Code and Resources for "It is just a flu": Assessing the Effect of Watch History on YouTube's Pseudoscientific Video Recommendations
Authors: Kostantinos Papadamou, Savvas Zannettou, Jeremy Blackburn, Emiliano De Cristofaro, Gianluca Stringhini, and Michael Sirivianos
The role played by YouTube's recommendation algorithm in unwittingly promoting misinformation and conspiracy theories is not entirely understood. Yet, this can have dire real-world consequences, especially when pseudoscientific content is promoted to users at critical times, such as the COVID-19 pandemic. In this paper, we set out to characterize and detect pseudoscientific misinformation on YouTube. We collect 6.6K videos related to COVID-19, the Flat Earth theory, as well as the anti-vaccination and anti-mask movements. Using crowdsourcing, we annotate them as pseudoscience, legitimate science, or irrelevant and train a deep learning classifier to detect pseudoscientific videos with an accuracy of 0.79.
We quantify user exposure to this content on various parts of the platform and how this exposure changes based on the user's watch history. We find that YouTube suggests more pseudoscientific content regarding traditional pseudoscientific topics (e.g., flat earth, anti-vaccination) than for emerging ones (like COVID-19). At the same time, these recommendations are more common on the search results page than on a user's homepage or in the recommendation section when actively watching videos. Finally, we shed light on how a user's watch history substantially affects the type of recommended videos.
Preprint available here. Dataset available here.
This repository makes publicly available to the research community, as well as the open-source community the folllowing tools and libraries:
-
The codebase of a Deep Learning Classifier for pseudoscientific videos detection on YouTube, and examples on how to train and test it;
-
A library that simplifies the usage of the trained classifier and implements all the required tasks for the classification of YouTube videos;
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An open-source library that provides a unified framework for assessing the effects of personalization on YouTube video recommendations in multiple parts of the platform: a) the homepage; b) the search results page; and c) the video recommendations section (recommendations when watching videos).
If you make use of any modules available in this codebase in your work, please cite the following paper:
@article{papadamou2020just,
title={'It is just a flu': Assessing the Effect of Watch History on YouTube's Pseudoscientific Video Recommendations},
author={Papadamou, Kostantinos and Zannettou, Savvas and Blackburn, Jeremy and De Cristofaro, Emiliano and Stringhini, Gianluca and Sirivianos, Michael},
journal={arXiv preprint arXiv:2010.11638},
year={2020}
}- Overview
- Installation
- Part 1: Detection of Pseudoscientific Videos
- Part 2: YouTube Recommendation Algorithm Audit Framework
- Acknowledgements
- LICENSE
YouTube has revolutionized the way people discover and consume video content online. However, while YouTube facilitates easy access to hundreds of well-produced educational,entertaining, and trustworthy news videos, mistargeted and abhorrent content is also common. While the scientific community has repeatedly pinpointed the need for effectively moderating inappropriate content, the various types of inappropriate content on the platform are relatively unstudied. At the same time, the role played by YouTube’s recommendation algorithm in unwittingly promoting such content is not entirely understood. In our work, we study pseudoscientific misinformation on YouTube by focusing on quantifying user exposure to pseudoscientific misinformation on various parts of the platform, and how this exposure changes based on the user's watch history. To do this, we first develop a deep learning model that detects pseudoscientific videos, as well as a methodology that allow us to simulate the behavior of logged-in and non-logged-in users with varying interests who casually browsing YouTube.
In this repository, we provide to the research community the source code of the developed classifier, as well as the source code of our methodology. In particular, the ability to run this kind of experiments while taking into account users' viewing history will be beneficial to researchers focusing on demystifying YouTube’s recommendation algorithm—irrespective of the topic of interest. Our methodology and codebase are generic and can be used to study other topics besides pseudoscience, e.g., additional conspiracy theories. More specifically, we make publicly available the following set of tools and libraries:
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The codebase of a Deep Learning Classifier for pseudoscientific videos detection on YouTube, and examples on how to train and test it;
-
A library that simplifies the usage of the classifier and implements all the required tasks for the classification of YouTube videos;
-
An open-source library that provides a unified framework for assessing the effects of personalization on YouTube video recommendations in multiple parts of the platform: a) the homepage; b) the search results page; and c) the video recommendations section (recommendations when watching videos).
Follow the steps below to install and configure all prerequisites for both the training and usage of the Pseudoscientific Content Detection Classifier (Part 1), and for using our YouTube Audit Framework (Part 2).
python3 -m venv virtualenv
source virtualenv/bin/activatepip install -r requirements.txtTo store the metadata of YouTube videos, as well as for other information we use MongoDB. Install MongoDB on your own system or server using:
We suggest the use of Robo3T as a user interface for interacting with your MongoDB instance.
pip install youtube-dl**Make use of youtube-dl wisely, carefully sending requests so that you do not spam YouTube with requests and get blocked.
This is the library utilized to call the YouTube Data API from Python
pip install --upgrade google-api-python-clientOur codebase uses HTTPS Proxies for multiple purposes:
- For downloading the transcripts of YouTube videos; and
- The YouTube Recommendation Algorithm Audit Framework uses an HTTPS Proxy for each one of the user profiles and browser instances that it maintains. This is mainly to ensure that all User Profiles used in our framework have the same geolocation and avoid changes to our results due to geolocation personalization.
You can either use your own HTTPS Proxies or buy some online and set them in the following files:
youtubeauditframework/userprofiles/info/user_profiles_info.json: Includes the HTTPS Proxies used to simulate distinct logged-in user profiles accessing YouTube from specific geolocations. Preferrably, according to our Audit framework, all HTTPS Proxies set in this file MUST be from similar locations (e.g., "US-San Fransisco-California").youtubehelpers/config/YouTubeAPIConfig.py: Includes the HTTPS Proxies used to download the transcript of YouTube videos usingyoutube-dl.
Our codebase uses the YouTube Data API to download video metadata and for many other purposes like searching YouTube. Hence, it is important that you create an API key for the YouTube Data API and set it in the configuration files of our codebase. You can enable the YouTube Data API for your Google account and obtain an API key following the steps here.
Once you have a YouTube Data API Key, please set the YOUTUBE_DATA_API_KEY variable in the following files:
-
youtubehelpers/config/YouTubeAPIConfig.py -
youtubeauditframework/utils/YouTubeAuditFrameworkConfig.py
We implement a deep learning model geared to detect pseudoscientific YouTube videos. As also described in our paper, to train and test our model we use the dataset available here.
Our classifier consists of four different branches, each processing a distinct input feature type: snippet, video tags, transcript, and the top 200 comments of a video. Then, all four branches' outputs are concatenated to form a five-layer, fully-connected neural network that merges their output and drives the final classification. The classifier uses fastText, a library for efficient learning of word/document-level vector representations and sentence classification. We use fastText to generate vector representations (embeddings) for all the available video metadata in text. For each input feature, we use the pre-trained fastText models (1) and fine-tune them using each of our input features. These models extract a 300-dimensional vector representation for each of the following input features of our dataset:
- Video Snippet: Concatenation of the title and the description of the video.
- Video Tags: Words defined by the uploader of a video to describe the content of the video.
- Transcript: Naturally, this is one of the most important features, as it describes the video’s actual content. (It includes the subtitles uploaded by the creator of the video or auto-generated by YouTube.) The classifier uses the fine-tuned model to learn a vector representation of the concatenated text of the transcript.
- Comments: We consider the top 200 comments of the video as returned by the YouTube Data API. We first concatenate each video’s comments and use them to fine-tune the fastText model and extract vector representations.
The second part of the classifier ("Fusing Network") is essentially a four-layer, fully-connected, dense neural network. We use a Flatten utility layer to merge the outputs of the four branches of the first part of the classifier, creating a 1200-dimensional vector. This vector is processed by the four subsequent layers comprising 256, 128, 64, and 32 units, respectively, with ReLU activation. To avoid over-fitting, we regularize using the Dropout technique; at each fully-connected layer, we apply a Dropout level of d=0.5, i.e., during each iteration of training, half of each layer's units do not update their parameters. Finally, the Fusing Network output is fed to the last neural network of two units with softmax activation, which yields the probabilities that a particular video is pseudoscientific or not. We implement our classifier using Keras with Tensorflow as the back-end.
1.2.1. Download pre-trained fastText word vectors that we fine-tune during feature engineering on our dataset:
cd pseudoscientificvideosdetection/models/feature_extraction
wget https://dl.fbaipublicfiles.com/fasttext/vectors-english/wiki-news-300d-1M.vec.zip
unzip wiki-news-300d-1M.vec.zip-
Create a MongoDB database called:
youtube_pseudoscience_dataseteither using Robo3T GUI or from the terminal. -
Create the following MongoDB collections under the
youtube_pseudoscience_datasetdatabase that you just created:
groundtruth_videosgroundtruth_videos_commentsgroundtruth_videos_transcripts
Note: If you are using our dataset please make sure that you create the appropriate MongoDB collections and import the data in each collection.
Below we describe how can you use the codebase to train our classifier using either our own data available here, or using your own dataset. We note that, our model is optimized for the detection of pseudoscientific content related to COVID-19, Anti-vaccination, Anti-mask, and Flat Earth. However, feel free to extend/enhance the provided codebase implementing your own deep learning model optimized for your use case.
In this step, we fine-tune four separate fastText models, one for each different video metadata type, which we use during the training of Deep Learning model to generate embeddings for each different video metadata type. This step is only required to run once.
At this step, we train and validate the Pseudoscientific Content Detection Deep Learning model using 10-fold cross-validation.
At the end of the training, the best model will be stored in: pseudoscientificvideosdetection\models\pseudoscience_model_final.hdf5.
We provide below an example of how you can use our codebase to train the Pseudoscientific Content Detection classifier.
from dataset.DatasetUtils import DatasetUtils
from classifier.featureengineering.FeatureEngineeringModels import FeatureEngineeringModels
from classifier.training.ClassifierTraining import ClassifierTraining
# Create Objects
dataset = DatasetUtils()
featureEngineeringModels = FeatureEngineeringModels(dataset_object=dataset)
""" Step 1: Fine-tune separate fastText models for each Video Metadata Type """
# Video Snippet
# Generate Video Snippet fastText input features
featureEngineeringModels.prepare_fasttext_data(model_type='video_snippet')
# Fine-tune a fastText model for Video Snippet
featureEngineeringModels.finetune_model(model_type='video_snippet')
# Video Tags
# Generate Video Tags fastText input features
featureEngineeringModels.prepare_fasttext_data(model_type='video_tags')
# Fine-tune a fastText model for Video Tags
featureEngineeringModels.finetune_model(model_type='video_tags')
# Video Transcript
# Generate Video Transcript fastText input features
featureEngineeringModels.prepare_fasttext_data(model_type='video_transcript')
# Fine-tune a fastText model for Video Transcript
featureEngineeringModels.finetune_model(model_type='video_transcript')
# Video Comments
# Generate Video Comments fastText input features
featureEngineeringModels.prepare_fasttext_data(model_type='video_comments')
# Fine-tune a fastText model for Video Comments
featureEngineeringModels.finetune_model(model_type='video_comments')
""" Step 2: Train the Pseudoscience Deep Learning Model """
# Create a Classifier Training Object
classifierTrainingObject = ClassifierTraining(dataset_object=dataset)
# Train the Classifier
classifierTrainingObject.train_model()In this repository, we also include a Python package that uses the trained classifier, which can be used by anyone who wants to train our classifier using our codebase and then use it to detect pseudoscientific videos on YouTube. The available Python module accepts a YouTube Video ID as input, and implements all the necessary steps to download the required information of the given video, extract the required features, and classifies the video using the trained classifier. Note that, the classifier is supposed to be trained and used to detect pseudoscientific videos on YouTube related to the following topics: a) COVID-19; b) Anti-vaccination; c) Anti-mask; and d) Flat Earth.
Finally, in order to use this package you first need to train the classifier using our classifier' codebase, and also to provide your own YouTube Data API key. If case you want to train the classifier using our own dataset, you request access to it and download it from here.
# Import the PSeudoscientific Videos Detection package
from pseudoscientificvideosdetection.PseudoscienceClassifier import PseudoscienceClassifier
from youtubehelpers.YouTubeVideoDownloader import YouTubeVideoDownloader
# Create an object of the classifier
pseudoscienceClassifier = PseudoscienceClassifier()
# You need to provide your own YouTube Data API Key when creating an object of the YouTube Video Downloader helper class
ytDownloader = YouTubeVideoDownloader()
# Download YouTube Video Details
# Note: You can replace the following line with your own method that creates the
# dictionary with the video details. See YouTubeVideoDownloader class to
# understand the format in which we convert the comments and the transcript
# of the video before we pass them to the classifier
video_details = ytDownloader.download_video(video_id='YOUTUBE_VIDEO_ID')
# Make a prediction
prediction, confidence_score = pseudoscienceClassifier.classify(video_details=video_details) -
Create a MongoDB database called:
youtube_recommendation_auditeither using Robo3T GUI or from the terminal. -
Create the following MongoDB collections under the
youtube_recommendation_auditdatabase that you just created:
audit_framework_videos: All videos of the YouTube Audit framework will be stored in this collection.audit_framework_youtube_homepage: Holds the details of each repetition of the YouTube Homepage audit experiment.audit_framework_youtube_search: Holds the details of each repetition of the YouTube Search Results audit experiment.audit_framework_youtube_video_recommendations: Holds the details of each repetition of the YouTube Video Recommendations audit experiment.
You can download the ChromeDriver that matches the one of your local Google Chrome version from here.
We recommend the usage of Google ChromeDriver 83.0.4103.39, however, if you download another one please make sure that you update youtubeauditframework/utils/YouTubeAuditFrameworkConfig.py.
-
Linux:
cd youtubeauditframework/utils/webdrivers wget https://chromedriver.storage.googleapis.com/83.0.4103.39/chromedriver_linux64.zip unzip chromedriver_linux64.zip && mv chromedriver_linux64 chromedriver rm chromedriver_linux64.zip
-
MacOS X:
cd youtubeauditframework/utils/webdrivers wget https://chromedriver.storage.googleapis.com/83.0.4103.39/chromedriver_mac64.zip unzip chromedriver_mac64.zip && mv chromedriver_mac64 chromedriver rm chromedriver_mac64.zip
You have to create your own Google Accounts for each YouTube user profile that you want to perform experiments with. According to our framework, the only aspect of each user profile that differs is the watch history, hence all the account information must be the similar for all user profiles (e.g., similar age, gender, country, etc.) to avoid confounding effects by profile differences. However, feel free to extend our framework assessing more personalization factors (i.e., age) and create user profiles based on your use case.
To decrease the likelihood of Google automatically detecting your user profiles, please carefully craft each one assigning them a unique name and surname and perform standard phone verification.
While crafting user profiles, make sure that you also update file youtubeauditframework/userprofiles/info/user_profiles_info.json with the information of each created YouTube User Profile.
To avoid Google banning or flagging the created user profiles, we perform manual authentication of each user profile before performing experiments using our framework. We provide a script to perform user authentication and create all the necessary files for each crafted YouTube User profile before running any audit experiment. To do this, perform the following for each created User Profile
cd youtubeauditframework/userprofiles/helpers
python initialize_authenticate_user_profile.py <USER_PROFILE_NICKNAME>Make sure that you run this for each user profile by providing the nickname of each user profile (as set in user profiles information file) in the beginning of the script. When running this script, a browser will automatically open and you will be able to perform manual Google authentication.
Previous step will open a browser and load the YouTube authentication page. Once this is done, proceed and authenticate the corresponding user manually.
Once the user is authenticated you MUST install Adblock Plus manually by visiting: https://adblockplus.org/
Please ensure that you properly close the browser window before executing this script for another user profile, or before running any experiment.
Once you have created all the User Profiles that you want to use and you have also authenticated all users to YouTube, then you can use the following class to
build the watch history of each user: youtubeauditframework/userprofiles/BuildUserWatchHistory.py.
from youtubeauditframework.userprofiles.BuildUserWatchHistory import BuildUserWatchHistory
# Set the User Profile's nickname for which you want to build the Watch History
user_profile = 'USER_PROFILE_NICKNAME'
# Create an object of the helper class for building the User Profile's Watch History
buildUserWatchHistoryHelper = BuildUserWatchHistory(user_profile=user_profile)
""" Build the User's Watch History """
# OPTION 1: Build the watch history of the user profile, but first create a file with the following naming convention
# "<USER_PROFILE_NICKNAME>_watch_history_videos.txt", which includes all the YouTube Video IDs separated by
# breakline (Enter Key). Store this file inside the "youtubeauditframework/userprofiles/info/" directory.
buildUserWatchHistoryHelper.build_watch_history()
# OPTION 2: Build the watch history of the user profile by providing a list of minimum 100 YouTube Video IDs.
# watch_history_videos = ['<VIDEO_ID>', '<VIDEO_ID>', '<VIDEO_ID>']
# buildUserWatchHistoryHelper.build_watch_history(watch_videos_list=watch_history_videos)
# Ensure that the browser has closed
buildUserWatchHistoryHelper.close_selenium_browser()Important: Remember to set the date you built User Profiles Watch Histories before running the Framework
YouTube's "Delete Watch History" functionality allows you to only delete the watch history and the search history of a user before a specific date. Due to this, when using our framework, you first need to build the watch history of all the User Profiles that you want to use at one date, and start performing experiments using our framework the next date.
After you have built the Watch History of all User Profiles,
please set the value of DELETE_WATCH_HISTORY_AFTER_DATE to the next date of that date in file:
youtubeauditframework/utils/YouTubeAuditFrameworkConfig.py.
For example, if you build the watch histories of your User Profiles on 01-03-2021 then you should set DELETE_WATCH_HISTORY_AFTER_DATE='02-03-2021' and start running experiments using our framework the next date on 02-03-2021.
We focus on three parts of the platform: 1) the homepage; 2) the search results page; and 3) the video recommendations section (recommendations when watching videos). With our framework, we simulate logged-in and non-logged-in user's behavior with varying interests and measure how the watch history affects pseudoscientific content recommendation. Below, we provide examples of how to run the difference experiments for each part of the YouTube platform.
Run the following for each one of your User Profiles:
cd youtubeauditframework
python perform_audit_youtube_homepage.py USER_PROFILE_NICKNAMENote: We suggest that you simultaneously run this script for each user profile in your scenario to avoid variations in results due to time.
Before auditing YouTube Search, you need to identify the search terms for which you will focus on your experiments. Then, run the following for each combination of search terms and User Profiles in your use case:
cd youtubeauditframework
# Run for a specific Search Term and User Profile (Replace spaces in SEARCH_TERM with '_')
python perform_audit_youtube_search.py USER_PROFILE_NICKNAME SEARCH_TERM
# Run for a specific Search Term using the YouTube Data API
python perform_audit_youtube_search.py YOUTUBE_DATA_API SEARCH_TERMNote: We suggest that you simultaneously run this script for each Search Term for all the User Profiles in your scenario to avoid variations in results due to time.
Before auditing YouTube's Video Recommendations Section, you need to identify the search terms for which you will focus on your experiments. Then, run the following for each combination of search terms and User Profiles in your use case:
cd youtubeauditframework
# Run for a specific Search Term and User Profile (Replace spaces in SEARCH_TERM with '_')
python perform_audit_video_recommendations.py USER_PROFILE_NICKNAME SEARCH_TERM
# Run for a specific Search Term using the YouTube Data API
python perform_audit_video_recommendations.py YOUTUBE_DATA_API SEARCH_TERMNote: We suggest that you simultaneously run this script for each Search Term for all the User Profiles in your scenario to avoid variations in results due to time.
When downloading YouTube videos while running the YouTube Recommendation Algorithm audit experiments, we only download and save the metadata of each video. Hence, we provide a script that you can run after you finished running the audit experiments so that you also download the comments and the transcripts of each video, which are both required to annotate a video.
Execute the following to download missing videos' information and annotate all videos that are not annotated, yet:
cd youtubeauditframework/helpers
python download_annotate_experiment_videos.pyWe also provide some helper classes that allows you to analyze the results of the audit experiments, and generate similar plots as the plots included in the paper. Note that, you should analyze the results of the experiments only after you have performed all audit experiments for all User Profiles, and you have also downloaded and annotated all audit experiments' videos.
Below we provide some examples for how you can use these helper classes to analyze audit experiments results and generate plots for each different part of the YouTube platform:
from youtubeauditframework.analysis.YouTubeHomepageAuditAnalysis import YouTubeHomepageAuditAnalysis
# Set the mapping between the User Profiles used for the experiments and their Plot Legend labels
# Below is an example from the one used in the paper
plot_legend_labels = {
'SCIENCE_USER': 'Science Profile',
'PSEUDOSCIENCE_USER': 'Pseudoscience Profile',
'SCIENCE_PSEUDOSCIENCE_USER': 'Science/Pseudoscience Profile',
'NO_PERSONALIZATION': 'No Profile (Browser)',
}
# Create a YouTube Homepage Audit Experiment Analysis
experimentAnalysisObject = YouTubeHomepageAuditAnalysis(legend_labels_mapping=plot_legend_labels)
# Perform audit experiments results analysis
experimentAnalysisObject.analyze_audit_experiments()
# Generate Plot
experimentAnalysisObject.plot_results()from youtubeauditframework.analysis.YouTubeSearchAuditAnalysis import YouTubeSearchAuditAnalysis
# Set the considered Serach Terms to analyze and generate plots
# Below is an example from the one used in the paper
considered_search_terms = ['covid-19', 'coronavirus']
# Set the mapping between the User Profiles used for the experiments and their Plot Legend labels
# Below is an example from the one used in the paper
plot_legend_labels = {
'SCIENCE_USER': 'Science Profile',
'PSEUDOSCIENCE_USER': 'Pseudoscience Profile',
'SCIENCE_PSEUDOSCIENCE_USER': 'Science/Pseudoscience Profile',
'NO_PERSONALIZATION': 'No Profile (Browser)',
'YOUTUBE_DATA_API': 'No Profile (YouTube Data API)'
}
# Create a YouTube Search Audit Experiment Analysis
experimentAnalysisObject = YouTubeSearchAuditAnalysis(search_terms=considered_search_terms,
legend_labels_mapping=plot_legend_labels)
# Perform audit experiments results analysis
experimentAnalysisObject.analyze_audit_experiments()
# Generate Plot
experimentAnalysisObject.plot_results()from youtubeauditframework.analysis.YouTubeVideoRecommendationsAuditAnalysis import YouTubeVideoRecommendationsAuditAnalysis
# Set the considered Serach Terms to analyze and generate plots
# Below is an example from the one used in the paper
considered_search_terms = ['covid-19', 'coronavirus']
# Set the mapping between the User Profiles used for the experiments and their Plot Legend labels
# Below is an example from the one used in the paper
plot_legend_labels = {
'SCIENCE_USER': 'Science Profile',
'PSEUDOSCIENCE_USER': 'Pseudoscience Profile',
'SCIENCE_PSEUDOSCIENCE_USER': 'Science/Pseudoscience Profile',
'NO_PERSONALIZATION': 'No Profile (Browser)',
'YOUTUBE_DATA_API': 'No Profile (YouTube Data API)'
}
# Create a YouTube Search Audit Experiment Analysis
experimentAnalysisObject = YouTubeVideoRecommendationsAuditAnalysis(search_terms=considered_search_terms,
legend_labels_mapping=plot_legend_labels)
# Perform audit experiments (Random Walks) results analysis
experimentAnalysisObject.analyze_audit_experiments()
# Generate Random Walks Plot
experimentAnalysisObject.plot_results()Note: Also, note that the provided helper classes analyze the results considering Pseudoscientific videos found in the audit experiments and provide similar plots as those in the paper. If you wish to use our codebase considering another type(s) of inappropriate videos then you need to adapt both the codebase of our framework, as well as the helper classes for analyzing the results of the audit experiments.
Unfortunately, due to regular Google Chrome Updates or other updates on the YouTube Website, our framework may not function properly from time to time. In this case, we list below some of the most common issues that we faced to assist you with overcoming them when using our framework.
If you find problems running our framework (i.e., the browser is closing right after you start an experiment), then this probably due to a mismatch
between the declared ChromeDriver downloaded, the User-Agent declared in youtubeauditframework/utils/YouTubeAuditFrameworkConfig.py and the current version of your
installed Google Chrome. It is better if all these three match and you can start by finding the version of your installed browser in its "About Google Chrome" section.
You can download the ChromeDriver that matches your installed Google Chrome and Operating System from here.
Last, if you do not use the recommended ChromeDriver version then make sure that you update User-Agent string in youtubeauditframework/utils/YouTubeAuditFrameworkConfig.py.
Automated functionalities of framework, like getting the recommended videos of a given video, or deleting the watching history of a logged-in user may not work from time to time and this is mainly because YouTube regularly updates its HTML and CSS classes. Hence, when you have such problems you may need to update the codebase of our framework with the latest XPaths of each element (i.e., button, video thumbnail) that you can find by inspecting each element on the YouTube website (using Google Chrome inspect option).
Before running an experiment with a given User Profile, ensure that this user is logged-in.
You can do this by running the helper script in youtubeauditframework/userprofiles/helpers/initialize_authenticate_user_profile.py.
You can set the desired User Profile in the beginning of this script, and when running this helper script the corresponding browser (with the details and activity of this user) will open.
Then you will be able to manually follow the authentication flow and authenticate this user profile on YouTube.
Upon successful authentication, please ensure that you properly close the opened browser window before running any experiments using this user profile.
If you have trouble accessing your created YouTube accounts from the automated browsers, then ensure that "Less secure app access" is enabled for all accounts. You can enable "less secure app access" to a Google Account in the following way:
- Open a browser and login to Google using the credentials of the corresponding User Profile.
- Visit Google Account Security settings.
- Scroll down to "Less secure app access" section and click "Turn on access" or enable it directly from here.
Please see the paper for funding details and non-code related acknowledgements.
MIT License