Aug 2026· International Migration Review· 0 citations· 70 references
TL;DR
The Reddit community r/IWantOut is introduced as a data source for studying migration aspirations and a validated natural language processing (NLP) pipeline for analyzing it and three analytical approaches are demonstrated, including dictionary- and rule-based extraction and large language model (LLM)-based zero-shot classification.
Abstract
In contemporary migration studies, online forums are common spaces where people exchange information and seek advice about international migration. This methods note introduces the Reddit community r/IWantOut as a data source for studying migration aspirations and presents a validated natural language processing (NLP) pipeline for analyzing it. The forum's community rules require posters to encode age, gender, origin, and intended destinations in a fixed title format, so users label their own migration aspirations in a semi-machine-readable form when they post. Using 156,313 submissions from 2009 to 2026 (58,892 after cleaning), we demonstrate three analytical approaches: (1) time-series analysis to identify temporal shifts in discourse volume, (2) dictionary- and rule-based extraction, together with Named Entity Recognition (NER), to recover origin–destination pairs and sociodemographic attributes from the structured titles, and (3) large language model (LLM)-based zero-shot classification, used only for classifying migration motivations. Validation against human-coded labels across three LLMs (GPT-4.1, Claude-3.5-Sonnet, and DeepSeek-V3) showed that GPT-4.1 achieved the highest agreement (mean κ = 0.566), with substantial agreement on push factors, moderate agreement on pull and enabling factors, and fair agreement on constraining factors. Applying this approach to 56,242 international migration posts, we found that enabling factors (individual-level resources) appeared most frequently (77.3%), followed by constraining factors (49.3%), pull factors (37.2%), and push factors (23.6%). For each method, we provide practical guidance on implementation, data access, and validation, and reflect on methodological limitations and ethical considerations.
A bibliometric and topic-based analysis of 7,120 Arabic NLP papers published between 1960 and 2026, sourced from five platforms plus an additional targeted OpenAlex subset, and offers recommendations to prioritize under-resourced dialects and develop culturally aligned benchmarks.
With the continuous iteration of Large Language Model (LLM) technology, it has slowly become an important tool in social media data analysis. However, social media data has its own characteristics, such as strong chronological order, content is easy to change dynamically, topics will drift from time to time, and there are some troubles such as streaming updates. Traditional and static large language models are often difficult to adapt to these characteristics, so they will encounter many difficulties when they are actually implemented. This study takes the analysis of large language models and chronological social media data together. It adopts a systematic literature review method to systematically sort out the literature published in some authoritative domestic journals from 2024 to 2025, hoping to extract some cutting-edge technologies from it. Path and research results. From these documents, it can be seen that much of the current work is carried out around the core tasks of chronological understanding, trend prediction, semantic drift and public opinion evolution, and has slowly formed some technical schemes such as chronological incremental adaptation, dynamic fine-tuning, and chronological knowledge injection. These methods have indeed helped us alleviate some old problems, such as catastrophic forgetting and semantic characterization obsolescence.
Semantic analysis has become a central challenge in natural language processing, driven by exponential growth in digitized textual data and the need for automated content processing across multiple applications including machine translation, text classification, sentiment analysis, and information retrieval. However, while semantic analysis methods are well-developed for resource-rich languages such as English, morphologically complex languages like Uzbek suffer from deficiencies in annotated corpora, lexical-semantic resources, and high-quality vector models – a gap amplified by governmental initiatives in digital economy development and national language technology advancement. This section grounds semantic analysis in the distributional semantics hypothesis principle that words exhibiting similar contexts possess similar meanings – thereby recasting the problem as a geometric challenge within continuous vector spaces. Two principal mathematical strategies are formalized: (1) prediction-based models (word2vec: CBOW/Skip-gram), which optimize context prediction objectives, and (2) count-based models (GloVe), which leverage global co-occurrence statistics through matrix factorization. Both project high-dimensional word co-occurrence relationships into low-dimensional dense vector spaces, enabling semantic analogy representation. For resource-scarce languages like Uzbek, cross-lingual embedding alignment (Procrustes optimization) enables semantic knowledge transfer from resource-rich languages, facilitating shared semantic spaces across the Turkic language family. The section concludes with formal problem specification: given vocabulary V and corpus C, semantic analysis is formalized as (1) a mapping problem preserving distributional properties, (2) an optimization problem minimizing loss through gradient-based methods, and (3) an evaluation problem assessing quality through semantic similarity, analogy, and downstream NLP task performance.
D. Akhmedjanova· Международный Журнал Теорети...· 0 citations
This paper analyzes metadata from Ecuador's SOCE, with particular emphasis on participant comments generated during the pre-contractual phase to propose a hybrid modeling framework that integrates unsupervised clustering and supervised classification within a natural language processing (NLP) pipeline to uncover latent patterns and detect potentially irregular procurement processes.
Bryan Torres, Daniel Riofrío, J. Vega-Sánchez et al.· 0 citations
In the modern world, hybrid and fully remote work formats are becoming increasingly widespread. The volume of digital communication continues to grow, and the need to exchange documents and information through email, corporate messengers (such as Microsoft Teams), and collaborative workspaces is rising. Personal data is often involved in these exchanges, which increases the risk of unintentional disclosure. To comply with GDPR requirements and ensure information security, it is necessary to implement methods for the automatic detection of personal data. This task is particularly relevant for low-resource languages, such as Latvian, for which existing tools often operate with limited accuracy and efficiency. This work proposes a hybrid approach to the automatic detection of personal data in Latvian texts from Microsoft Teams messages, emails, and documents, combining a transformer-based NER model with rule-based detection of structured identifiers. The approach builds on a multilingual NER model, supplements it with Latvian-specific rules and structured-identifier detectors, and demonstrates the potential of adapted solutions to improve the accuracy and robustness of personal-data detection in real-world workflows.
Henrihs Gorskis, Jūlija Strebko, Jurijs Kornijenko et al.· Future Internet· 0 citations
This study proposes a robust and semantically enriched framework for conversation understanding based on a composite distributed representation that incorporates both temporal adjacency and semantic proximity between utterances, enabling the visualization of key conversational connections.