评估跨语言分类方法以支持多语言社交媒体数据中的主题发现
Evaluating Cross-Lingual Classification Approaches Enabling Topic Discovery for Multilingual Social Media Data
AI总结：
本研究评估了四种跨语言分类方法，以过滤社交媒体数据中的相关推文并发现主题。
摘要：
分析多语言社交媒体 discourse 仍然是自然语言处理中的主要挑战，特别是在大规模公共辩论跨越多种语言时。本研究探讨了不同跨语言文本分类方法如何支持对全球对话的可靠分析。以氢能为例，我们分析了2013至2022年间超过九百万条英文、日文、印地语和韩语推文的数据集，用于主题发现。在线关键词驱动的数据收集产生大量无关内容。我们探索了四种过滤相关内容的方法：（1）将英文标注数据翻译成目标语言，为每种目标语言构建语言特定模型；（2）将所有语言中未标注的数据翻译成英文，创建基于英文标注的单一模型；（3）直接应用经过英文微调的多语言转换器到每种目标语言数据；（4）结合翻译标注与多语言训练的混合策略。每种方法均被评估其过滤与氢能相关的推文的能力。随后，通过主题建模提取相关子集中的主导主题。结果突显了翻译与多语言方法之间的关键权衡，为优化大规模社交媒体分析的跨语言管道提供了可行见解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
