Blind Leading the Blind: A Social-Media Analysis of the Tech Industry (2023)

Abstract: 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.

## introduction in the last two decades, online social networks (osns) like facebook, instagram, linkedin, and x have dramatically impacted our lives. by converting our limited offline social capabilities into digital form, osns enable exponential reach, touching all aspects of life. one such area with a colossal impact is our careers. online profiles on the aforesaid platforms are now used to screen and terminate employees. history and activity are examined and scrutinized, and any negativity is marked @xcite . while * these authors contributed equally to this work positive discussions thrive, the other side of the story is stifled. critical discourse on already taboo topics -toxic culture, poor management, and especially fair compensation -are thus completely left out of the picture. strong reasons for honesty gave rise to the platform teamblind (often just shortened to blind), with the first post in 2015. since then, blind has grown to 9m+ employees from 300k+ companies. 1 there are two characteristics of blind that make it valuable. first, blind is entirely anonymous. users are free to post negative and positive reviews of companies, freely listing the pros and cons of their workplace. at the heart of blind lies the discussion section, allowing for productive discussions on any topic whatsoever -be it hr issues, careerrelated, broader industry-related, referrals, layoffs, and more. people are free to comment underneath and provide contrasting opinions -an idea nonexistent in the previous platforms. second, blind has a strict verification check. without a login to the network, a user can only view two posts for free. to participate in the discussions and leave reviews, the user must log in via a work email. this step is strict, and it was (and is) crucial to blind's continued growth. due to a lack of strict checks, another platform with similar motives, glassdoor, got poisoned by the epidemic of fake reviews. 2 blind has invited both supporters and critics, with the former arguing for the wealth and variety of opinions and the latter citing some level of toxicity that anonymity brings. without a doubt, both agree that the platform has reshaped how we discuss careers. hence, a detailed analysis of the blind platform will shed some light on the pulse of the industry. ## blind data blind has three main ways in which users interact with the platform: blind company reviews, blind posts, and company-specific arenas. we examine the first two since they provide broader, acrosscompany analyses. all the data is collected from blind's inception on october 20, 2015, until february 2023 -totaling approximately seven years of data. we release the two following novel datasets using fair principles. 3 we follow strict ethical guidelines by excluding any personally identifiable information, like the names of users or any details that could lead to identification. ## network characterization in this section, we zoom out and gain a broader perspective on the entire blind platform. we look at the platform's interactions, quantifying the drivers of user engagement and how covid-19 has impacted the tech industry. figure 3 : cluster based on user interaction in the usa (pink nodes) and india (green nodes). ## conclusion blind is an anonymous social media platform for professionals, serving the need for the missing negative side of the story in career discussions. in this paper, for the first time, we acquire and conduct large-scale analyses on two novel datasets: 63,477 blind company reviews and 767,224 blind posts, containing seven years of anonymized industry data. user-given company reviews show an abundance of opposing opinions, confirming the unique dual discourse of the platform. we see surprising correlations between metrics across companies, as users talk about work-life balance the most, but we find culture and management as more vital reasons affecting ratings. the views on a post follow the expected gibrat's rule of proportionate growth. uniquely, we see an inversion in the number of likes versus comments received, a symptom of anonymity fostering discussions. exploiting blind's bias towards the tech industry, we conduct a temporal analysis and find mappings from global trends like covid-19, work-from-home, return-tooffice, and layoffs. next, we propose a novel content classification pipeline leveraging the blind datasets to go beyond. we first filter out tech-related content from an ocean of discussions from any social media with a 99.25% accuracy. then, we automatically annotate the tech data with ten finer classes, achieving an accuracy of 78.41%, showing a deeper understanding of the text. finally, we mine the opinions of users to provide an aggregated birds-eye view of the sentiments in the industry, with a 98.29% accuracy. these high-accuracy results demonstrate the high practicality of our novel pipeline.