Fake-News-Detection | Fake news detector based on the content and users | Machine Learning library

by iamjagdeesh Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | Fake-News-Detection Summary

Fake-News-Detection is a Python library typically used in Artificial Intelligence, Machine Learning applications. Fake-News-Detection has no bugs, it has no vulnerabilities and it has low support. However Fake-News-Detection build file is not available. You can download it from GitHub.

The project aims at classifying the given news articles as fake or true based on the content and users associated with it using Graph Attention Networks (GATs). Technology used: Google BERT, Graph Attention Network (GAT), Python, Pandas, NumPy, scikit-learn, Tensorflow.

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Security

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Fake-News-Detection has a low active ecosystem.

It has 26 star(s) with 15 fork(s). There are 8 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Fake-News-Detection is current.

Quality

Fake-News-Detection has 0 bugs and 0 code smells.

Security

Fake-News-Detection has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Fake-News-Detection code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Fake-News-Detection does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

Fake-News-Detection releases are not available. You will need to build from source code and install.

Fake-News-Detection has no build file. You will be need to create the build yourself to build the component from source.

Fake-News-Detection saves you 450 person hours of effort in developing the same functionality from scratch.

It has 1064 lines of code, 49 functions and 15 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed Fake-News-Detection and discovered the below as its top functions. This is intended to give you an instant insight into Fake-News-Detection implemented functionality, and help decide if they suit your requirements.

Process p2p dataset
Finds the split based on mapping
Runs the dfs algorithm
Performs the DFS decomposition on the adjacency matrix
Generate the adjacency matrix
Return the list of News objects connected to the given news
Fetches the news data from the website
Extract news and user data from folder
Generate the feature matrix
Get data from folder
Get data from file
Creates a tokenizer from the hub module
Get the features for a given dataset
R Returns the adjacency matrix
Returns a pandas DataFrame containing the feature
Load pandas data
Generate a mask from a given index
Parse an index file
Read json files and real news files
Extract description from a JSON file
Calculate accuracy
Get all newsfeed data
Create a training op
Preprocess the features
Calculate softmax cross entropy
Preprocess an adjacency matrix
This test is used for testing

Get all kandi verified functions for this library.

Fake-News-Detection Key Features

No Key Features are available at this moment for Fake-News-Detection.

Fake-News-Detection Examples and Code Snippets

No Code Snippets are available at this moment for Fake-News-Detection.

Community Discussions

Trending Discussions on Fake-News-Detection

Extract emojis from tweets in R

Trying to separate my data points into multiple arrays, instead of having one big array

found input variables with inconsistent number of samples error

QUESTION

Extract emojis from tweets in R

Asked 2020-Apr-24 at 10:55

I'm doing feature extraction from labelled Twitter data to use for predicting fake tweets. I've been spending a lot of time on various GitHub methods, R libraries, stackoverflow posts, but somehow I couldn't find a "direct" method of extracting features related to emojis, e.g. number of emojis, whether the tweet contains emoji(1/0) or even occurrence of specific emojis(that might occur more often in fake/real news). I'm not sure whether there is a point in showing reproducible code.

"Ore" library, for example, offers functions that gather all tweets in an object and extracts emojis, but the formats are problematic (at least, to me) when trying to create features out of the extractions, as mentioned above. The example below uses a whatsapp text sample. I will add twitter data from kaggle to make it somewhat reproducible. Twitter Dataset: https://github.com/sherylWM/Fake-News-Detection-using-Twitter/blob/master/FinalDataSet.csv

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ANSWER

Answered 2020-Apr-24 at 10:55

I wrote a function for this purpose in my package rwhatsapp.

As your example is a whatsapp dataset, you can test it directly using the package (install via remotes::install_github("JBGruber/rwhatsapp"))

Source https://stackoverflow.com/questions/61216342

QUESTION

Trying to separate my data points into multiple arrays, instead of having one big array

Asked 2020-Apr-09 at 19:33

Im working on an nlp project and am working with fake news, with one of the inputs being the headlines. I have tokenized my headlines in the following format:

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ANSWER

Answered 2020-Apr-09 at 19:33

You are iterating over each word and appending them one at a time to the list, which is why it is flattening. Instead of appending each word you need to append the filtered list. This is probably clearer if you do it as a list comprehension:

Source https://stackoverflow.com/questions/61128739

QUESTION

found input variables with inconsistent number of samples error

Asked 2020-Apr-06 at 22:56

Im trying to train a model, however when I fit the model, I am getting the following error:

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ANSWER

Answered 2020-Apr-06 at 22:56

I think you have one feature and 3608 records, but the code thinks there is one sample with 3608 features.

change the code where x and y are defined as follows.

Source https://stackoverflow.com/questions/61068176

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Fake-News-Detection

You can download it from GitHub.
You can use Fake-News-Detection like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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