Linguistic diversity is increasingly under pressure globally and is becoming ever more relevant in digital contexts, where many languages remain structurally under-resourced, limiting access to language technologies and inhibiting equitable NLP development. To support linguistic diversity, publicly available data are needed that capture both the number of languages spoken and the distribution of speakers across them. We introduce GlobLingDiv, a database that uses country-level speaker distributions to derive language richness and entropy-based diversity measures, alongside a population-weighted digital language support measure. Applying these metrics globally, we examine the association between linguistic diversity and digital support conditions. The results reveal a substantial imbalance: highly diverse linguistic landscapes show comparatively low digital support, underscoring the need for more inclusive NLP environments.

With the conflation of digital and non-digital spaces, and NLP technologies being integrated into an increasing number of aspects of daily life, linguistic diversity cannot be fully understood without considering how language is used online. While existing models of linguistic diversity typically have relied on speaker numbers or language production, the dimension of diversity in language consumption remains comparatively understudied. To facilitate such research, we introduce WikiLingDiv, an openly accessible dataset for quantifying linguistic diversity in online knowledge retrieval using Wikipedia page views. Our dataset is based on yearly page views of 340 language editions of Wikipedia, aggregated across 239 countries and territories over 10 years (2015-2024). Using the dataset, we illustrate spatial and temporal patterns of digital linguistic diversity, suggesting that diversity has both increased and decreased across countries and regions, while highlighting country-specific dynamics in language usage. We release the dataset as an openly available and easily integrable data resource for researchers in computational linguistics, digital humanities, and the broader social sciences, enabling further work on linguistic variation, digital inequality, and the interaction between language use and digital technology.

Evidently, words can have multiple senses. For example, the word mess refers to a place to have food or to a confusing situation. How exactly multiple senses emerge is less clear. In this work, we propose and analyze a mathematical model of the evolution of lexical meaning to investigate mechanisms leading to polysemy. This model features factors that have been discussed to impact the semantic processing and transmission of words: word frequency, non-conformism, and semantic discriminability. We formally derive conditions under which a sense of a word tends to diversify itself into multiple senses that coexist stably. The model predicts that diversification is promoted by low frequency, a strong bias for non-conformist usage, and high semantic discriminability. We statistically validate these predictions with historical language data covering semantic developments of a set of English words. Multiple alternative measures are used to operationalize each variable involved, and we confirm the predicted tendencies for twelve combinations of measures.

This paper introduces the Austrian German sentiment dictionary ALPIN to account for the lack of resources for dictionary-based sentiment analysis in this specific variety of German, which is characterized by lexical idiosyncrasies that also affect word sentiment. The proposed language resource is based on Austrian news media in the field of politics, an austriacism list based on different resources and a posting data set based on a popular Austrian news media. Different resources are used to increase the diversity of the resulting language resource. Extensive crowd-sourcing is performed followed by evaluation and automatic conversion into sentiment scores. We show that crowd-sourcing enables the creation of a sentiment dictionary for the Austrian German domain. Additionally, the different parts of the sentiment dictionary are evaluated to show their impact on the resulting resource. Furthermore, the proposed dictionary is utilized in a web application and available for future research and free to use for anyone.

In this paper, we present a web based interactive visualization tool for lexical networks based on the utterances of Austrian Members of Parliament. The tool is designed to compare two networks in parallel and is composed of graph visualization, node-metrics comparison and time-series comparison components that are interconnected with each other.

Most diachronic studies on both lexico-semantic change and political language usage are based on individual or comparable corpora. In this paper, we explore ways of studying the stability (and changeability) of lexical usage in political discourse across two corpora which are substantially different in structure and size. We present a case study focusing on lexical items associated with political parties in two diachronic corpora of Austrian German, namely a diachronic media corpus (AMC) and a corpus of parliamentary records (ParlAT), and measure the cross-temporal stability of lexical usage over a period of 20 years. We conduct three sets of comparative analyses investigating a) the stability of sets of lexical items associated with the three major political parties over time, b) lexical similarity between parties, and c) the similarity between the lexical choices in parliamentary speeches by members of the parties vis-‘a-vis the media’s reporting on the parties. We employ time series modeling using generalized additive models (GAMs) to compare the lexical similarities and differences between parties within and across corpora. The results show that changes observed in these measures can be meaningfully related to political events during that time.