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Datasets:
electricsheepafrica
/
nigerian_retail_and_ecommerce_social_media_interaction_logs

Tasks:
Text Classification
Tabular Classification
Modalities:
Tabular
Text
Formats:
parquet
Languages:
English
Size:
100K - 1M
Tags:
retail
ecommerce
nigeria
synthetic-data
marketing-analytics
campaigns
Libraries:
Datasets
pandas
License:
Dataset card Data Studio Files
xet
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metadata
license: gpl
task_categories:
  - text-classification
  - tabular-classification
language:
  - en
tags:
  - retail
  - ecommerce
  - nigeria
  - synthetic-data
  - marketing-analytics
  - campaigns
size_categories:
  - 100K<n<1M
pretty_name: Social Media Interaction Logs

Social Media Interaction Logs

Dataset Description

Comprehensive social media interaction logs for Nigerian retail and e-commerce analysis

Dataset Information

  • Category: Marketing and Engagement
  • Industry: Retail & E-Commerce
  • Country: Nigeria
  • Format: CSV, Parquet
  • Rows: 400,000
  • Columns: 11
  • Date Generated: 2025-10-06
  • Location: data/social_media_interaction_logs/
  • License: GPL

Schema

Column Type Sample Values
post_id String POST0000000
platform String Instagram
post_date String 2024-05-25
post_type String announcement
content String Better point drive free price.
likes Integer 1179
comments Integer 479
shares Integer 378
reach Integer 37051
engagement_rate Float 0.0709
sentiment String positive

Sample Data 

    post_id  platform  post_date    post_type                                content  likes  comments  shares  reach  engagement_rate sentiment
POST0000000 Instagram 2024-05-25 announcement         Better point drive free price.   1179       479     378  37051           0.0709  positive
POST0000001   Twitter 2024-06-25 announcement                  Degree sense company.    886       976      80   2623           0.1004  positive
POST0000002  LinkedIn 2024-07-20    promotion Remain too war later mouth one either.   5399       498     500  42936           0.1322  positive

Use Cases

  • Data analysis and insights
  • Machine learning model training
  • Business intelligence
  • Research and education
  • Predictive analytics

Nigerian Context

This dataset incorporates authentic Nigerian retail and e-commerce characteristics:

E-Commerce Platforms

  • Jumia (35% market share) - Leading marketplace
  • Konga (25% market share) - Major competitor
  • Jiji (20% market share) - Classifieds platform
  • PayPorte, Slot, and other platforms

Physical Retail

  • Shoprite, Spar, Game - Major supermarket chains
  • Slot, Pointek - Electronics retailers
  • Mr Price - Fashion retail
  • Traditional markets: Balogun Market, Computer Village

Payment Methods

  • Cash on Delivery (45%) - Most popular
  • Bank Transfer (25%)
  • Debit Card (15%)
  • USSD (8%)
  • Mobile Money (5%)
  • Credit Card (2%)

Logistics & Delivery

  • GIG Logistics - Nationwide coverage
  • Kwik Delivery - Fast urban delivery
  • DHL, FedEx - International and express
  • Red Star Express - Nationwide courier
  • Local dispatch riders

Geographic Coverage

Major Nigerian cities including:

  • Lagos - Commercial capital, highest retail density
  • Abuja - Federal capital, high e-commerce penetration
  • Kano - Northern commercial hub
  • Port Harcourt - Oil city, strong purchasing power
  • Ibadan - Large urban market
  • Plus 10+ other major cities

Products & Categories

  • Electronics: Tecno, Infinix, Samsung phones; laptops, TVs
  • Fashion: Ankara fabric, Agbada, Kaftan, sneakers
  • Groceries: Rice (50kg bags), Garri, Palm Oil, Indomie
  • Beauty: Shea butter, Black soap, hair extensions
  • Home: Generators, inverters, solar panels

Currency & Pricing

  • Currency: Nigerian Naira (NGN, ₦)
  • Exchange Rate: ~₦1,500/USD
  • Price Ranges: Realistic Nigerian market prices
  • Time Zone: West Africa Time (WAT, UTC+1)

File Formats

CSV 

data/social_media_interaction_logs/nigerian_retail_and_ecommerce_social_media_interaction_logs.csv

Parquet (Recommended) 

data/social_media_interaction_logs/nigerian_retail_and_ecommerce_social_media_interaction_logs.parquet

Nigerian Retail and E-Commerce - Loading the Dataset

Hugging Face Datasets 

from datasets import load_dataset

# Load dataset
dataset = load_dataset("electricsheepafrica/nigerian_retail_and_ecommerce_social_media_interaction_logs")

# Convert to pandas
df = dataset['train'].to_pandas()

print(f"Loaded {len(df):,} rows")

Pandas (Direct) 

import pandas as pd

# Load CSV
df = pd.read_csv('data/social_media_interaction_logs/nigerian_retail_and_ecommerce_social_media_interaction_logs.csv')

# Load Parquet (recommended for large datasets)
df = pd.read_parquet('data/social_media_interaction_logs/nigerian_retail_and_ecommerce_social_media_interaction_logs.parquet')

PyArrow 

import pyarrow.parquet as pq

# Load Parquet
table = pq.read_table('data/social_media_interaction_logs/nigerian_retail_and_ecommerce_social_media_interaction_logs.parquet')
df = table.to_pandas()

Data Quality

  • ✅ Realistic Distributions: Based on Nigerian retail patterns
  • ✅ No Missing Critical Fields: Complete core data
  • ✅ Proper Data Types: Appropriate types for each column
  • ✅ Consistent Naming: Clear, descriptive column names
  • ✅ Nigerian Context: Authentic local characteristics
  • ✅ Production Scale: Suitable for real-world applications

Ethical Considerations

  • This is synthetic data generated for research and development
  • No real customer data or personally identifiable information
  • Designed to reflect realistic patterns without privacy concerns
  • Safe for public use, testing, and education

License

GPL License - General Public License

This dataset is free to use for:

  • Research and academic purposes
  • Commercial applications
  • Educational projects
  • Open source development

Citation 

@dataset{nigerian_retail_social_media_interaction_logs_2025,
  title={Social Media Interaction Logs},
  author={Electric Sheep Africa},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/datasets/electricsheepafrica/nigerian-retail-social-media-interaction-logs}}
}

Related Datasets

This dataset is part of the Nigerian Retail & E-Commerce Datasets collection, which includes 42 datasets covering:

  • Customer & Shopper Data
  • Sales & Transactions
  • Product & Inventory
  • Marketing & Engagement
  • Operations & Workforce
  • Pricing & Revenue
  • Customer Support
  • Emerging & Advanced Technologies

Browse all datasets: https://huggingface.co/electricsheepafrica

Updates & Maintenance

  • Version: 1.0
  • Last Updated: 2025-10-06
  • Maintenance: Active
  • Issues: Report via Hugging Face discussions

Contact

For questions, feedback, or collaboration:

  • Hugging Face: electricsheepafrica
  • Issues: Open a discussion on the dataset page
  • General Inquiries: Via Hugging Face profile

Part of the Nigerian Industry Datasets Initiative
Building comprehensive, authentic datasets for African markets.