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The Algorithmic Assembly Line

Abstract

Gig economy platforms like Uber, DoorDash, and Upwork have transformed the landscape of labor, yet they do so through opaque algorithmic structures. This study uses text mining, topic modeling, and sentiment analysis to create a computational typology of gig platforms based on their degree of labor control, surveillance, and worker autonomy. By combining platform Terms of Service (TOS) documents and worker narratives from Reddit, we reveal structural differences in control regimes and their sociological implications for the future of work.

Introduction

Contemporary platform work is managed through what Christin (2020) and Dubal (2017) have called “algorithmic management” — systems that automate supervision, evaluation, and discipline. However, platforms differ significantly in their governance of labor, from rigid rideshare systems to loosely structured marketplaces.

This project asks: Can we measure those differences computationally? And what sociological insights can that measurement produce?

Research Questions

  1. How do gig platforms differ in their algorithmic control mechanisms?
  2. Can we build a typology of platform work that goes beyond industry labels?
  3. What kinds of worker experiences cluster with different control regimes?

Literature Review

Prior ethnographic and legal scholarship (Dubal 2020; Rosenblat 2018; Vallas 2022) has highlighted the exploitative potentials of algorithmic management. Yet much of this literature focuses on a few high-profile platforms and uses deep but narrow data (e.g., interviews, litigation).

This study extends the discussion by offering a cross-platform, computational comparison rooted in publicly available documents and narratives — a social data science approach that provides new scale and structure.

Methods

We employed a three-part analytic pipeline:

1. Document Mining of Terms of Service

  • Scraped TOS documents from 40+ gig economy platforms.
  • Cleaned and tokenized text using spaCy.
  • Applied TF-IDF and K-means clustering to classify control language.

2. Narrative Analysis from Reddit

  • Collected 5,000 posts from r/UberDrivers, r/DoorDash, r/Freelance, and r/overemployed.
  • Used BERTopic for topic modeling.
  • Sentiment scored using VADER to track affective trends.

3. Platform Typology Construction

  • Merged TOS and narrative scores.
  • Created an ordinal “Control Score” index based on surveillance terms, deactivation clauses, and worker autonomy language.

```python

Load TOS data

import pandas as pd from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.cluster import KMeans

df = pd.read_csv(“platform_tos_texts.csv”) vectorizer = TfidfVectorizer(max_df=0.8, min_df=5, stop_words=’english’) X = vectorizer.fit_transform(df[‘clean_text’])

K-means clustering

kmeans = KMeans(n_clusters=3, random_state=42) df[‘cluster’] = kmeans.fit_predict(X)

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