Users on Wikipedia collaborate in a structured and organized manner to publish and update articles on numerous topics, which makes Wikipedia a very rich source of knowledge. English Wikipedia has the most amount of information available (more than 6.7 million articles); however, there are few good informative articles on Wikipedia in Indian languages. Hindi Wikipedia has approximately only 160k articles. The same article in Hindi can be vastly different from its English version and generally contains less information. This poses a problem for native Indian language speakers who are not proficient in English. Therefore, having the same amount of information in Indian Languages will help promote knowledge among those who are not well-versed in English. Publishing the articles manually, like the usual process in Global English Wikipedia, is a timeconsuming process. To get the amount of information in native Indian languages up-to-speed with the amount of information in English, automating the whole article generation process is the best option. In this study, we present a stage-wise approach ranging from Data Collection to Summarization and Translation, and finally ending with Template Creation. This approach ensures the efficient generation of a large amount of content in Hindi Wikipedia in less time. With the help of this study, we were able to successfully generate more than a thousand articles in Hindi Wikipedia with ease.

Online social networks (OSNs) have changed the way we perceive careers. A standard screening process for employees now involves profile checks on LinkedIn, X, and other platforms, with any negative opinions scrutinized. Blind, an anonymous social networking platform, aims to satisfy this growing need for taboo workplace discourse. In this paper, for the first time, we present a large-scale empirical text-based analysis of the Blind platform. We acquire and release two novel datasets: 63k Blind Company Reviews and 767k Blind Posts, containing over seven years of industry data. Using these, we analyze the Blind network, study drivers of engagement, and obtain insights into the last eventful years, preceding, during, and post-COVID-19, accounting for the modern phenomena of work-from-home, return-to-office, and the layoffs surrounding the crisis. Finally, we leverage the unique richness of the Blind content and propose a novel content classification pipeline to automatically retrieve and annotate relevant career and industry content across other platforms. We achieve an accuracy of 99.25% for filtering out relevant content, 78.41% for fine-grained annotation, and 98.29% for opinion mining, demonstrating the high practicality of our software.