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.

We describe our participation in all the subtasks of the Germeval 2021 shared task on the identification of Toxic, Engaging, and Fact-Claiming Comments. Our system is an ensemble of state-of-the-art pre-trained models finetuned with carefully engineered features. We show that feature engineering and data augmentation can be helpful when the training data is sparse. We achieve an F1 score of 66.87, 68.93, and 73.91 in Toxic, Engaging, and Fact-Claiming comment identification subtasks.