What Kind of Judge is Brett Kavanaugh?

This article reports the results of a series of data analyses of how recent Supreme Court nominee Brett Kavanaugh compares to other potential Supreme Court nominees and current Supreme Court Justices in his judging style. The analyses reveal a number of ways in which Judge Kavanaugh differs systematically from his colleagues. First, Kavanaugh dissents and is dissented against along partisan lines. More than other Judges and Justices, Kavanaugh dissents at a higher rate during the lead-up to elections, suggesting that he feels personally invested in national politics. Far more often than his colleagues, he justifies his decisions with conservative doctrines, including politicized precedents that tend to be favored by Republican-appointed judges, the original Articles of the Constitution, and the language of economics and free markets. These findings demonstrate the usefulness of quantitative analysis in the evaluation of judicial nominees

Predicting outcomes in crowdfunding campaigns with textual, visual, and linguistic signals

This paper introduces a neural network and natural language processing approach to predict the outcome of crowdfunding startup pitches using text, speech, and video metadata in 20,188 crowdfunding campaigns. Our study emphasizes the need to understand crowdfunding from an investor’s perspective. Linguistic styles in crowdfunding campaigns that aim to trigger excitement or are aimed at inclusiveness are better predictors of campaign success than firm-level determinants. At the contrary, higher uncertainty perceptions about the state of product development may substantially reduce evaluations of new products and reduce purchasing intentions among potential funders. Our findings emphasize that positive psychological language is salient in environments where objective information is scarce and where investment preferences are taste based. Employing enthusiastic language or showing the product in action may capture an individual’s attention. Using all technology and design-related crowdfunding campaigns launched on Kickstarter, our study underscores the need to align potential consumers’ expectations with the visualization and presentation of the crowdfunding campaign

Information Retrieval with Finnish Case Law Embeddings

In this work, five text vectorisation models' capability in embedding Finnish case law texts to vector space for inter-textual similarity computation is studied. The embeddings and their computed similarities are used to create a Finnish case law retrieval system that allows effective querying with full documents. A working web application is presented as a part of the work. The case law data for the work is provided by the Finnish Ministry of Justice, and the studied models are: TF-IDF, LDA, Word2Vec, Doc2Vec and Doc2vecC

A framework for intelligent policy decision making based on a government data hub

Author ProofThe e-Oman Integration Platform is a data hub that enables data exchanges across government in response to transactions. With millions of transactions weekly, and thereby data exchanges, we propose to investigate the potential of gathering intelligence from these linked sources to help government officials make more informed decisions. A key feature of this data is the richness and accuracy, which increases the value of the learning outcome when augmented by other big and open data sources. We consider a high-level framework within a government context, taking into account issues related to the definition of public policies, data privacy, and the potential benefits to society. A preliminary, qualitative validation of the framework in the context of e-Oman is presented. This paper lays out foundational work into an ongoing research to implement government decision-making based on big data.“SmartEGOV: Harnessing EGOV for Smart Governance (Foundations, Methods, Tools)/NORTE-01-0145-FEDER-000037”, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (EFDR

Evolving linguistic divergence on polarizing social media

Language change is influenced by many factors, but often starts from synchronic variation, where multiple linguistic patterns or forms coexist, or where different speech communities use language in increasingly different ways. Besides regional or economic reasons, communities may form and segregate based on political alignment. The latter, referred to as political polarization, is of growing societal concern across the world. Here we map and quantify linguistic divergence across the partisan left-right divide in the United States, using social media data. We develop a general methodology to delineate (social) media users by their political preference, based on which (potentially biased) news media accounts they do and do not follow on a given platform. Our data consists of 1.5M short posts by 10k users (about 20M words) from the social media platform Twitter (now “X”). Delineating this sample involved mining the platform for the lists of followers (n = 422M) of 72 large news media accounts. We quantify divergence in topics of conversation and word frequencies, messaging sentiment, and lexical semantics of words and emoji. We find signs of linguistic divergence across all these aspects, especially in topics and themes of conversation, in line with previous research. While US American English remains largely intelligible within its large speech community, our findings point at areas where miscommunication may eventually arise given ongoing polarization and therefore potential linguistic divergence. Our flexible methodology — combining data mining, lexicostatistics, machine learning, large language models and a systematic human annotation approach — is largely language and platform agnostic. In other words, while we focus here on US political divides and US English, the same approach is applicable to other countries, languages, and social media platforms