AMMICO - AI Media and Misinformation Content Analysis Tool

This package extracts data from images such as social media posts that contain an image part and a text part. The analysis can generate a very large number of features, depending on the user input. See our paper for a more in-depth description.

This project is currently under development!

Use pre-processed image files such as social media posts with comments and process to collect information:

1. Text extraction from the images
   1. Language detection
   2. Translation into English or other languages
   3. Cleaning of the text, spell-check
   4. Sentiment analysis
   5. Named entity recognition
   6. Topic analysis
2. Content extraction from the images
   1. Textual summary of the image content ("image caption") that can be analyzed further using the above tools
   2. Feature extraction from the images: User inputs query and images are matched to that query (both text and image query)
   3. Question answering
3. Performing person and face recognition in images
   1. Face mask detection
   2. Age, gender and race detection
   3. Emotion recognition
4. Color analysis
   1. Analyse hue and percentage of color on image
5. Multimodal analysis
   1. Find best matches for image content or image similarity
6. Cropping images to remove comments from posts

Installation

The AMMICO package can be installed using pip:

pip install ammico

This will install the package and its dependencies locally. If after installation you get some errors when running some modules, please follow the instructions below.

Compatibility problems solving

Some ammico components require tensorflow (e.g. Emotion detector), some pytorch (e.g. Summary detector). Sometimes there are compatibility problems between these two frameworks. To avoid these problems on your machines, you can prepare proper environment before installing the package (you need conda on your machine):

1. First, install tensorflow (https://www.tensorflow.org/install/pip)

• create a new environment with python and activate it

  conda create -n ammico_env python=3.10

  conda activate ammico_env

• install cudatoolkit from conda-forge

  conda install -c conda-forge cudatoolkit=11.8.0

• install nvidia-cudnn-cu11 from pip

  python -m pip install nvidia-cudnn-cu11==8.6.0.163

• add script that runs when conda environment ammico_env is activated to put the right libraries on your LD_LIBRARY_PATH

  mkdir -p $CONDA_PREFIX/etc/conda/activate.d
  echo 'CUDNN_PATH=$(dirname $(python -c "import nvidia.cudnn;print(nvidia.cudnn.__file__)"))' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
  echo 'export LD_LIBRARY_PATH=$CUDNN_PATH/lib:$CONDA_PREFIX/lib/:$LD_LIBRARY_PATH' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
  source $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
  

• deactivate and re-activate conda environment to call script above

  conda deactivate

  conda activate ammico_env

• install tensorflow

  python -m pip install tensorflow==2.12.1

2. Second, install pytorch

• install pytorch for same cuda version as above

  python -m pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

3. After we prepared right environment we can install the ammico package

• python -m pip install ammico

It is done.

Micromamba

If you are using micromamba you can prepare environment with just one command:

micromamba create --no-channel-priority -c nvidia -c pytorch -c conda-forge -n ammico_env "python=3.10" pytorch torchvision torchaudio pytorch-cuda "tensorflow-gpu<=2.12.3" "numpy<=1.23.4"

Windows

To make pycocotools work on Windows OS you may need to install vs_BuildTools.exe from https://visualstudio.microsoft.com/visual-cpp-build-tools/ and choose following elements:

• Visual Studio extension development
• MSVC v143 - VS 2022 C++ x64/x86 build tools
• Windows 11 SDK for Windows 11 (or Windows 10 SDK for Windows 10)

Be careful, it requires around 7 GB of disk space.

Usage

The main demonstration notebook can be found in the notebooks folder and also on google colab

There are further sample notebooks in the notebooks folder for the more experimental features:

1. Topic analysis: Use the notebook get-text-from-image.ipynb to analyse the topics of the extraced text.
   You can run this notebook on google colab: Here
   Place the data files and google cloud vision API key in your google drive to access the data.
2. Multimodal content: Use the notebook multimodal_search.ipynb to find the best fitting images to an image or text query. You can run this notebook on google colab: Here
3. Color analysis: Use the notebook color_analysis.ipynb to identify colors the image. The colors are then classified into the main named colors in the English language. You can run this notebook on google colab: Here
4. To crop social media posts use the cropposts.ipynb notebook. You can run this notebook on google colab: Here

Features

Text extraction

The text is extracted from the images using google-cloud-vision. For this, you need an API key. Set up your google account following the instructions on the google Vision AI website. You then need to export the location of the API key as an environment variable:

export GOOGLE_APPLICATION_CREDENTIALS="location of your .json"

The extracted text is then stored under the text key (column when exporting a csv).

Googletrans is used to recognize the language automatically and translate into English. The text language and translated text is then stored under the text_language and text_english key (column when exporting a csv).

If you further want to analyse the text, you have to set the analyse_text keyword to True. In doing so, the text is then processed using spacy (tokenized, part-of-speech, lemma, ...). The English text is cleaned from numbers and unrecognized words (text_clean), spelling of the English text is corrected (text_english_correct), and further sentiment and subjectivity analysis are carried out (polarity, subjectivity). The latter two steps are carried out using TextBlob. For more information on the sentiment analysis using TextBlob see here.

The Hugging Face transformers library is used to perform another sentiment analysis, a text summary, and named entity recognition, using the transformers pipeline.

Content extraction

The image content ("caption") is extracted using the LAVIS library. This library enables vision intelligence extraction using several state-of-the-art models, depending on the task. Further, it allows feature extraction from the images, where users can input textual and image queries, and the images in the database are matched to that query (multimodal search). Another option is question answering, where the user inputs a text question and the library finds the images that match the query.

Emotion recognition

Emotion recognition is carried out using the deepface and retinaface libraries. These libraries detect the presence of faces, and their age, gender, emotion and race based on several state-of-the-art models. It is also detected if the person is wearing a face mask - if they are, then no further detection is carried out as the mask prevents an accurate prediction.

Color/hue detection

Color detection is carried out using colorgram.py and colour for the distance metric. The colors can be classified into the main named colors/hues in the English language, that are red, green, blue, yellow, cyan, orange, purple, pink, brown, grey, white, black.

Cropping of posts

Social media posts can automatically be cropped to remove further comments on the page and restrict the textual content to the first comment only.