Christian Pipal

Some social media influencers (SMIs) occasionally discuss political actors and events, while others regularly engage with political topics. We ask here whether audience response helps explain this difference. We argue that engagement with political posts provides feedback that can reinforce further political posting and, over time, a move toward formal-political content. We test this argument using 761,102 Instagram posts from 1,683 Austrian and German SMIs collected over 60 months (2021–2025), combining a validated two-stage text classification pipeline with within-creator fixed-effects regressions and Cox proportional-hazards models. Political posts receive 18% more likes and 28% more comments than non-political posts by the same creator, although this boost is not larger for smaller creators. A better-performing first political post is followed sooner by another political post. Stronger engagement with lifestyle-political posts also predicts an earlier move into formal-political content, with implications for democracy given the increasing prominence of SMIs as agenda-setters in digital ecosystems. Smaller creators post political content at higher rates. Audience response therefore helps explain why political communication by SMIs persists and how it can move toward formal politics.

Large language models (LLMs) are increasingly being used for text classification across the social sciences, yet researchers overwhelmingly classify one text per variable per prompt. Coding 100,000 texts on four variables requires 400,000 API calls. Batching 25 items and stacking all variables into a single prompt reduces this to 4,000 calls, cutting token costs by over 80%. Whether this degrades coding quality is unknown. We tested eight production LLMs from four providers on 3,962 expert-coded tweets across four tasks, varying batch size from 1 to 1,000 items and stacking up to 25 coding dimensions per prompt. Six of eight models maintained accuracy within 2 pp of the single-item baseline through batch sizes of 100. Variable stacking with up to 10 dimensions produced results comparable to single-variable coding, with degradation driven by task complexity rather than prompt length. Within this safe operating range, the measurement error from batching and stacking is smaller than typical inter-coder disagreement in the ground-truth data.

It is becoming increasingly common to encounter AI-generated text online. Yet at the same time it is often difficult to distinguish authentic text from generative outputs, particularly within shorter posts. Given this conflict, we expect accusations that a text was AI-generated to become a common strategy for deflecting unwelcome arguments. To study the occurrence of these accusations and their impact on online discourse, we draw on posts collected across two years and twelve political Reddit communities to track who is accused, whether accused comments are flagged by detection tools as AI more often, and whether these accusations disproportionately disrupt debates. Consistent with research on motivated reasoning, accusers from across the political spectrum targeted comments which opposed their views at higher rates, 72% of the time against 50% for comparable same-thread comments. AI accusations also disproportionately undermined open debate. Compared to other types of hostile dismissals, we find that substantive engagement falls to 29% (from 67%), with threads often turning to the litigation of a post’s authenticity rather than engaging with its substance. These findings illustrate the growing tendency to indict political opponents as inauthentic, which we call the motivated attribution of inauthenticity, while illustrating how the existence of generative AI can harm political conversations through suspicion alone.

While Large Language Models (LLMs) have transformed content analysis, their ability to self-correct to achieve higher agreement with human coders remains contested. Recent evidence suggests LLMs fail to self-correct on reasoning tasks, but it is unclear if this limitation applies to classification, where the goal is maximizing overlap with human ground truth. We investigate iterative self-refinement, a pipeline where a model generates an initial classification, critiques its own output, and generates a final refined prediction. We test this across 14 variables (spanning framing, emotions, styles, politics, and topics) using a smaller, cost-effective model (Gemini 2 Flash) by systematically isolating the effects of codebooks versus few-shot examples. Results demonstrate a clear trade-off: refinement significantly boosts human alignment for complex, low-baseline constructs but degrades performance on simple, high-baseline tasks. Notably, a smaller model using refinement matches the accuracy of state-of-the-art reasoning models (Gemini 3 Flash) on complex tasks at a lower cost. These findings establish boundary conditions for self-correction, showing that external structure and task complexity jointly determine when refinement improves data quality.