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Abstract Purpose of ReviewArtificial intelligence (AI) is disrupting science and discovery across disciplines, offering new modes of inquiry that are changing how questions are asked and answered and upsetting established norms. In this paper, we review the state of the art of AI in landscape ecology and offer six areas of opportunity for landscape ecologists to capitalize on AI tools moving forward. These areas include geospatial AI (GeoAI), geometric AI, Explainable AI (xAI), generative AI (GenAI), Natural Language Processing (NLP), and robotics. Recent FindingsLandscape ecology has a long history of using AI, notably machine learning methods for image classification tasks, agent-based modeling, and species distribution modeling but also knowledge representation and automated reasoning for landscape generation and spatial planning. Methods have become more diverse and complex in recent years, with a new generation of AI-based tools rapidly emerging. These new tools have potential to improve how landscape ecologists map, measure, and model landscape patterns and processes as well as improve the explainability of model outputs. SummaryThere are many untapped opportunities for landscape ecologists to leverage emerging AI-based tools in research and practice including generating virtual landscapes for simulating processes such as wildfires and leveraging natural language processing to generate new insights from text data. Regardless of the application, researchers using AI tools must also consider the ethical implications of data and algorithmic biases and critically assess how these methods can be used responsibly.
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This content will become publicly available on May 1, 2027
Variation across Regions and Demographics in African American Language Morphosyntax: Evidence from Large-Scale Twitter Data
Abstract Social media data and computational tools have become increasingly powerful alternatives to traditional methods of data collection and annotation for sociolinguistic research. While not without their own drawbacks, these resources can help address critical problems faced by researchers, such as sparse data, the observer's paradox, and annotating corpora at scale. This article presents a Twitter dataset of 227 million conversational messages across the U.S. and uses deep learning natural language processing (NLP) methods to analyze how 18 morphosyntactic features used by speakers of African American Language (AAL) vary geographically and across 12 demographic factors. Results demonstrate more frequent usage of AAL features in the rural South and in Mexican American communities, both of which are underrepresented in the literature. This work constitutes the first national-level description and analysis of overall morphosyntactic variation in AAL, and demonstrates how NLP tools enable the study of large-scale data to gain a more representative understanding of speech in marginalized communities.
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- PAR ID:
- 10670531
- Publisher / Repository:
- Duke University Press
- Date Published:
- Journal Name:
- American Speech
- Volume:
- 101
- Issue:
- 2
- ISSN:
- 0003-1283
- Page Range / eLocation ID:
- 144 to 178
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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