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Applied Machine Learning Days 2022 Repository for the Workshop "Visual Disinformation and the Dark Side of Internet Memes"

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Applied Machine Learning Days EPFL 2022 - Workshop

armasuisse S+T

Visual Disinformation and the Dark Side of Internet Memes

This repository contains code and data for the workshop "Visual Disinformation and the Dark Side of Internet Memes" at the Applied Machine Learning Days EPFL 2022 (Workshop Link).

Workshop Part 1

Click on the following badge to open the notebook in Google Colab (recommended):

Open In Colab

Workshop Part 2

Click on the following badge to open the notebook in Google Colab (recommended):

Open In Colab

Local Installation (Optional)

If you want to run the code locally, follow the instructions below to setup your environment.

Workshop Part 1

Clone the repository and install dependencies.

  1. Clone the repository or download the notebook
  2. Install dependencies
pip install -r requirements_part1.txt
  1. set global variable DATA_ROOT_PATH to any directory (via notebook)

Workshop Part 2

Clone the repository and install dependencies. Warning: In this version you will see the solutions to some exercises (cell-hiding is a Colab feature).

  1. Clone the repository. To get the data you need Git lfs while cloning the repository. Alternatively, you can download the data from this Link

(Optional) install git-lfs:

apt-get update
apt-get install git-lfs

Clone the repository:

git clone https://github.com/i4Ds/AMLD-2022-Visual-Disinformation.git
cd AMLD-2022-Visual-Disinformation
  1. Install the dependencies
pip install -r requirements_part2.txt
  1. Prepare the data (if not cloned via git-lfs)

Place the data into your preferred directory (default is ./data/) and unpack.

tar -xf ./data/GRU_202012.tar.gz --directory ./data/
  1. Open Notebook: In the notebook you can skip the data-fetching / unpacking steps.

Workshop Organizers

Raphael Meier, Scientific Project Manager, armasuisse S+T

Marco Willi, Research Associate, FHNW

Michael Graber, Professor, FHNW

Supported By

Armasuisse S+T

armasuisse S+T

FHNW - University of Applied Sciences and Arts Northwestern Switzerland

FHNW

Cyber Defence Campus

Cyber Defense Campus

Data Sources & References

Data

Data for Part 1 are from:

Data for Part 2 are from:

References

Radford, Alec, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, et al. “Learning Transferable Visual Models From Natural Language Supervision.” ArXiv:2103.00020 [Cs], February 26, 2021. http://arxiv.org/abs/2103.00020.

Kiela, Douwe, Hamed Firooz, Aravind Mohan, Vedanuj Goswami, Amanpreet Singh, Pratik Ringshia, and Davide Testuggine. “The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes.” ArXiv:2005.04790 [Cs], April 7, 2021. http://arxiv.org/abs/2005.04790.

Suryawanshi, Shardul, Bharathi Raja Chakravarthi, Mihael Arcan, and Paul Buitelaar. “Multimodal Meme Dataset (MultiOFF) for Identifying Offensive Content in Image and Text.” In Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying, 32–41. Marseille, France: European Language Resources Association (ELRA), 2020. https://aclanthology.org/2020.trac-1.6.

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Applied Machine Learning Days 2022 Repository for the Workshop "Visual Disinformation and the Dark Side of Internet Memes"

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