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HAL
open science

Active Tigger

Axel Morin, Émilien Schultz, Annina Claesson, Arnault Chatelain, Emma

Bonutti D'Agostini, Julien Boelaert, Etienne Ollion

► To cite this version:

Axel Morin, Émilien Schultz, Annina Claesson, Arnault Chatelain, Emma Bonutti D'Agostini, et al. Active Tigger. AI and Society - Exploring Inequality in the Digital Age, May 2025, Mannheim, Germany. 2025. hal-05097192

HAL Id: hal-05097192

https://cnrs.hal.science/hal-05097192v1

Submitted on 4 Jun 2025

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Distributed under a Creative Commons Attribution 4.0 International License
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# Active Tigger
AN INTUITIVE & COLLABORATIVE
TEXT ANNOTATION TOOL FOR
OPEN SOCIAL SCIENCE RESEARCH

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Contact and Information!

## Context

Across social sciences, researchers are increasingly interested in skyrocketing amounts of textual data. Even as computer scientists have developed useful tools for analysing textual data, these often place high demand on technical mastery. Widening access to these research methods requires evidence-based, inclusive and transparent tools.

## Goals

* Facilitate **collaborative** human annotation and **exploration** of textual datasets
* Use AI to carry out **text classification** and information extraction
* **Democratise** computational social sciences practices for other communities
* Create a lightweight, frugal and **open source** application

## Use case

### How prevalent is gender in French social sciences?

To answer this question we collected 50,000 journal article abstracts from French publications. Each needed to be annotated: "mentioning gender" (in all its different dimensions) or not.

Using Active Tigger helped in many ways:

* The exploration tools eased the stabilisation of a codebook
* The collaborative annotation helped coordinate the process
* After annotating only about 500 abstracts (1% of the dataset), we trained a classifier that performs as well as an expert (F1>0.9)
* We used the model to predict the labels of each abstract

We are actively looking for more use cases. Contact us!

## Workflow

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## How to use:

* From a user-friendly web app or a Python package
* Connect to an existing instance running on a server
* Run own your API (docker and code available in the repository)

## How was it made:

* State of the art Python libraries (PyTorch, Hugging Face)
* Built to leverage acceleration of GPUs
* Open source development for transparency and reproducibility

## Where to start:

* Read the *Quick Start* and documentation
* Visit the GitHub repository
* Test the app with a testing account

## And more to come!

Where we are now
* The first stable version will be released in June 2025

Next steps
* Generative features
* Enhanced data exploration with topic analysis
* Better collaboration management options

**You want to contribute? Brilliant!**

Please reach out!

## Glossary

### Classifier / Classification

* In machine learning, "classification" refers to a task consisting of labelling texts with predefined labels. In the context of Active Tigger, classifiers are used in the annotation automation.

**BERT models (Bidirectional Encoder Representations from Transformers)**
* Pre-trained models based on the Transformer architecture. These are trained to fill gaps in a sentence on large text datasets.

**Generative LLMs (ex: GPT, Gemini, Llama...)**
* Pre-trained models based on the Transformer architecture. These are trained to predict the next token on gigantic text datasets.

**GPU (Graphics Processing Unit)**

* Computer components designed to accelerate parallel computation. They are broadly used for using and training AI models

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CENTER FOR RESEARCH
IN ECONOMICS AND STATISTICS

**Authors:** Axel Morin, Émilien Schultz, Annina Claesson, Arnault Chatelain, Emma Bonutti, Julien Boelaert, Étienne Ollion.

ActiveTigger is developed in the CREST laboratory / CSS@IPP with the help of OuestWare, and funded by DRARI Île-de-France and Progédo