Abstract:Annotation quality is difficult to sustain when campaigns span weeks or months with small annotator pools. We present a Setswana sentiment dataset of 3,565 tweets annotated by three native-speaker annotators across eight batches and examine why inter-annotator agreement (IAA) declines over time. Despite an aggregate Randolph's free-marginal Kappa of $\kappa = 0.76$, "excellent," per-batch $\kappa$ falls by more than 32 points across the annotation task. Through six targeted analyses, we find that (i) label confusion concentrates on the negative/neutral boundary, (ii) two annotators show run-length drift consistent with autopilot labeling, and (iii) the dominant predictor of $\kappa$ is temporal simultaneity: tweets labeled within one minute achieve $\kappa = 0.98$, while those labeled more than a day apart reach only $\kappa = 0.65$. Annotation speed and tweet-level linguistic features show no meaningful association with $\kappa$. We benchmark three open multilingual encoders and proprietary models (GPT-5 and Gemini) on three-class sentiment classification; fine-tuning yields gains of 29 to 43 macro-F1 points over pretrained baselines, with GPT-5 few-shot leading overall (62.2 macro-F1). We release the dataset, per-annotation timestamps, and analysis code to support reproducible quality auditing for future African language NLP resources.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2605.27239 [cs.CL] |
| (or arXiv:2605.27239v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.27239 arXiv-issued DOI via DataCite (pending registration) |
From: Idris Abdulmumin [view email]
[v1]
Tue, 26 May 2026 16:21:20 UTC (9,222 KB)
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