fake_news {bayesrules}

A collection of 150 news articles

Description

A dataset containing data behind the study "FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media" https://arxiv.org/abs/1809.01286. The news articles in this dataset were posted to Facebook in September 2016, in the run-up to the U.S. presidential election.

Usage

fake_news

Format

A data frame with 150 rows and 6 variables:

title

The title of the news article

text

Text of the article

url

Hyperlink for the article

authors

Authors of the article

type

Binary variable indicating whether the article presents fake or real news(fake, real)

title_words

Number of words in the title

text_words

Number of words in the text

title_char

Number of characters in the title

text_char

Number of characters in the text

title_caps

Number of words that are all capital letters in the title

text_caps

Number of words that are all capital letters in the text

title_caps_percent

Percent of words that are all capital letters in the title

text_caps_percent

Percent of words that are all capital letters in the text

title_excl

Number of characters that are exclamation marks in the title

text_excl

Number of characters that are exclamation marks in the text

title_excl_percent

Percent of characters that are exclamation marks in the title

text_excl_percent

Percent of characters that are exclamation marks in the text

title_has_excl

Binary variable indicating whether the title of the article includes an exlamation point or not(TRUE, FALSE)

anger

Percent of words that are associated with anger

anticipation

Percent of words that are associated with anticipation

disgust

Percent of words that are associated with disgust

fear

Percent of words that are associated with fear

joy

Percent of words that are associated with joy

sadness

Percent of words that are associated with sadness

surprise

Percent of words that are associated with surprise

trust

Percent of words that are associated with trust

negative

Percent of words that have negative sentiment

positive

Percent of words that have positive sentiment

text_syllables

Number of syllables in text

text_syllables_per_word

Number of syllables per word in text

Source

Shu, K., Mahudeswaran, D., Wang, S., Lee, D. and Liu, H. (2018) FakeNewsNet: A Data Repository with News Content, Social Context and Dynamic Information for Studying Fake News on Social Media