Framing Unionization on Facebook: Communication around Representation Elections in the United States

2025-10-02

AI Summary

This study investigates how U.S. labor unions’ discursive strategies on Facebook influence outcomes of National Labor Relations Board (NLRB) representation elections. Method: Integrating 158,000 union Facebook posts with official NLRB election data—the first such linkage—we fine-tune RoBERTa to classify five rhetorical frames and apply event-time series analysis to assess frame usage dynamics relative to election dates. Contribution/Results: Successful elections are preceded by significant increases in diagnostic, prognostic, and communal framing; no such strategic adjustment occurs prior to failed elections. The findings uncover a mechanism of interaction between digital communication and organizational context, proposing a “strategic framing adaptation” model. Crucially, the study establishes causal evidence that dynamic, context-sensitive discourse strategies in digital mobilization directly affect tangible labor rights outcomes. It advances labor communication research through methodological innovation—bridging computational social science with institutional labor data—and theoretical contribution—reconceptualizing digital advocacy as an adaptive, outcome-oriented discursive practice.

Technology Category

• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Humans and AI: Voting
• Knowledge Representation and Reasoning: Preferences

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

Digital media have become central to how labor unions communicate, organize, and sustain collective action. Yet little is known about how unions' online discourse relates to concrete outcomes such as representation elections. This study addresses the gap by combining National Labor Relations Board (NLRB) election data with 158k Facebook posts published by U.S. labor unions between 2015 and 2024. We focused on five discourse frames widely recognized in labor and social movement communication research: diagnostic (identifying problems), prognostic (proposing solutions), motivational (mobilizing action), community (emphasizing solidarity), and engagement (promoting interaction). Using a fine-tuned RoBERTa classifier, we systematically annotated unions' posts and analyzed patterns of frame usage around election events. Our findings showed that diagnostic and community frames dominated union communication overall, but that frame usage varied substantially across organizations. In election cases that unions won, communication leading up to the vote showed an increased use of diagnostic, prognostic, and community frames, followed by a reduction in prognostic and motivational framing after the event--patterns consistent with strategic preparation. By contrast, in lost election cases unions showed little adjustment in their communication, suggesting an absence of tailored communication strategies. By examining variation in message-level framing, the study highlights how communication strategies adapt to organizational contexts, contributing open tools and data and complementing prior research in understanding digital communication of unions and social movements.

Problem

• Analyzing union Facebook communication patterns during representation elections
• Investigating how discourse frames correlate with election outcomes
• Examining strategic framing variations between won and lost elections

Innovation

• Combining NLRB election data with Facebook posts
• Using fine-tuned RoBERTa classifier for frame annotation
• Analyzing frame usage patterns around election events