├── .gitignore ├── README.md ├── data ├── automated-v1.0 │ ├── automatedAccountData.json │ └── nonautomatedAccountData.json └── fake-v1.0 │ ├── fakeAccountData.json │ └── realAccountData.json ├── main.py ├── requirements.txt └── utils.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | .pytest_cache/ 49 | 50 | # Translations 51 | *.mo 52 | *.pot 53 | 54 | # Django stuff: 55 | *.log 56 | local_settings.py 57 | db.sqlite3 58 | 59 | # Flask stuff: 60 | instance/ 61 | .webassets-cache 62 | 63 | # Scrapy stuff: 64 | .scrapy 65 | 66 | # Sphinx documentation 67 | docs/_build/ 68 | 69 | # PyBuilder 70 | target/ 71 | 72 | # Jupyter Notebook 73 | .ipynb_checkpoints 74 | 75 | # pyenv 76 | .python-version 77 | 78 | # celery beat schedule file 79 | celerybeat-schedule 80 | 81 | # SageMath parsed files 82 | *.sage.py 83 | 84 | # Environments 85 | .env 86 | .venv 87 | env/ 88 | venv/ 89 | ENV/ 90 | env.bak/ 91 | venv.bak/ 92 | 93 | # Spyder project settings 94 | .spyderproject 95 | .spyproject 96 | 97 | # Rope project settings 98 | .ropeproject 99 | 100 | # mkdocs documentation 101 | /site 102 | 103 | # mypy 104 | .mypy_cache/ 105 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # InstaFake Dataset 2 | Dataset of the [Intagram Fake and Automated Account Detection](https://arxiv.org/pdf/1910.03090.pdf) paper 3 | 4 | ### Installation 5 | 6 | ##### Install miniconda 7 | https://conda.io/en/latest/miniconda.html 8 | 9 | ##### Setup a CONDA environment 10 | We create a new vitrual environment named `instafake`. 11 | ``` 12 | conda create --name instafake python=3.6 13 | ``` 14 | 15 | ##### Activating the virtual environment 16 | If you are inside the virtual environment, your shell prompt should look like: `(instafake) user@computer:~$` 17 | If that is not the case, you can enable the virtual environment using: 18 | ``` 19 | conda activate instafake 20 | ``` 21 | To deactivate the virtual environment, use: 22 | ``` 23 | conda deactivate 24 | ``` 25 | 26 | ##### Install required packages 27 | 28 | To install the required packages, run the following command in your `instafake` virtual environment: 29 | ``` 30 | pip install -r requirements.txt 31 | ``` 32 | 33 | ### Import Datasets as Dataframes 34 | To import the fake and automated datasets as pandas dataframes, simply define the dataset folder path `data`, and dataset version `dataset_version` and call `import_data` from `utils`: 35 | 36 | ~~~py 37 | from utils import import_data 38 | 39 | dataset_path = "data" 40 | dataset_version = "fake-v1.0" 41 | 42 | fake_dataset = import_data(dataset_path, dataset_version) 43 | 44 | dataset_path = "data" 45 | dataset_version = "automated-v1.0" 46 | 47 | automated_dataset = import_data(dataset_path, dataset_version) 48 | ~~~ 49 | 50 | ### Dataset Structures 51 | 52 | The dataset contains of 2 set of json files with given features: 53 | 54 | ##### Fake Account Detection 55 | 1. `user_media_count` - Total number of posts, an account has. 56 | 2. `user_follower_count` - Total number of followers, an account has. 57 | 3. `user_following_count` - Total number of followings, an account has. 58 | 4. `user_has_profil_pic` - Whether an account has a profil picture, or not. 59 | 5. `user_is_private` - Whether an account is a private profile, or not. 60 | 6. `user_biography_length` - Number of characters present in account biography. 61 | 7. `username_length` - Number of characters present in account username. 62 | 8. `username_digit_count` - Number of digits present in account username. 63 | 9. `is_fake` - True, if account is a spam/fake account, False otherwise 64 | 65 | ##### Automated Account Detection 66 | 1. `user_media_count` - Total number of posts, an account has. 67 | 2. `user_follower_count` - Total number of followers, an account has. 68 | 3. `user_following_count` - Total number of followings, an account has. 69 | 4. `user_has_highlight_reels` - Whether an account has at least one highlight reel present, or not. 70 | 5. `user_has_url` - Whether an account has an url present in biography, or not. 71 | 6. `user_biography_length` - Number of characters present in account biography. 72 | 7. `username_length` - Number of characters present in account username. 73 | 8. `username_digit_count` - Number of digits present in account username. 74 | 9. `media_comment_numbers` - Total number of comments for a given media. 75 | 10. `media_comments_are_disabled` - Whether given media is closed for comments, or not. 76 | 11. `media_has_location_info` - Whether given media includes location, or not. 77 | 12. `media_hashtag_numbers` - Total number of hashtags, given media has. 78 | 13. `media_upload_times` - Media upload timastamps. 79 | 14. `automated_behaviour` - True, if account is an automated account, False otherwise 80 | 81 | ### Dataset Metadata 82 | The following table is necessary for this dataset to be indexed by search 83 | engines such as Google Dataset Search. 84 |
85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 116 | 134 | 135 | 136 | 137 | 155 | 156 |
propertyvalue
nameInstaFake Dataset: An Instagram fake and automated account detection dataset
alternateNameInstaFake Dataset
url
sameAshttps://github.com/fcakyon/instafake-dataset
sameAshttps://github.com/fcakyon/instafake-dataset
descriptionThe InstaFake Dataset is comprised of anonymized Instagram user data collected by Fatih Cagatay Akyon and Esat Kalfaoglu over the second half of 2018. We’re releasing this dataset publicly to aid the research community in making advancements in machine learning based social media analysis.
provider 117 |
118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 |
propertyvalue
nameFatih C. Akyon and Esat Kalfaoglu
sameAshttps://scholar.google.com.tr/citations?user=RHGyDE0AAAAJ&hl=en
132 |
133 |
license 138 |
139 | 140 | 141 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 149 | 150 | 151 | 152 |
propertyvalue
nameAttribution-NonCommercial
url
153 |
154 |
157 |
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1, "userIsPrivate": 0, "usernameDigitCount": 1, "usernameLength": 19, "isFake": 1}, {"userFollowerCount": 1, "userFollowingCount": 1, "userBiographyLength": 59, "userMediaCount": 7, "userHasProfilPic": 1, "userIsPrivate": 0, "usernameDigitCount": 4, "usernameLength": 15, "isFake": 1}, {"userFollowerCount": 0, "userFollowingCount": 25, "userBiographyLength": 138, "userMediaCount": 6, "userHasProfilPic": 1, "userIsPrivate": 0, "usernameDigitCount": 0, "usernameLength": 10, "isFake": 1}, {"userFollowerCount": 0, "userFollowingCount": 0, "userBiographyLength": 52, "userMediaCount": 6, "userHasProfilPic": 1, "userIsPrivate": 0, "usernameDigitCount": 3, "usernameLength": 7, "isFake": 1}, {"userFollowerCount": 0, "userFollowingCount": 1, "userBiographyLength": 92, "userMediaCount": 9, "userHasProfilPic": 1, "userIsPrivate": 0, "usernameDigitCount": 1, "usernameLength": 15, "isFake": 1}, {"userFollowerCount": 1, "userFollowingCount": 0, "userBiographyLength": 60, "userMediaCount": 5, "userHasProfilPic": 1, "userIsPrivate": 0, "usernameDigitCount": 1, "usernameLength": 13, "isFake": 1}, {"userFollowerCount": 0, "userFollowingCount": 0, "userBiographyLength": 108, "userMediaCount": 6, "userHasProfilPic": 1, "userIsPrivate": 0, "usernameDigitCount": 2, "usernameLength": 10, "isFake": 1}] -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | from utils import import_data 2 | 3 | dataset_path = "data" 4 | dataset_version = "fake-v1.0" 5 | 6 | fake_dataset = import_data(dataset_path, dataset_version) 7 | 8 | dataset_path = "data" 9 | dataset_version = "automated-v1.0" 10 | 11 | automated_dataset = import_data(dataset_path, dataset_version) 12 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | pandas==0.25.1 2 | seaborn==0.9.0 -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | import os 2 | import re 3 | import json 4 | import pandas as pd 5 | 6 | #%% Create dataframe 7 | 8 | def create_dataframe(account_data_list, dataset_type): 9 | dataframe = pd.DataFrame({}) 10 | 11 | if dataset_type == "automated": 12 | for account_data in account_data_list: 13 | user_follower_count = account_data["userFollowerCount"] 14 | user_following_count = account_data["userFollowingCount"] 15 | follower_following_ratio = user_follower_count/max(1,user_following_count) 16 | 17 | temp_dataframe = pd.Series({"user_media_count":account_data["userMediaCount"], 18 | "user_follower_count":account_data["userFollowerCount"], 19 | "user_following_count":account_data["userFollowingCount"], 20 | "user_has_highligh_reels":account_data["userHasHighlighReels"], 21 | "user_has_external_url":account_data["userHasExternalUrl"], 22 | "user_tags_count":account_data["userTagsCount"], 23 | "follower_following_ratio":follower_following_ratio, 24 | "user_biography_length":account_data["userBiographyLength"], 25 | "username_length":account_data["usernameLength"], 26 | "username_digit_count":account_data["usernameDigitCount"], 27 | "media_comment_numbers":account_data["mediaCommentNumbers"], 28 | "media_comments_are_disabled":account_data["mediaCommentNumbers"], 29 | "media_has_location_info":account_data["mediaHasLocationInfo"], 30 | "media_hashtag_numbers":account_data["mediaHashtagNumbers"], 31 | "media_like_numbers":account_data["mediaLikeNumbers"], 32 | "mediaUpload_times":account_data["mediaUploadTimes"], 33 | "automated_behaviour":account_data["automatedBehaviour"] 34 | }) 35 | dataframe = dataframe.append(temp_dataframe, ignore_index=True) 36 | 37 | elif dataset_type == "fake": 38 | for account_data in account_data_list: 39 | user_follower_count = account_data["userFollowerCount"] 40 | user_following_count = account_data["userFollowingCount"] 41 | follower_following_ratio = user_follower_count/max(1,user_following_count) 42 | 43 | temp_dataframe = pd.Series({"user_media_count":account_data["userMediaCount"], 44 | "user_follower_count":account_data["userFollowerCount"], 45 | "user_following_count":account_data["userFollowingCount"], 46 | "user_has_profil_pic":account_data["userHasProfilPic"], 47 | "user_is_private":account_data["userIsPrivate"], 48 | "follower_following_ratio":follower_following_ratio, 49 | "user_biography_length":account_data["userBiographyLength"], 50 | "username_length":account_data["usernameLength"], 51 | "username_digit_count":account_data["usernameDigitCount"], 52 | "is_fake":account_data["isFake"] 53 | }) 54 | dataframe = dataframe.append(temp_dataframe, ignore_index=True) 55 | return dataframe 56 | 57 | #%% Import automated/nonautomated data 58 | 59 | def import_data(dataset_path, dataset_version): 60 | #base_path = os.path.dirname(os.path.abspath(__file__)) 61 | #base_path = "/Users/fca/Documents/GitHub/instafake-dataset" 62 | dataset_type = re.findall("automated|fake",dataset_version)[0] 63 | if dataset_type == "automated": 64 | with open(dataset_path + "/" + dataset_version + "/automatedAccountData.json") as json_file: 65 | automated_account_data = json.load(json_file) 66 | with open(dataset_path + "/" + dataset_version + "/nonautomatedAccountData.json") as json_file: 67 | nonautomated_account_data = json.load(json_file) 68 | 69 | automated_account_dataframe = create_dataframe(automated_account_data, dataset_type) 70 | nonautomated_account_dataframe = create_dataframe(nonautomated_account_data, dataset_type) 71 | merged_dataframe = automated_account_dataframe.append(nonautomated_account_dataframe, ignore_index=True) 72 | data = dict({"dataset_type":dataset_type, 73 | "dataframe":merged_dataframe}) 74 | 75 | elif dataset_type == "fake": 76 | with open(dataset_path + "/" + dataset_version + "/fakeAccountData.json") as json_file: 77 | fake_account_data = json.load(json_file) 78 | with open(dataset_path + "/" + dataset_version + "/realAccountData.json") as json_file: 79 | real_account_data = json.load(json_file) 80 | 81 | fake_account_dataframe = create_dataframe(fake_account_data, dataset_type) 82 | real_account_dataframe = create_dataframe(real_account_data, dataset_type) 83 | merged_dataframe = fake_account_dataframe.append(real_account_dataframe, ignore_index=True) 84 | data = dict({"dataset_type":dataset_type, 85 | "dataframe":merged_dataframe}) 86 | 87 | return data --------------------------------------------------------------------------------