Note: This repository is not maintained anymore. While functioning, please note that due significant changes in the YouTube API quota, the data acquisition using the official YouTube API through the here presented crawler code is not feasible anymore.

You can find our Journal paper Collaborations on YouTube: From Unsupervised Detection to the Impact on Video and Channel Popularity published in ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) here.

YouTube analysis concerning content creator collaborations in videos, using face recognition. This projects includes the framework for data acquisition from YouTube, video and face processing and statistical evaluation of the acquired YouTube data.

This repository contains the framework described in the paper Collaborations on YouTube: From Unsupervised Detection to the Impact on Video and Channel Popularity, C. Koch, M. Lode, D. Stohr, A. Rizk, R. Steinmetz, ACM Transactions on Multimedia Computing, Communications and Applications (TOMM)

For face feature extraction trained models and code from FaceNet is used. Face detection and alignment uses MTCNN Multi-task CNN.

For clustering face features, the algorithm HDBSCAN is used.

Face recognition evaluation were conducted on the YouTube Faces dataset as well as Labeled Faces in the Wild LFW dataset.

Collaborations between YouTube channel are detected based on appearing persons (faces). Content creator are assigned to channel based on number of appearances and, as a result, collaborations are modeled in a directed graph.

Graph Schema Example:

Data for applying face recognition and evaluation can be acquired using the provided crawler in [data_collection] directory.

The dataset of this work consists of 7,942 channel, >250K videos as well as statistical YouTube data for over 3 months.

Evaluation data and plots of this dataset are made available in the data/figures directory. If you are interested in the actual crawled YouTube data you can contact us.

For a simplified deployment we provide a Docker image published under lodemo/catana:0.1.

Which can be run with:

docker pull lodemo/catana:0.1
docker run -p 9000:9000 -p 8000:8000 -t -i lodemo/catana:0.1 /bin/bash

After that a bash is opened and the CATANA repository residing in /catana can be executed. (CPU only)

(Note: This Docker image does not include requirements for running the face recognition evaluation scripts (LFW, YTF), which are not directly part of the CATANA framework.)

The Docker image can also be compiled by hand using the Dockerfile residing in this repository.

Depending on the use-case different requirements must be met.

For using the CATANA framework only, without evaluation of the other face recognition methods like OpenFace, see requirements.txt as well as the requirements of FaceNet.

For executing the face recognition evaluations, additionally requirements for the OpenFace (https://cmusatyalab.github.io/openface/) framework must be installed.

All use cases also require a current OpenCV 2.4 version installed.

For video extraction, youtube_dl is used, a version is included in the face_recognition directory, but should be updated to a current version when used.

All parts of the framework in some way interact with a database in which the crawled YouTube data and found face representation are stored. We use the python ORM-framework SQLAlchemy to support different underlying database technologies without code changes. By default CATANA expects a MySQL server configuration, but depending on the available technology this can be changed to i.e. a local SQLite for testing.

All database configurations and credentials are set in the respective database.py file, residing in the modules directory. database.py needs to be updated before usage!

Crawling is based on Scrapy. Main crawling for YouTube is implemented in two separated jobs in the youtubeAPICrawler directory. Crawl "populate" populates an existing MySQL database with static data of channels and should only be executes once at the beginning. Crawl "update" is the task for repeated crawl of dynamically data like view, or subscriber counts. This crawl also checks for new videos uploaded by the channels in the MySQL database and adds these to the database. In the work of CATANA this crawl was executed daily.

database.py configuration resides in the crawler directory under youtubeAPICrawler/youtubeAPICrawler.

The face recognition pipeline is a multi-threaded script which downloads a list of YouTube videos and extracts the detected face's embeddings, which are then saved to the MySQL database. File videoPipeline.py is the entry point for the face recognition pipeline, where certain videos are read from the database and a multi-thread video downloader based on youtube_dl is started. The actual face recognition application is implemented as a convenient PostProcessor class of youtube_dl which directly plugs-in after the download process. The process potentially runs a long time, depending on the internet-connection and number of videos. For us, a processing rate of 21sec/video were achieved in average. After video analysis is finished, the extracted face embeddings are stored in the database with information of the video and duration. Next, clustering should be executed on the found embeddings to link occuring faces across videos.

database.py configuration resides in the face_recognition directory.

Depending on number of embeddings and system, the clustering process can be applied through different scripts.

collabDetection.py executes all clustering steps in one job and may fail due to memory consumption.

IF COMPUTATION TIME IS TOO HIGH, USE THE SPLIT UP pipeline: (collabCreateIndex.py, collabCompIndexBlock.py, collabCompIndexBlockProcess.py)

• create features array from database
• create indices splitting the matrix up into n parts using collabCreateIndex.py
• compute the parts separate using collabCompIndexBlock.py or collabCompIndexBlockProcess.py

IF MEMORY CONSUMPTION IS TOO HIGH: (collabCreateMemMap.py, collabCompDistanceMemMap.py, collabDetectionMemMap.py)

• try mem-mapped script types, loading the distance matrices as mem-mapped numpy arrays

After clustering, cluster are stored in the database and using the 00_Collaboration.ipynb notebook, the collaboration detection and graph creation can be executed.

Directory src/visualization contains different scripts to the creation of a interactive visualization of the found collaboration in form of a graph.

CATANA collaboration graph overview:

1-hop sub-graph for the "PewDiePie" channel:

For further information see the respective directory README files and code documentation for instructions.

database.py configuration resides in the visualization directory.

Data evaluation is based on Jupyter notebook .ipynb files located in data_evaluation. File names indicate an execution order, while 'XX' files can be executed independent after the strict order execution.

• 00_Channel Statistics.ipynb: Evaluates channel statistics, mainly only statically data and no daily statistics.
• 01_Collaborations.ipynb: Reads the found cluster from the database, creates the actual collaboration graph.
• 02_Collaborations_Graph_Metric.ipynb: Graph metrics evaluation, clustering cooefficient etc.
• Collaboration History Channel/Video Statistics.ipynb: Both files evaluate the effects on video or channel statistics (ie. view, subscriber counts)

contains evaluation results, channel data, YouTube API information and plots

contains code for the different pipeline steps

crawler code for YouTube, Socialblade and Google Images

evaluation code, jupyter-notebook based evaluation, code for plot creation

video download, face recognition and clustering pipeline, contains Facenet code base as well as youtube_dl

collaboration graph visualization based on Gugel Universum (https://universum.gugelproductions.de/).

misc scripts, face recognition evaluation scripts (heavily based on FaceNet and OpenFace (https://cmusatyalab.github.io/openface/) code)