avihayamor/social-instagram-marketing

Social — Instagram Marketing Multimodal Dataset

A synthetic, multimodal dataset for Social, an AI Instagram-marketing agent. Every row is a single Instagram post idea that pairs a marketing caption with a matching AI-generated image, conditioned on a business brief and brand preferences.

Agent pattern: owner brief + brand preferences → 3 similar successful posts (retrieval / recommendation) + 1 freshly generated post (caption + image).

Rows (total)	1,447
Multimodal pairs (rows with a real image)	1,000
Modalities	text (caption, hashtags, CTA) + image (PNG)
Language	English
License	CC-BY-4.0
Status	100% synthetic — no scraping, no personal data

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Sample generated posts

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Quick start

from datasets import load_dataset

ds = load_dataset("avihayamor/social-instagram-marketing", split="train")
print(ds[0]["caption"], ds[0]["hashtags"])

Images live in the images/ folder and are referenced by each row's image_path.

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How it was built

The dataset is generated end-to-end — nothing is scraped or hand-written.

• Captions — produced with Qwen/Qwen2.5-7B-Instruct through the Hugging Face serverless Inference API, in small JSON batches (temperature 0.9), then parsed, validated, and de-duplicated.
• Images — produced with black-forest-labs/FLUX.1-schnell (text-to-image) from each row's image_prompt.
• Diversity — a seed grid of 30 business types × 8 brand voices × 8 palettes × 20 occasions × 2 formats is randomly sampled so combinations stay varied.
• Robustness — generation is fully resumable via on-disk checkpoints, with retry/backoff on API errors.

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Schema

column	description
business_type	type of business (e.g. coffee shop, yoga studio)
brand_voice	tone of voice (e.g. playful and witty)
brand_palette	color palette (e.g. pastel and soft)
platform	always "Instagram"
format	"feed post" or "story"
occasion	marketing occasion (e.g. grand opening)
caption	generated post caption
hashtags	space-separated hashtags
cta	short call-to-action
image_prompt	text-to-image prompt used to render the photo
image_path	relative path to the generated PNG (in images/)
has_image	whether a real image was generated for this row

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Dataset statistics (EDA)

• Rows total: 1,447
• Rows with a real image (multimodal pairs): 1,000
• Diversity: 30 business types · 8 brand voices · 8 palettes · 20 occasions
• Format split: feed post 726 · story 721
• Caption length: mean 9.8 words (min 3, max 25)
• Hashtags per post: mean 4.0

Visual EDA

EDA findings & model-failure flags

Model failures are flagged transparently rather than silently cleaned:

• Exact duplicate captions: 0
• Near-duplicate captions (normalized): 0
• Empty / very short captions (< 4 words): 5
• Very long captions (> 60 words): 0
• Posts with no hashtags: 0
• Captions that leaked a # into the caption body (the model was asked to keep hashtags out of the caption text): 322 — the main observed instruction-following slip, logged here for transparency.

Overall the generator followed instructions well: JSON parse failures were retried, duplicates were dropped during generation, and missing required fields were ~0.

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Files

• social_dataset.csv / social_dataset.parquet — the tabular dataset
• images/ — 1,000 generated PNG images referenced by image_path
• eda/ — exploratory data analysis charts

Intended use & limitations

Built as a course project. The content is fully synthetic marketing material: no real brands, no personal data, and no harmful or adult domains. Captions and images are AI-generated and may contain factual or stylistic mistakes, so they should not be treated as ground-truth marketing copy.