Evaluating Cross-Lingual Classification Approaches Enabling Topic Discovery for Multilingual Social Media Data
arXiv:2602.17051v1 Announce Type: new 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 t
arXiv:2602.17051v1 Announce Type: new 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.
Executive Summary
This study investigates the effectiveness of different cross-lingual text classification approaches in enabling topic discovery for multilingual social media data. Using a decade-long dataset of over nine million tweets in four languages, the authors evaluate four approaches to filter relevant content and extract dominant themes within the relevant subsets. The results highlight trade-offs between translation and multilingual approaches, offering actionable insights into optimizing cross-lingual pipelines for large-scale social media analysis. The study contributes to the development of more efficient and effective methods for analyzing global conversations in diverse languages.
Key Points
- ▸ The study explores four approaches to filter relevant content from noisy keyword-based collections: translating annotated data, translating unlabelled data, applying fine-tuned multilingual transformers, and a hybrid strategy.
- ▸ The results highlight key trade-offs between translation and multilingual approaches, including differences in accuracy, computational efficiency, and interpretability.
- ▸ The study demonstrates the feasibility of using topic modeling to extract dominant themes within relevant subsets of multilingual social media data.
Merits
Strength in Addressing a Critical Challenge
The study addresses a critical challenge in natural language processing, namely, the analysis of multilingual social media discourse, which is essential for understanding global conversations.
Demerits
Limitation in Evaluating Approaches
The study evaluates the approaches using a single case study, namely, hydrogen energy, which may limit the generalizability of the findings to other topics and languages.
Expert Commentary
The study makes a significant contribution to the field of natural language processing by investigating the effectiveness of different cross-lingual text classification approaches for topic discovery in multilingual social media data. The findings highlight the importance of considering trade-offs between translation and multilingual approaches, which is crucial for developing efficient and effective methods for analyzing global conversations. The study's use of topic modeling to extract dominant themes within relevant subsets of multilingual social media data is also a notable contribution. However, the study's reliance on a single case study may limit the generalizability of the findings to other topics and languages.
Recommendations
- ✓ Future studies should evaluate the approaches using a diverse set of case studies to increase the generalizability of the findings.
- ✓ Researchers should explore the application of the study's findings to other domains, such as politics, health, and finance, to demonstrate the practical relevance of the study's contributions.
