Evaluating Cross-Lingual Classification Approaches Enabling Topic Discovery for Multilingual Social Media Data

• URL: http://arxiv.org/abs/2602.17051v1
• Date: Thu, 19 Feb 2026 03:46:11 GMT
• Title: Evaluating Cross-Lingual Classification Approaches Enabling Topic Discovery for Multilingual Social Media Data
• Authors: Deepak Uniyal, Md Abul Bashar, Richi Nayak,
• Abstract summary: This study investigates how different approaches for cross-lingual text classification can support reliable analysis of global conversations.<n>Using hydrogen energy as a case study, we analyse a decade-long dataset of over nine million tweets in English, Japanese, Hindi, and Korean.
• Score: 1.0025691625593705
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Analysing multilingual social media discourse remains a major challenge in natural language processing, particularly when large-scale public debates span across diverse languages. This study investigates how different approaches for cross-lingual text classification can support reliable analysis of global conversations. Using hydrogen energy as a case study, we analyse a decade-long dataset of over nine million tweets in English, Japanese, Hindi, and Korean (2013--2022) for topic discovery. The online keyword-driven data collection results in a significant amount of irrelevant content. We explore four approaches to filter relevant content: (1) translating English annotated data into target languages for building language-specific models for each target language, (2) translating unlabelled data appearing from all languages into English for creating a single model based on English annotations, (3) applying English fine-tuned multilingual transformers directly to each target language data, and (4) a hybrid strategy that combines translated annotations with multilingual training. Each approach is evaluated for its ability to filter hydrogen-related tweets from noisy keyword-based collections. Subsequently, topic modeling is performed to extract dominant themes within the relevant subsets. The results highlight key trade-offs between translation and multilingual approaches, offering actionable insights into optimising cross-lingual pipelines for large-scale social media analysis.

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