SpanishMicroEvents

SpanishMicroEvents is the first publicly available multimodal dataset for fine-grained news micro-event detection and retrieval in Spanish. It contains 13,831 news articles from a single Spanish outlet (2024-2025), organized into 1,632 micro-events through a two-phase construction pipeline combining neural topic modelling and hybrid temporal segmentation.

Each article is represented by three modalities:

• Text: multilingual E5-small embeddings (384d)
• Image: CLIP ViT-B/32 embeddings (512d), one per unique MD5 image hash
• Temporal: daily publication-density signal per micro-event

Paper: SpanishMicroEvents: A Multimodal Dataset for Fine-Grained News Event Retrieval in Spanish — Under review.

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Dataset Structure

Files

File	Description
articles.parquet	Main article-level dataset (13,831 × 14 columns)
micro_events.csv	Micro-event summary table (1,632 rows)
embeddings_e5_small.npy	Text embeddings aligned 1:1 with articles.parquet (N × 384)
embeddings_clip_articulos.npy	CLIP image embeddings aligned 1:1 with articles.parquet (N × 512)
validation_sample.csv	Manually annotated quality sample (50 micro-events)

articles.parquet — column reference

Column	Type	Source	Description
id	int	Raw corpus	Unique article identifier
url	str	Raw corpus	Article URL
publication_date	datetime	Raw corpus	Publication timestamp
headline	str	Raw corpus	Article headline
article_body	str	Raw corpus	Full article body text
description	str	Raw corpus	Article description/teaser
image_hash	str	Raw corpus	MD5 hash of the associated image
named_entities	list[dict]	phase0_corpus_enrichment	Named entities extracted with Stanza (fields: text, type; types: PER, ORG, LOC, MISC)
dbpedia_entities_raw	list[dict]	phase0_corpus_enrichment	Raw DBpedia entity linking output before deduplication (fields: text, uri, types)
named_entities_dbpedia	list[dict]	phase0_corpus_enrichment	Deduplicated DBpedia-linked entities (fields: text, uri, types)
date_mentions	list[str]	phase0_corpus_enrichment	Raw date mentions detected in the text
extracted_dates	list[str]	phase0_corpus_enrichment	Normalised date expressions extracted from text
micro_event_id	str	phase2_temporal_segmentation	Micro-event identifier. Format: "{topic_id}" for atomic events, "{topic_id}_{segment}" for split clusters. Value "-1" indicates the article was not assigned to any valid micro-event (singleton or isolated burst).
event_label	str	phase2_temporal_segmentation	JIT label for the micro-event: top-4 BM25-weighted c-TF-IDF terms, deduplicated by Porter stem (e.g. "ábalos_fiscal_koldo_corrupción").

micro_events.csv — column reference

Column	Type	Description
micro_event_id	str	Micro-event identifier (matches articles.parquet)
label	str	JIT event label
start_date	date	First article date
end_date	date	Last article date
n_articles	int	Number of articles in the event
source_cluster_id	int	Phase 1 cluster of origin
route	str	"A" (temporal segmentation) or "B" (atomic, bypass)
duration_days	int	Duration in days (end_date − start_date)

validation_sample.csv — column reference

Column	Type	Description
micro_event_id	str	Micro-event identifier
n_articles	int	Number of articles
duration_days	int	Duration in days
label	str	JIT event label
representative_headline	str	Representative headline
remaining_headlines	str	Remaining headlines (pipe-separated)
annotation	str	Manual annotation: C = Correct, P = Partial, N = Non-event
notes	str	Free-text annotation notes

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Key Statistics

Statistic	Value
Total articles	13,831
Articles in valid micro-events	4,418 (31.9%)
Total micro-events	1,632
Articles / micro-event (mean)	2.71
Articles / micro-event (median)	2
Median event duration (Phase 1 clusters)	190 days
Median event duration (Phase 2 micro-events)	3 days
Duration reduction factor	63×
Articles with CLIP embedding	12,713 (91.9%)
Unique image hashes	12,789
Time span	2024-2025
Language	Spanish

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Construction Pipeline

The dataset was built through a five-stage pipeline, each corresponding to a Jupyter notebook. All notebooks are written in English, except phase0_corpus_enrichment which was developed as a prior preprocessing step and is provided in Spanish for reproducibility.

Phase 0 — Corpus Enrichment (phase0_corpus_enrichment.ipynb)

Ingestion and enrichment of the raw corpus: text normalisation, NER with Stanza (→ named_entities), temporal expression extraction (→ date_mentions, extracted_dates), and DBpedia entity linking (→ named_entities_dbpedia).

Phase 1 — Semantic Micro-Clustering (phase1_semantic_clustering.ipynb)

BERTopic with HDBSCAN (min_cluster_size=6) over multilingual E5-small embeddings projected to 5d via UMAP. Noise articles rescued via Ward agglomerative clustering. Produces 3,577 labelled semantic clusters.

Quality metrics: $C_v = 0.62$, Silhouette (raw embeddings) $= 0.006$, intra/inter distance ratio $= 0.637$.

Phase 2 — Hybrid Temporal Segmentation (phase2_temporal_segmentation.ipynb)

Dual routing (Route A: ≥4 articles → temporal segmentation; Route B: <4 → atomic). Gaussian smoothing (σ=0.75) + peak detection. Triple-criterion split validator: temporal gap ≥30 days (70.5% of splits), residual cosine dissimilarity (29.5%), named-entity Jaccard=0 (~0%). Produces 1,632 micro-events and the micro_event_id / event_label columns.

Phase 3 — CLIP Embeddings (phase3_clip_embeddings.ipynb)

CLIP ViT-B/32 embeddings computed once per unique MD5 image hash and broadcast to all articles sharing that hash. 12,713/13,831 articles have a valid embedding; the remaining 1,118 receive a zero vector and are excluded from image-only evaluations.

Baseline Retrieval (baseline_retrieval.ipynb)

Reproduces BM25, text-only, image-only, concat, and late-fusion retrieval baselines reported in the paper. Evaluation protocol: 500 sampled queries, MAP@5 and MAP@10.

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Reproducing the Pipeline

The notebooks/ folder contains the five pipeline notebooks. The src/ folder contains the supporting Python modules imported by the notebooks.

Recommended environment: Google Colab (notebooks mount Google Drive automatically). To run locally, update the DATA_DIR path in Cell 1 of each notebook.

Dependencies (installed automatically by the notebooks via !pip install):

bertopic>=0.16
sentence-transformers>=2.2
gensim>=4.3
umap-learn>=0.5
hdbscan>=0.8
scipy>=1.10
scikit-learn>=1.2
rank-bm25>=0.2
open-clip-torch>=2.20
stanza>=1.6

Execution order: fase0 → fase1 → fase2 → fase3 → baseline_retrieval. fase0 only needs to be run once to generate the enriched corpus; the remaining notebooks load pre-computed artifacts from Drive.

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Loading the Dataset

import pandas as pd
import numpy as np

# Article-level data
articles = pd.read_parquet("articles.parquet")

# Micro-event summary
events = pd.read_csv("micro_events.csv")

# Text embeddings — aligned 1:1 with articles (same row order)
text_emb = np.load("embeddings_e5_small.npy")        # shape: (13831, 384)

# Image embeddings — aligned 1:1 with articles (same row order)
clip_emb = np.load("embeddings_clip_articulos.npy")  # shape: (13831, 512)

# Filter only articles in valid micro-events
valid = articles[articles["micro_event_id"] != "-1"]  # 4,418 articles

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Retrieval Baseline Results

System	P@5	R@5	MAP@5	MAP@10
Random	.0002	.0002	.0002	.0002
BM25	.1544	.3555	.2645	.2796
Text-only (E5-small)	.0224	.0416	.0299	.0297
Image-only (CLIP)	.0312	.0678	.0429	.0443
Concat (E5 + CLIP)	.0372	.0710	.0485	.0514
Late fusion	.0376	.0713	.0481	.0508

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Manual Validation

A random sample of 50 micro-events was manually annotated:

Label	Count	%
Correct	13	26%
Partial	3	6%
Non-event	34	68%

The 68% non-event rate reflects the single-outlet nature of the corpus: recurring sections (weather, gastronomy, opinion columns, historical essays) are correctly grouped by topic but do not constitute discrete news events. The true clustering error rate (articles from distinct events merged) is only 14%.

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Citation

Under review.

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License

This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.