зеркало из
https://github.com/ssciwr/AMMICO.git
synced 2025-10-29 13:06:04 +02:00
38 строки
2.3 KiB
Markdown
38 строки
2.3 KiB
Markdown
# Misinformation campaign analysis
|
|
|
|

|
|

|
|

|
|

|
|

|
|
|
|
Extract data from from social media images and texts in disinformation campaigns.
|
|
|
|
**_This project is currently under development!_**
|
|
|
|
Use the pre-processed social media posts (image files) and process to collect information:
|
|
1. Text extraction from the images
|
|
1. Improving the preparation of the text for the data analysis (e.g., text cleaning)
|
|
1. Performing person and face recognition in images, facial expressions recognition, as well as the extraction of any other available individual characteristics (e.g., gender, clothes)
|
|
1. Extraction of other non-human objects in the image
|
|
1. 5-Color analysis of the images
|
|
|
|
This development will serve the fight to combat misinformation, by providing more comprehensive data about its content and techniques.
|
|
The ultimate goal of this project is to develop a computer-assisted toolset to investigate the content of disinformation campaigns worldwide.
|
|
|
|
# Installation
|
|
|
|
The `misinformation` package can be installed using pip: Navigate into your package folder `misinformation/` and execute
|
|
```
|
|
pip install .
|
|
```
|
|
This will install the package and its dependencies locally.
|
|
|
|
# Usage
|
|
|
|
There are sample notebooks in the `misinformation/notebooks` folder for you to explore the package usage:
|
|
1. Facial analysis: Use the notebook `facial_expressions.ipynb` to identify if there are faces on the image, if they are wearing masks, and if they are not wearing masks also the race, gender and dominant emotion.
|
|
1. Object analysis: Use the notebook `ojects_expression.ipynb` to identify certain objects in the image. Currently, the following objects are being identified: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, cell phone.
|
|
|
|
There are further notebooks that are currently of exploratory nature (`colors_expression` to identify certain colors on the image, `get-text-from-image` to extract text that is contained in an image.)
|