged sentences. Finally, to explore whether edit actions are predictable, we introduce three novel tasks aimed at predicting actions performed during version updates. We show that these tasks are possible for expert humans but are challenging for large NLP models. We hope this can spur research in narrative framing and help provide predictive tools for journalists chasing breaking news.

cs.CL↗

arXiv2022

If it Bleeds, it Leads: A Computational Approach to Covering Crime in Los Angeles

Developing and improving computational approaches to covering news can increase journalistic output and improve the way stories are covered. In this work we approach the problem of covering crime stories in Los Angeles. We present a machine-in-the-loop system that covers individual crimes by (1) learning the prototypical coverage archetypes from classical news articles on crime to learn their structure and (2) using output from the Los Angeles Police department to generate "lede paragraphs", first structural unit of crime-articles. We introduce a probabilistic graphical model for learning article structure and a rule-based system for generating ledes. We hope our work can lead to systems that use these components together to form the skeletons of news articles covering crime. This work was done for a class project in Jonathan May's Advanced Natural Language Processing Course, Fall, 2019.

cs.CL↗

arXiv2021

Modeling "Newsworthiness" for Lead-Generation Across Corpora

Journalists obtain "leads", or story ideas, by reading large corpora of government records: court cases, proposed bills, etc. However, only a small percentage of such records are interesting documents. We propose a model of "newsworthiness" aimed at surfacing interesting documents. We train models on automatically labeled corpora -- published newspaper articles -- to predict whether each article was a front-page article (i.e., \textbf{newsworthy}) or not (i.e., \textbf{less newsworthy}). We transfer these models to unlabeled corpora -- court cases, bills, city-council meeting minutes -- to rank documents in these corpora on "newsworthiness". A fine-tuned RoBERTa model achieves .93 AUC performance on heldout labeled documents, and .88 AUC on expert-validated unlabeled corpora. We provide interpretation and visualization for our models.

cs.CL↗

arXiv2021

"Don't quote me on that": Finding Mixtures of Sources in News Articles

Journalists publish statements provided by people, or \textit{sources} to contextualize current events, help voters make informed decisions, and hold powerful individuals accountable. In this work, we construct an ontological labeling system for sources based on each source's \textit{affiliation} and \textit{role}. We build a probabilistic model to infer these attributes for named sources and to describe news articles as mixtures of these sources. Our model outperforms existing mixture modeling and co-clustering approaches and correctly infers source-type in 80\% of expert-evaluated trials. Such work can facilitate research in downstream tasks like opinion and argumentation mining, representing a first step towards machine-in-the-loop \textit{computational journalism} systems.

cs.CL↗

arXiv2021

Multitask Learning for Class-Imbalanced Discourse Classification

Small class-imbalanced datasets, common in many high-level semantic tasks like discourse analysis, present a particular challenge to current deep-learning architectures. In this work, we perform an extensive analysis on sentence-level classification approaches for the News Discourse dataset, one of the largest high-level semantic discourse datasets recently published. We show that a multitask approach can improve 7% Micro F1-score upon current state-of-the-art benchmarks, due in part to label corrections across tasks, which improve performance for underrepresented classes. We also offer a comparative review of additional techniques proposed to address resource-poor problems in NLP, and show that none of these approaches can improve classification accuracy in such a setting.

cs.CL↗

arXiv2019

Actionable Recourse in Linear Classification

Machine learning models are increasingly used to automate decisions that affect humans - deciding who should receive a loan, a job interview, or a social service. In such applications, a person should have the ability to change the decision of a model. When a person is denied a loan by a credit score, for example, they should be able to alter its input variables in a way that guarantees approval. Otherwise, they will be denied the loan as long as the model is deployed. More importantly, they will lack the ability to influence a decision that affects their livelihood. In this paper, we frame these issues in terms of recourse, which we define as the ability of a person to change the decision of a model by altering actionable input variables (e.g., income vs. age or marital status). We present integer programming tools to ensure recourse in linear classification problems without interfering in model development. We demonstrate how our tools can inform stakeholders through experiments on credit scoring problems. Our results show that recourse can be significantly affected by standard practices in model development, and motivate the need to evaluate recourse in practice.

stat.ML↗

arXiv2018

Analysis of Strategy and Spread of Russia-sponsored Content in the US in 2017

The Russia-based Internet Research Agency (IRA) carried out a broad information campaign in the U.S. before and after the 2016 presidential election. The organization created an expansive set of internet properties: web domains, Facebook pages, and Twitter bots, which received traffic via purchased Facebook ads, tweets, and search engines indexing their domains. We investigate the scope of IRA activities in 2017, joining data from Facebook and Twitter with logs from the Internet Explorer 11 and Edge browsers and the Bing.com search engine. The studies demonstrate both the ease with which malicious actors can harness social media and search engines for propaganda campaigns, and the ability to track and understand such activities by fusing content and activity resources from multiple internet services. We show how cross-platform analyses can provide an unprecedented lens on attempts to manipulate opinions and elections in democracies.

cs.SI↗

arXiv2015

Bayesian Nonparametrics in Topic Modeling: A Brief Tutorial

Using nonparametric methods has been increasingly explored in Bayesian hierarchical modeling as a way to increase model flexibility. Although the field shows a lot of promise, inference in many models, including Hierachical Dirichlet Processes (HDP), remain prohibitively slow. One promising path forward is to exploit the submodularity inherent in Indian Buffet Process (IBP) to derive near-optimal solutions in polynomial time. In this work, I will present a brief tutorial on Bayesian nonparametric methods, especially as they are applied to topic modeling. I will show a comparison between different non-parametric models and the current state-of-the-art parametric model, Latent Dirichlet Allocation (LDA).

stat.ML↗