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If the issue persists, it's likely a problem on our side. Unexpected token < in JSON at position 4. keyboard_arrow_up. content_copy. SyntaxError: Unexpected token < in JSON at position 4. Refresh. Fake News Classification on WELFake Dataset.
Feb 24, 2022 · In this project, a dataset about fake news is collected and combined with pre-existing datasets. In addition, a model that can detect if an input text is a piece of fake news is created.
LIAR is a publicly available dataset for fake news detection. A decade-long of 12.8K manually labeled short statements were collected in various contexts from POLITIFACT.COM, which provides detailed analysis report and links to source documents for each case.
- Overview
- Installation
- Running Code
- References
FakeNewsNet
*** We will never ask for money to share the datasets. If someone claims that s/he has the all the raw data and wants a payment, please be careful. ***
We released a tool FakeNewsTracker, for collecting, analyzing, and visualizing of fake news and the related dissemination on social media. Check it out!
The latest dataset paper with detailed analysis on the dataset can be found at FakeNewsNet
Please use the current up-to-date version of dataset
Previous version of the dataset is available in branch named old-version of this repository.
Requirements:
Data download scripts are writtern in python and requires python 3.6 + to run. Twitter API keys are used for collecting data from Twitter. Make use of the following link to get Twitter API keys https://developer.twitter.com/en/docs/basics/authentication/guides/access-tokens.html Script make use of keys from tweet_keys_file.json file located in code/resources folder. So the API keys needs to be updated in tweet_keys_file.json file. Provide the keys as array of JSON object with attributes app_key,app_secret,oauth_token,oauth_token_secret as mentioned in sample file. Install all the libraries in requirements.txt using the following command
Configuration:
FakeNewsNet contains 2 datasets collected using ground truths from Politifact and Gossipcop. The config.json can be used to configure and collect only certain parts of the dataset. Following attributes can be configured •num_process - (default: 4) This attribute indicates the number of parallel processes used to collect data. •tweet_keys_file - Provide the number of keys available configured in tweet_keys_file.txt file •data_collection_choice - It is an array of choices of various parts of the dataset. Configure accordingly to download only certain parts of the dataset. Available values are {"news_source": "politifact", "label": "fake"},{"news_source": "politifact", "label": "real"}, {"news_source": "gossipcop", "label": "fake"},{"news_source": "gossipcop", "label": "real"} •data_features_to_collect - FakeNewsNet has multiple dimensions of data (News + Social). This configuration allows one to download desired dimension of the dataset. This is an array field and can take following values. •news_articles : This option downloads the news articles for the dataset. •tweets : This option downloads tweets objects posted sharing the news in Twitter. This makes use of Twitter API to download tweets. •retweets: This option allows to download the retweets of the tweets provided in the dataset. •user_profile: This option allows to download the user profile information of the users involved in tweets. To download user profiles, tweet objects need to be downloaded first in order to identify users involved in tweets. •user_timeline_tweets: This option allows to download upto 200 recent tweets from the user timeline. To download user's recent tweets, tweet objects needs to be downloaded first in order to identify users involved in tweets. •user_followers: This option allows to download the user followers ids of the users involved in tweets. To download user followers ids, tweet objects need to be downloaded first in order to identify users involved in tweets. •user_following: This option allows to download the user following ids of the users involved in tweets. To download user's following ids, tweet objects needs to be downloaded first in order to identify users involved in tweets.
Inorder to collect data set fast, code makes user of process parallelism and to synchronize twitter key limitations across mutiple python processes, a lightweight flask application is used as keys management server. Execute the following commands inside code folder,
The above command will start the flask server in port 5000 by default.
Configurations should be done before proceeding to the next step !!
Execute the following command to start data collection,
Logs are wittern in the same folder in a file named as data_collection_ .log and can be used for debugging purposes.
The dataset will be downloaded in the directory provided in the config.json and progress can be monitored in data_collection.out file.
If you use this dataset, please cite the following papers:
(C) 2019 Arizona Board of Regents on Behalf of ASU
LIAR is a dataset for fake news detection with 12.8K human labeled short statements from politifact.com's API, and each statement is evaluated by a politifact.com editor for its truthfulness.
Weibo21 is a benchmark of fake news dataset for multi-domain fake news detection (MFND) with domain label annotated, which consists of 4,488 fake news and 4,640 real news from 9 different domains. 8 PAPERS • NO BENCHMARKS YET
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FakeNewsNet is collected from two fact-checking websites: GossipCop and PolitiFact containing news contents with labels annotated by professional journalists and experts, along with social context information. Source: Leveraging Multi-Source Weak Social Supervision for Early Detection of Fake News.
