Astrodetection

Astrodetection is a Python library designed for detecting astroturfing clues from lists of posts (mainly on X up to now, but not exclusively)

Installation

Pip

pip install "astrodetection[standard]"

pip install "astrodetection[light]"

Conda

Use the YAML file to configure the environment with conda:

conda create -n astrodetection_env
conda activate astrodetection_env
conda env update -f environment_standard.yml

Note: the environment_standard.yml configuration file uses FAISS and Fasttext libraries for VIGINUM D3LTA implementation

**If you have compatibility issues, prefer environment_light.yml and use astrodetection_light module

Usage

You can import directly the main functions:

from astrodetection import semantic_faiss, prepare_input_data, compute_bot_likelihood_metrics, create_network

Or use them directly:

import glob
import pandas as pd
import os
import numpy as np
import astrodetection

# Load a single JSON file into a DataFrame
file = "file_path"  # Select the first file
df = pd.read_json(file)
df.index = df.index.astype(str)  # Compatibility with d3lta

# Preprocess the DataFrame
df = df[df['tweet'].str.len() > 100]
df = df[df['username'] != 'grok']
df.index = df.index.astype(str)

# Compute matches and scores
df_filtered, df_emb = astrodetection.prepare_input_data(df, embeddings=df['emb'])

matches, df_cluster = astrodetection.semantic_faiss(
    df_filtered.rename(columns={'tweet': 'original'}),
    min_size_txt=0,
    df_embeddings_use=df_emb,
    threshold_grapheme=0.8,
    threshold_language=0.715,
    threshold_semantic=0.9
) #function taken from D3LTA 

scores = astrodetection.compute_bot_likelihood_metrics(df, matches=matches)

# Create a network
network = astrodetection.create_network(matches, df)

New changes

semantic_faiss function can now take detect only copypastas based on levenshtein distance, ignoring embeddings, if "skip" is passed as argument in df_embeddings_use field.
compute_bot_likelihood_metrics function can now take columns' names as arguments for more customization
compute_bot_likelihood_metrics now returns high_following_followers_ratio (%), the share of rows whose following/followers ratio exceeds fw_ratio_threshold (default 10).

Intended Audience
- Science/Research
Operating System
- OS Independent
Programming Language
- Python :: 3
Topic
- Scientific/Engineering :: Information Analysis

Release files for astrodetection 0.2.8

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for astrodetection 0.2.8
File	Size	Uploaded
astrodetection-0.2.8.tar.gz	54.1 kB	Aug 5, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for astrodetection 0.2.8
File	Interpreter	ABI	Platform	Reset
astrodetection-0.2.8-py3-none-any.whl 55.6 kB Aug 5, 2026	Python 3	none	any	Details

Total release size: 109.7 kB

Release files / astrodetection-0.2.8.tar.gz

Download URL	astrodetection-0.2.8.tar.gz
Size	54.1 kB
Tags	Source
SHA-256 checksum How to use checksums	`548dc88799d1c77d2df83d418bf20c369e188ae2ef939efa48d79981e784abd2`
BLAKE2b-256 checksum How to use checksums	`09e9fe443aa50ea97bcadf0b95983b5db63b31a82eef492a18805562bbd5a899`
Upload date	Aug 5, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.2.0 CPython/3.11.4`

Release files / astrodetection-0.2.8-py3-none-any.whl

Download URL	astrodetection-0.2.8-py3-none-any.whl
Size	55.6 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`78a715852fca77f3d5152c433f844ce48400e16c22070caf400a93339705bc13`
BLAKE2b-256 checksum How to use checksums	`81f2cd0f1889f8212e70ab88e0685b24ae81ed58680a523084e20fd6b91b10e0`
Upload date	Aug 5, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.2.0 CPython/3.11.4`