{"id":19120254,"url":"https://github.com/i4ds/amld-2022-visual-disinformation","last_synced_at":"2025-07-17T20:33:05.086Z","repository":{"id":85538258,"uuid":"466296549","full_name":"i4Ds/AMLD-2022-Visual-Disinformation","owner":"i4Ds","description":"Applied Machine Learning Days 2022 Repository for the Workshop \"Visual Disinformation and the Dark Side of Internet Memes\"","archived":false,"fork":false,"pushed_at":"2022-03-29T11:02:40.000Z","size":15817,"stargazers_count":2,"open_issues_count":0,"forks_count":1,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-06-18T01:51:27.328Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/i4Ds.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-03-04T23:17:25.000Z","updated_at":"2023-07-17T08:47:24.000Z","dependencies_parsed_at":null,"dependency_job_id":"8bc69472-28fb-4bfd-a50c-33ada962e221","html_url":"https://github.com/i4Ds/AMLD-2022-Visual-Disinformation","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/i4Ds/AMLD-2022-Visual-Disinformation","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/i4Ds%2FAMLD-2022-Visual-Disinformation","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/i4Ds%2FAMLD-2022-Visual-Disinformation/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/i4Ds%2FAMLD-2022-Visual-Disinformation/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/i4Ds%2FAMLD-2022-Visual-Disinformation/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/i4Ds","download_url":"https://codeload.github.com/i4Ds/AMLD-2022-Visual-Disinformation/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/i4Ds%2FAMLD-2022-Visual-Disinformation/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":265658900,"owners_count":23806832,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-09T05:13:35.274Z","updated_at":"2025-07-17T20:33:04.255Z","avatar_url":"https://github.com/i4Ds.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"### Applied Machine Learning Days EPFL 2022 - Workshop\n\n\u003cp align=\"left\"\u003e\n  \u003cimg src=\"./logos/logo_amld-epfl-dark.png\" width=\"200\" alt=\"armasuisse S+T\"\u003e\n\u003c/p\u003e\n\n# _Visual Disinformation and the Dark Side of Internet Memes_\n\nThis 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](https://appliedmldays.org/events/amld-epfl-2022/workshops/visual-disinformation-and-the-dark-side-of-internet-memes)).\n\n## Workshop Part 1\n\nClick on the following badge to open the notebook in Google Colab (recommended):\n\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/i4Ds/AMLD-2022-Visual-Disinformation/blob/main/part1.ipynb)\n\n## Workshop Part 2\n\nClick on the following badge to open the notebook in Google Colab (recommended):\n\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/i4Ds/AMLD-2022-Visual-Disinformation/blob/main/part2.ipynb)\n\n## Local Installation (Optional)\n\nIf you want to run the code locally, follow the instructions below to setup your environment.\n### Workshop Part 1\n\nClone the repository and install dependencies.\n\n1) Clone the repository or download the notebook\n2) Install dependencies\n  \n```\npip install -r requirements_part1.txt\n```\n\n3) set global variable `DATA_ROOT_PATH` to any directory (via notebook)\n\n### Workshop Part 2\n\nClone the repository and install dependencies. Warning: In this version you will see the solutions to some exercises (cell-hiding is a Colab feature).\n\n1) Clone the repository. To get the data you need [Git lfs](https://git-lfs.github.com/) while cloning the repository. Alternatively, you can download the data from this [Link](https://github.com/i4Ds/AMLD-2022-Visual-Disinformation/raw/main/data/GRU_202012.tar.gz)\n\n(Optional) install git-lfs:\n```\napt-get update\napt-get install git-lfs\n```\n\nClone the repository:\n```\ngit clone https://github.com/i4Ds/AMLD-2022-Visual-Disinformation.git\ncd AMLD-2022-Visual-Disinformation\n```\n\n2) Install the dependencies\n  \n```\npip install -r requirements_part2.txt\n```\n\n3) Prepare the data (if not cloned via git-lfs)\n  \nPlace the data into your preferred directory (default is ./data/) and unpack.\n\n```\ntar -xf ./data/GRU_202012.tar.gz --directory ./data/\n```\n\n4) Open Notebook: In the notebook you can skip the data-fetching / unpacking steps.\n\n## Workshop Organizers\n\nRaphael Meier, Scientific Project Manager, armasuisse S+T\n\nMarco Willi, Research Associate, FHNW\n\nMichael Graber, Professor, FHNW\n\n## Supported By\n\n[Armasuisse S+T](https://www.ar.admin.ch/de/armasuisse-wissenschaft-und-technologie-w-t/home.html)\n\n\u003cp align=\"left\"\u003e\n  \u003cimg src=\"./logos/ar.png\" width=\"350\" alt=\"armasuisse S+T\"\u003e\n\u003c/p\u003e\n\n[FHNW - University of Applied Sciences and Arts Northwestern Switzerland](https://www.fhnw.ch/en)\n\n\u003cp align=\"left\"\u003e\n  \u003cimg src=\"./logos/fhnw.png\" width=\"350\" alt=\"FHNW\"\u003e\n\u003c/p\u003e\n\n\n[Cyber Defence Campus](https://www.ar.admin.ch/en/armasuisse-wissenschaft-und-technologie-w-t/cyber-defence_campus.html)\n\n\u003cp align=\"left\"\u003e\n  \u003cimg src=\"./logos/cyd.png\" width=\"350\" alt=\"Cyber Defense Campus\"\u003e\n\u003c/p\u003e\n\n\n\n## Data Sources \u0026 References\n\n### Data\n\nData for Part 1 are from:\n- [The Hateful Memes Challenge](https://ai.facebook.com/tools/hatefulmemes/)\n- [COCO Text](https://bgshih.github.io/cocotext/)\n- [MS COCO](https://cocodataset.org/)\n- [MultiOFF](https://aclanthology.org/2020.trac-1.6/)\n\nData for Part 2 are from:\n- [Twitter Transparency](https://transparency.twitter.com/en/reports/information-operations.html). Any usage is subject to Twitter's [terms and conditions](https://developer.twitter.com/en/developer-terms)\n\n\n### References\n\nRadford, 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.\n\n\nKiela, 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.\n\n\nSuryawanshi, 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.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fi4ds%2Famld-2022-visual-disinformation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fi4ds%2Famld-2022-visual-disinformation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fi4ds%2Famld-2022-visual-disinformation/lists"}