NLP-POL/instagram-political-communication-it

Instagram Political Communication (Italy) — NLP-POL

Dataset Summary

This dataset is part of NLP-POL (NLP for Political Communication), a research project focused on the analysis of political communication strategies through Natural Language Processing.

The dataset contains Instagram posts and comments collected from more than 300 Italian political figures, primarily members of the Italian Parliament (with a strong focus on Deputies). It includes both content published by political actors and public audience reactions expressed through comments.

The dataset is intended to support research in:

• political discourse and framing
• sentiment and emotional tone in political communication
• public reactions to political messaging
• hate speech and moderation-related analysis
• semantic representations of political language

The dataset is actively maintained and periodically updated as new Instagram content is scraped and processed.

Dataset Structure

The dataset is released as a multi-table, relational dataset with flat schemas and stable identifiers.
A normalized design is used to ensure scalability, efficient joins, and reproducibility.

Core Tables (this repository)

Table	Description
profiles	Public political figures
posts	Instagram posts published by political profiles
comments	Public comments under posts
post_sentiment_saliency	Salient sentiment terms extracted from posts
post_hate_speech_saliency	Salient hate-speech-related terms extracted from posts
post_keyphrases	Keyphrases extracted from posts
comment_sentiment_saliency	Salient sentiment terms extracted from comments
comment_hate_speech_saliency	Salient hate-speech-related terms extracted from comments
comment_keyphrases	Keyphrases extracted from comments

🔗 Companion embeddings dataset: instagram-political-communication-it-embeddings — Go to repository

Example Usage

The following example shows how to load multiple tables from the core dataset.

import duckdb
from datasets import load_dataset

q_posts = con.execute("""
    SELECT *
    FROM read_parquet('hf://datasets/NLP-POL/instagram-political-communication-it/data/posts/*.parquet')
    LIMIT 10
""")
posts_df = q_posts.fetch_df()

comments_q = con.execute(f"""
    SELECT *
    FROM read_parquet('hf://datasets/NLP-POL/instagram-political-communication-it/data/comments/*.parquet')
    WHERE post_info__id IN ({', '.join([f"'{_id}'" for _id in posts_df['_id'].tolist()])})
""")
comments_df = comments_q.fetch_df()

display(posts_df.head())
display(comments_df.head())

Data Fields Overview

Profiles (profiles)

Each row represents a public political figure.

Key fields:

• _id: unique profile identifier
• nome: full name
• instagram: Instagram handle
• x: X/Twitter handle (if available)
• partito: political party affiliation
• descriptions: list of public role descriptions
• url_name: normalized URL-friendly name
• instagram_posts_count: number of scraped posts
• dataset_version

Posts (posts)

Each row represents one Instagram post.

Key fields:

• _id: post identifier
• uri: public Instagram URL
• author: Instagram username
• datetime: UTC timestamp of publication
• caption: post caption text
• topics: high-level topic labels
• sentiment scores (sentiment_positive, neutral, negative)
• hate speech scores (acceptable, inappropriate, offensive, violent)
• comments_ids_count
• dataset_version

Comments (comments)

Each row represents a public comment under a post.

Key fields:

• _id: comment identifier
• username: commenting user
• datetime: UTC timestamp
• text: comment text
• likes: number of likes (if available)
• post_info__id: referenced post identifier
• post_info_author: post author username
• post_info_datetime: post publication timestamp
• sentiment and hate speech scores
• dataset_version

Data Collection

Sources

• Public Instagram profiles of Italian political figures
• Publicly available posts and comments only

Data is collected through periodic scraping of publicly accessible content.

Data Processing Pipeline

The dataset is generated through a structured NLP pipeline:

1. Scraping of Instagram content
2. Text normalization and cleaning
3. Topic classification
4. Sentiment analysis
5. Hate speech classification
6. Keyphrase extraction
7. Semantic embedding generation (released separately)

All preprocessing steps are applied consistently across dataset versions.

Embeddings Dataset

Vector representations are released in a separate companion dataset:

instagram-political-communication-it-embeddings

This includes:

• post-level embeddings
• sentence-level embeddings
• comment embeddings
• keyphrase embeddings

This separation enables lighter downloads, independent versioning, and model updates without breaking the core dataset.

Intended Use

Primary Use Cases

• Political communication analysis
• Computational social science
• NLP benchmarking on political language
• Sentiment and hate speech research

Limitations and Biases

• The dataset reflects Instagram usage and engagement patterns
• Audience comments are not representative of the general population
• Automated NLP annotations may introduce bias or errors

Users should assess suitability for their specific research goals.

License

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

Citation

If you use this dataset, please cite:

@dataset{nlp_pol_instagram_political_communication_it_2026,
  title     = {NLP-POL: Instagram Political Communication in Italy},
  author    = {PMG-t and NLP-POL Project},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/PMG-t/instagram-political-communication-it},
  note      = {Maintained by PMG-t. Part of the NLP-POL (NLP for Political Communication) project.},
  howpublished = {\url{https://github.com/PMG-t}}
}