isspek / fake_news_detection

Training a neural network model from small size of high quality labeled dataset for fake news detection

False Article Detection With Weakly Supervised Learning

This contains the code for detecting the false article detection using weakly supervised learning.

Due to storage constraint, we are not able to load many files such as embedding.model, glove, etc. We have kept them in Google drive

The Data\Input2 is the dataset on which overall comparision have been done and it is default dataset for model.The reports also generated from dataset2

Starting

Install libraries in requirements.txt:

pip3 install -r requirements.txt

Data Preprocessing for Mean Teacher and Pseduo Label

From the command prompt, change your present working directory to folder where your downloaded code is present. Change the directory to Fakenewdetection folder execute the below command

python Datapreprocessing\Datapreprocessing.py

Place the label.csv and unlabel.csv under Data\Preprocessing\Input1 folder. We have created 4 different types of datasets each of them are placed under Input1, Input2, Input3, Input4 folders respectively.

Summary of the columns present in the data file.

Column	Description
Article	contains true and fake articles
Label	For True news = 1, Fake news = 0 and unlabeled = -1. (numeric value)

Once the data preprocessing is completed, train data and test data files will be available under Data\Input folder.

Default values

As the preprocessing is done, the npy files will be available in Data\Input2, which will be input for Mean Teacher and Pseduo Label for training.

For example:-

python Datapreprocessing\Datapreprocessing.py --inputPath C:\fake_news_detection\Data\Preprocessing\Input4\ --outputPath C:\fake_news_detection\Data\Input4\

Parameters required for running Datapreprocessing.py

Parameter	Description
--inputPath	input path for un-processed data
--outputPath	output path for processed data, taken as input for ML models
--labeldataname	file name for label data
--unlabeldataname	file name for unlabel data
--xTrain	npy xTrain file name
--xTest	npy xTest file name
--yTrain	npy yTrain file name
--yTest	npy yTest file name
--xUnlabel	npy xUnlabel file name

Default values

Parameter	Default Value
--inputPath	os.path.abspath(os.getcwd())+"\Data\\Preprocessing\Input2\
--outputPath	os.path.abspath(os.getcwd())+"\Data\Input2\
--labeldataname	label.csv
--unlabeldataname	unlabel.csv
--xTrain	xtr_shuffled.npy
--xTest	xte_shuffled.npy
--yTrain	ytr_shuffled.npy
--yTest	yte_shuffled.npy
--xUnlabel	xun_shuffled.npy

We have completed preprocessing and placed all those data files in respective folders such as Data\Input1,Data\Input2,Data\Input3,Data\Input4. From these folder, preprocessed data can take for further training the meanteacher and pseduolabel models

From the analysis,we found that

Dataset 1 is over fitting

Dataset 2 is tested and working correctly

Dataset 3 has less amount of data compare to other dataset

Dataset 4 is working fine but not tested completely.

Mean Teacher

Mean Teacher model for false article classification.

It will take test and train data from Data\Input2 folder as default, and once training complete it will place the report and plot in Data\Output1 folder as default Please Go to folder path where code is available and run like below Mean Teacher, we have created different method of noises which can be called by parameters

Parameter	Description
--meanteacher MT_syn_unlabel_diff	Mean teacher
--meanteacher MT_syn_noise_diff	Mean teacher with synonym noise
--meanteacher MT_dropout	Mean teacher with dropout
--meanteacher MT_syn_dropout	Mean teacher with synonym and dropout

Once you call these method as parameter, automatically code regarding to this function and train the data in Data\Input2 folder (default) and the report will be placed in Data\Output2 folder (default) , default folders also changed by parameter passing

python MeanTeacher\main.py --meanteacher MT_dropout --lr 0.0005 --epochs 5 --batch_size 64 --alpha 0.95

other parameters are also available

Parameter	Description
--ratio
--threashold
--inputPath	Input path to the train model ( /Users/tmp/Testing/)
--outputPath	Output path to place report and files ( /Users/tmp/Testing/)

Default values

Parameter	Default Value
--lr	0.0001
--epochs	10
--batchSize	64
--ratio	0.5
--alpha	0.97
--dropout	0.2
--synonym_noise_b1	0.5
--synonym_noise_b2	0.3
--inputPath	os path where you placed the code in system + \Data\Input2\
--outputPath	os.path.here you placed the code in system + \Data\Output2\
--xTrain	"xtr_shuffled.npy"
--xTest	"xte_shuffled.npy"
--yTrain	"ytr_shuffled.npy"
--yTest	"yte_shuffled.npy"
--xUnlabel	"xun_shuffled.npy"
--reportIterate	"report_iterate_mean_teacher.csv"

Pseduo Label

Pseduo Label model for false article classification.

It will take test and train data from Data\Input2 folder as default, and once training complete it will place the report and plot in Data\Output2 folder as default Please Go to folder path where code is available and run like below

python PseudoLabel\main.py --inputPath C:\fake_news_detection\Data\Input4\ --epochs 30 --batch_size 32

--inputPath - input path to the train model (Eg: /Users/tmp/Testing/) --outputPath - output path to place report and files (Eg: /Users/tmp/Testing/)

Parameter	Default Value
--lr	0.0001
--Epochs	14
--batch_size	64
--Thresh	0.90
--n	10
--max_len	128
--inputPath	os path where you placed the code in system + \Data\Input2\
--outputPath	os.path.here you placed the code in system + \Data\Output2\
--xTrain	"xtr_shuffled.npy"
--xTest	"xte_shuffled.npy"
--yTrain	"ytr_shuffled.npy"
--yTest	"yte_shuffled.npy"
--xUnlabel	"xun_shuffled.npy"

VAT

Data Preprocessing for VAT

VAT used the spacy for the preprocessing the code and its has its own folder strutcure to the run the VAT main code.

Raw data was placed in the VAT\Input1\raw -- which is not processed data (label.csv and unlable.csv files), the processed files will be placed in the temp inside same folder

we have created 4 different types datasets each data sets placed in the different folder like Input1,Input2,Input3,Input4 in the same way.

Here preprocessing done in four stages

Stage 1 : Here raw (default-VAT\Input1\raw) file wil be processed and placed in processed folder ( default - VAT\Input1\processed)

Example:

python VAT\preprocessing\text_spacy.py --inputPath C:\fake_news_detection\VAT\Input4\raw\ --outputPath C:\fake_news_detection\VAT\Input4\processed\

Parameter	Default Value
--inputPath	\VAT\Input1\raw\
--labelfilename	label.csv
--unlabelfilename	unlabel.csv
--outputPath	\VAT\Input1\processed\
--Remain	label.csv
--xUnlabel	"xun_shuffled.npy"

Stage 2 : split the label data and place it in the processed folder

Example:

python VAT\preprocessing\split_data.py --Path C:\fake_news_detection\VAT\Input2\processed\

Parameter	Default Value
--Path	\VAT\Input1\processed\
--filename	label.csv
--xUnlabel	xun_shuffled.npy
--xTrain	x_train.npy
--xTest	x_test.npy
--yTrain	y_train.npy
--yTest	y_test.npy

Stage 3 : Tokenize the splitted data., here word picket file will be placed in the meta folder

Example:

python VAT\preprocessing\tokenization_vat.py --inputPath C:\fake_news_detection\VAT\Input2\processed\ --outputPath C:\fake_news_detection\VAT\Input2\temp\ --pickelPath C:\fake_news_detection\VAT\Input2\meta\

Parameters with default values:

Parameter	Default Value
--inputPath	\VAT\Input1\processed\
--outputPath	\VAT\Input1\temp\
--pickelPath	\VAT\Input1\meta\
--filename	label.csv
--xUnlabel	xun_shuffled.npy
--xTrain	x_train.npy
--xTest	x_test.npy
--yTrain	y_train.npy
--yTest	y_test.npy

Stage 4 : Emedding, for this golve file is need, this file you can download from the site https://nlp.stanford.edu/projects/glove/ and place it in the path \VAT\ because this files is common to all input files. Once emedding its created and it will place it in the meta folder which is default.

Example :

python VAT\preprocessing\embedding.py --pickelPath C:\fake_news_detection\VAT\Input2\meta\ 

Parameters with default values:

Parameter	Default Value
--pickelPath	\VAT\Input1\meta
--glovePath	\VAT
--EMBEDDING_DIM	300

Once all processing completed the final processed file will be placed inside the VAT\Input1\temp\ folder for each data. We have completed the preprocessing and those files are already in location.

VAT main

Once all files are available in temp directery, then we have to call only the main\vat.py

Example:

python VAT\main\vat.py --n_epochs 30 --batchSize 64 --lr 0.0001 

Parameters with default values:

Parameter	Default Value
--lr	0.0001
--n_epochs	10
--batch_size	32
--emb_dim	300
--doc_length	100
--n_splits	10
--epsilon	0.01
--act_fn	'tanh'
--model_type	'VAT'
--comment	'Unknown embeddings 6210/38028'
--inputPath	os.path.abspath(os.getcwd())+"\VAT\Input1\"
--outputPath	os.path.abspath(os.getcwd())+"\VAT\Output1\"

Contributors

Bhupender kumar Saini

Swathi Chidipothu Hare

Mayur Rameshbhai Waghela

Lokesh Sharma

Ravineesh Goud

Ipek Baris