The Grammar of Truth and Lies
Using NLP to detect Fake News
Peter J Bleackley
Playful Technology Limited
peter.bleackley@playfultechnology.co.uk

The Problem
●
“A lie can run around the world before the truth can get its
boots on.”
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Fake News spreads six times faster than real news on Twitter
●
The spread of true and false news online, Sorush Vosougi,
Deb Roy, Sinan Aral, Science, Vol. 359, Issue 6380, pp.
1146-1151, 9th March 2018
●
https://science.sciencemag.org/content/359/6380/1146

The Data
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“Getting Real about Fake News” Kaggle Dataset
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https://www.kaggle.com/mrisdal/fake-news
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12999 articles from sites flagged as unreliable by the BS Detector
chrome extension
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Reuters-21578, Distribution 1.0 Corpus
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10000 articles from Reuters Newswire, 1987
●
http://kdd.ics.uci.edu/databases/reuters21578/reuters21578.html
●
Available from NLTK

Don’t Use Vocabulary!
●
Potential for bias, especially as corpora are from different
time periods
●
Difficult to generalise
●
Could be reverse-engineered by a bad actor

Sentence structure features
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Perform Part of Speech tagging with TextBlob
●
Concatenate tags to form a feature for each sentence
●
“Pete Bleackley is a self-employed data scientist and
computational linguist.”
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'NNP_NNP_VBZ_DT_JJ_NNS_NN_CC_JJ_NN'
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Very large, very sparse feature set

First model
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Train LSI model (Gensim) on sentence structure features
from whole dataset
●
70/30 split between training and test data
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Sentence structure features => LSI => Logistic Regression
(scikit-learn)
●
https://www.kaggle.com/petebleackley/the-grammar-of-trut
h-and-lies

Performance
●
Precision 61%
●
Recall 96%
●
Accuracy 70%
●
Matthews Correlation Coefficient 50%
●
Precision measures our ability to catch the bad guys.

Sentiment analysis
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Used VADER model in NLTK
●
Produces Positive, Negative and Neutral scores for each
sentence
●
Sum over document
●
Precision 71%, Recall 88%, Accuracy 79%, Matthews 59%

Sentence Structure + Sentiments
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Precision 74%
●
Recall 90%
●
Accuracy 81%
●
Matthews 64%
●
Slight improvement, but it looks like sentiment is doing
most of the work

Random Forests
Precision Recall Accuracy Matthews
Sentence
structure
83% 89% 86% 71%
Sentiments 75% 75% 78% 76%
Both 84% 89% 87% 76%

Understanding the models
●
Out of 333264 sentence structure features, 298332 occur
only in a single document
●
Out of 23000 documents, 11276 have no features in
common with others
●
We need some denser features

Function words
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Pronouns, prepositions, conjunctions, auxilliaries
●
Present in every document – most common words
●
Usually discarded as “stopwords”...
●
...but useful for stylometric analysis, eg document
attribution
●
NLTK stopwords corpus

New model
●
Sentence structure features + function words => LSI =>
Logistic Regression
●
Precision 90%
●
Recall 96%
●
Accuracy 93%
●
Matthews 87%

What have we learnt?
●
Grammatical and stylistic features can be used to
distinguish between real and fake news
●
Good choice of features is the key to success
●
Will this generalise to other sources?

See also...
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The (mis)informed citizen
●
Alan Turing Institute project
●
https://www.turing.ac.uk/research/research-projects/misinf
ormed-citizen