NLP-POL/instagram-political-communication-it-embeddings

Instagram Political Communication (Italy) — Embeddings

This dataset is the companion embeddings dataset of `instagram-political-communication-it`, released as part of the NLP-POL (NLP for Political Communication) project.

It provides vector representations (embeddings) for Instagram posts, comments, sentences, and keyphrases related to the political communication of Italian politicians.

The dataset is designed to support research on:

• —semantic analysis of political language
• —representation learning in political discourse
• —similarity, clustering, and retrieval tasks
• —downstream NLP experiments built on top of the NLP-POL core dataset

Relationship to the Core Dataset

This dataset does not contain raw text or metadata. Instead, it provides embeddings aligned via stable identifiers to the core dataset:

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

All records reference entities in the core dataset using:

• —post__id
• —comment__id

Example Usage

The following example shows how to load post table from the core dataset, load post embeddings from this companion embeddings dataset, and perform explicit joins using stable identifiers.

This approach ensures transparency, reproducibility, and full control over relational operations.

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()

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

join_df = posts_df.merge(
    post_embeddings_df,
    left_on='_id',
    right_on='post__id',
    how='inner',
    suffixes=('_post', '_embedding')
)

display(join_df.head())

Dataset Structure

The dataset is released as a multi-table relational dataset with flat schemas.

Tables

Data Fields Overview

Post Caption Embeddings (post_caption_embeddings)

Post Sentence Embeddings (post_sentence_embeddings)

Comment Embeddings (comment_embeddings)

Keyphrase Embeddings

Post keyphrases (`post_keyphrase_embeddings`)

Comment keyphrases (`comment_keyphrase_embeddings`)

Embedding Generation

Embeddings are generated as part of the NLP-POL preprocessing pipeline after text normalization and linguistic analysis.

Key characteristics:

• —Fixed-size dense vectors
• —Sentence-level and document-level representations
• —Generated consistently across dataset versions

The specific embedding model used is recorded in the embedding_model field to support reproducibility and model comparison.

Intended Use and Limitations

Intended use: semantic analysis, similarity search, clustering, representation learning, downstream NLP tasks in political communication research.

Limitations:

• —Embeddings inherit biases from the underlying language models
• —Semantic representations depend on preprocessing choices and model selection
• —This dataset should always be used together with the core dataset for interpretation

License

Released under Creative Commons Attribution 4.0 (CC-BY 4.0).

Citation

If you use this dataset, please cite the core dataset:

@dataset{nlp_pol_instagram_political_communication_it_2026,
  title     = {NLP-POL: Instagram Political Communication (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}}
}