ALTO: African and Levantine Tech-Facilitated Gender-Based Violence Corpus

⚠️ Content warning: this dataset contains real instances of hate speech, harassment, threats, and other tech-facilitated gender-based violence (TFGBV).

Annotated dataset for tech-facilitated gender-based violence (TFGBV) classification in Levantine Arabic (ar), Swahili (sw), and African French (fr), collected from community-operated tiplines and social media. The dataset was annotated through an active-learning human-in-the-loop pipeline.

Usage

from datasets import load_dataset

alto_ds = load_dataset("meedan/alto")            # all three languages
alto_ds_sw = load_dataset("meedan/alto", "sw")   # single language: "ar", "sw", or "fr"

Dataset Statistics

Splits

The below are the split counts for the train-test split for instances with adjudicated or fully agreed (gold and silver) binary TFGBV labels.

config	train	test	total	TFGBV+ train	TFGBV+ test
ar	467	117	584	206 (44.1%)	52 (44.4%)
fr	418	105	523	181 (43.3%)	46 (43.8%)
sw	508	127	635	193 (38.0%)	48 (37.8%)
all	1393	349	1742	580	146

The train/test split is an 80/20 train_test_split stratified jointly on tier1 × batch (random_state=42; a stratum with a single member is merged into the largest stratum of the same tier1 before splitting), so every annotation round and every tier-1 category is represented proportionally in both splits.

Annotation quality by language

language	gold_agreement	gold_adjudicated	silver	bronze	total
ar	404	46	279	217	946
fr	297	85	182	83	647
sw	364	115	273	284	1036

Schema

column	type	description
text	string	Original post text
english_text	string	English translation of the text
simple_hate	int	Binary hate/toxicity label (0/1) - hard label
gendered_content	int	Whether the content is gendered (0/1) - hard label
tier1	string	Tier-1 TFGBV taxonomy category hard label (non_tfgbv, harassment_and_hate_speech, threats_and_incitement_t_i_of_harm_and_violence, image_based_abuse, doxxing)
tier2	list(string)	Tier-2 subcategories hard labels (up to 2) within the Tier-1 category
tfgbv	int	Binary TFGBV hard label (0/1) derived from gendered_content==1 and tier1 != non_tfgbv, null when no agreement or adjudication
batch	int	Active-learning annotation round (1–6) the item was labelled in
status	string	Annotation status: gold (adjudicated or full agreement), silver (Missing one annotation target agreement from gendered_content, tier1, or tier2)
status_detail	string	Indicates the detail of the status for gold whether adjudicated or full agreement
language	string	ar, sw, or fr
annotators	list(string)	Anonymized codes of the annotators who labelled the item
soft_gendered	list(string)	Per-annotator gendered answer, aligned with annotators
soft_tier1	list(string)	Per-annotator Tier-1 label, aligned with annotators
soft_tier2	list(string)	Per-annotator Tier-2 labels, aligned with annotators

Data Sources

• Sources: Civil society partner-operated tiplines and social media collection based on keywords.
• Annotation: multi-annotator labelling in Label Studio against a two-tier TFGBV taxonomy; items adjudicated by an expert annotator.
• Sampling: annotation batches were selected via an active-learning loop (diversity/uncertainty sampling) rather than at random, so label distributions do not reflect base rates in the wild.

Dataset produced by Meedan and collaborators.