Cryptocurrency has attracted a lot of public attention and opinion worldwide. Users have different kinds of information needs regarding such topics and publicly available information is a good resource to satisfy those information needs. In this paper, we investigate the public opinion on cryptocurrency and bitcoin on two social media – Twitter and Reddit. We have created a multi-level dataset CryptOpiQA and garnered valuable insights. The dataset contains both gold standard (manually annotated) and silver standard (inferred from the gold standard) labels. As a part of this dataset, we have also created a Question Answering sub-corpus. We have used state-of-the-art LLMs and advanced techniques such as retrieval augmented generation (RAG) to improve question-answering (QnA) results. We believe this dataset and the analysis will be useful in studying user opinions and Question-Answering on cryptocurrency in the research community.

Although Wikipedia is the largest multilingual encyclopedia, it remains inherently incomplete. There is a significant disparity in the quality of content between high-resource languages (HRLs, e.g., English) and low-resource languages (LRLs, e.g., Hindi), with many LRL articles lacking adequate information. To bridge these content gaps, we propose a lightweight framework to enhance knowledge equity between English and Hindi. In case the English Wikipedia page is not up-to-date, our framework extracts relevant information from external resources readily available (such as English books), and adapts it to align with Wikipedia’s distinctive style, including its neutral point of view (NPOV) policy, using in-context learning capabilities of large language models. The adapted content is then machine-translated into Hindi for integration into the corresponding Wikipedia articles. On the other hand, if the English version is comprehensive and up-to-date, the framework directly transfers knowledge from English to Hindi. Our framework effectively generates new content for Hindi Wikipedia sections, enhancing Hindi Wikipedia articles respectively by 65% and 62% according to automatic and human judgment-based evaluations.