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Identifying Partisan Slant in News Articles
and Twitter during Political Crises
Dmytro Karamshuk12, Tetyana Lokot3, Oleksandr Pryymak4 , Nishanth Sastry2
1Skyscanner, 2King’s College London, 3Dublin City University, 4Facebook
“A Shared Space & A Space for Sharing”, ESRC project, http://www.space4sharingstudy.org/
• what are the interrelationships between mainstream media
and social networks in shaping public opinion during mass
protests and war conflicts
• how propaganda and manipulation in the information sphere
work
• can we identify and characterize media bias in traditional and
social media during conflict
Identifying partisan slant during political crises
Use case and datasets
Ukrainian	Crisis	in	2013-2014
• revolution of “dignity”, Nov’13 – Feb’14
• annexation of Crimea and war conflict in
Earstern Ukraine, Feb’14 - today
A headline from a Russian news agency, 2015
“The	country	is	a	madhouse,	and	people	in	it	are	patients”	
RIA	News,	2015
In contrast to the brainwashed masses, the leaders of the junta
understand that the 150K army of "Ukrainian patriots" resists not Russian troops but 20
thousand local "separatists", complimented with a couple of thousands (or even
less) volunteers from nearly a dozen countries around the world, including Russia.
The leaders of the junta understand the situation at the front and, in particular, the
lack of effective command and control, arbitrariness in the ranks of the National Guard
under and Right Sector, the hatred of the population to the law enforcement bodies, but the
main thing – horrible morale of the personnel of the Armed Forces of Ukraine, which is
expressed in the mass desertion, drunkenness, looting and robbery, which
do not reflect the goals of Ukrainian "revolution."
https://ria.ru/analytics/20150410/1057804681.html
Theory of propaganda
International	Encyclopedia	of	Propaganda	- Cole,	R.	(Ed.).	(1998) -
identifies	over	40	kinds	of	propaganda	techniques,	corroborated	in	
other	media	literacy	and	media	studies	sources.	
• Ad nauseam (insistent ideas)
• Repetition
• Demonizing the enemy
• “Kind words,” Slogans, Euphoria
• Cult of personality
• Lingvo propaganda (verbal control)
• Assigning labels to events/personas
Identifying markers of partisan slant
Extract	and	compare	semantics	of	words	in	different	sources
using	Word2Vec	approach
Predicting slant – Machine learning approach
Select	top	media	sources	
in	Ukraine	and	Russia	
Manually	classify
Train	supervised	learning	model
• Russian independent
• Russian pro-government
• Ukrainian
• Top-30 Russian online news source
• Top-5 Ukrainian online news sources
• use text features from news articles as
features
• predict from a news source of which
party it originates
Problem with this approach
We	need	to	make	sure	that	the	language	patterns	we	learn	
are	partisan	– not	source-specific
• Exemplar markers of individual news sources
Identifying markers of partisan slant
discourage	learning	patterns	specific	to	individual	news	
agencies	by	modifying	objective	function
take-one-source-out	cross	validation	where	we	test	on	a	
news	source	which	was	not	shown	during	training	
Machine Learning results
Control for source-specific bias
An average Twitter user is exposed to a variety of news sources
BUT with a clear partisan focus
How about Social Media?
We	can	reason	about	bias	in	Twitter	by	looking	at	news	reposts	
… but, only a small share of users repost news articles
Predicting political leaning in social media
Supervised learning model to identify political leaning of a Twitter user
based on the content of his/her Tweets
• use content of all tweets (except of news reposts) from a user profile as features
• predict leaning based on what they repost
There	is	a	reasonable/identifiable	difference	in	posts	of	Twitter	
profiles	exposed	to	different	partisan	media
Conclusions
• We have shown how to measure the difference in word
choices in partisan media during conflicts
• We trained a supervised machine learning model to
recognize media bias in both traditional and social media
• Even such “coarse-grain” approach of labelling news
agencies can perform reasonably well in identifying political
leaning
Dmytro Karamshuk
follow me on Twitter: @karamshuk

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Identifying Partisan Slant in News Articles and Twitter during Political Crises

  • 1. Identifying Partisan Slant in News Articles and Twitter during Political Crises Dmytro Karamshuk12, Tetyana Lokot3, Oleksandr Pryymak4 , Nishanth Sastry2 1Skyscanner, 2King’s College London, 3Dublin City University, 4Facebook “A Shared Space & A Space for Sharing”, ESRC project, http://www.space4sharingstudy.org/
  • 2. • what are the interrelationships between mainstream media and social networks in shaping public opinion during mass protests and war conflicts • how propaganda and manipulation in the information sphere work • can we identify and characterize media bias in traditional and social media during conflict Identifying partisan slant during political crises
  • 3. Use case and datasets Ukrainian Crisis in 2013-2014 • revolution of “dignity”, Nov’13 – Feb’14 • annexation of Crimea and war conflict in Earstern Ukraine, Feb’14 - today
  • 4. A headline from a Russian news agency, 2015 “The country is a madhouse, and people in it are patients” RIA News, 2015 In contrast to the brainwashed masses, the leaders of the junta understand that the 150K army of "Ukrainian patriots" resists not Russian troops but 20 thousand local "separatists", complimented with a couple of thousands (or even less) volunteers from nearly a dozen countries around the world, including Russia. The leaders of the junta understand the situation at the front and, in particular, the lack of effective command and control, arbitrariness in the ranks of the National Guard under and Right Sector, the hatred of the population to the law enforcement bodies, but the main thing – horrible morale of the personnel of the Armed Forces of Ukraine, which is expressed in the mass desertion, drunkenness, looting and robbery, which do not reflect the goals of Ukrainian "revolution." https://ria.ru/analytics/20150410/1057804681.html
  • 5. Theory of propaganda International Encyclopedia of Propaganda - Cole, R. (Ed.). (1998) - identifies over 40 kinds of propaganda techniques, corroborated in other media literacy and media studies sources. • Ad nauseam (insistent ideas) • Repetition • Demonizing the enemy • “Kind words,” Slogans, Euphoria • Cult of personality • Lingvo propaganda (verbal control) • Assigning labels to events/personas
  • 6. Identifying markers of partisan slant Extract and compare semantics of words in different sources using Word2Vec approach
  • 7. Predicting slant – Machine learning approach Select top media sources in Ukraine and Russia Manually classify Train supervised learning model • Russian independent • Russian pro-government • Ukrainian • Top-30 Russian online news source • Top-5 Ukrainian online news sources • use text features from news articles as features • predict from a news source of which party it originates
  • 8. Problem with this approach We need to make sure that the language patterns we learn are partisan – not source-specific • Exemplar markers of individual news sources
  • 9. Identifying markers of partisan slant discourage learning patterns specific to individual news agencies by modifying objective function take-one-source-out cross validation where we test on a news source which was not shown during training Machine Learning results Control for source-specific bias
  • 10. An average Twitter user is exposed to a variety of news sources BUT with a clear partisan focus How about Social Media? We can reason about bias in Twitter by looking at news reposts … but, only a small share of users repost news articles
  • 11. Predicting political leaning in social media Supervised learning model to identify political leaning of a Twitter user based on the content of his/her Tweets • use content of all tweets (except of news reposts) from a user profile as features • predict leaning based on what they repost There is a reasonable/identifiable difference in posts of Twitter profiles exposed to different partisan media
  • 12. Conclusions • We have shown how to measure the difference in word choices in partisan media during conflicts • We trained a supervised machine learning model to recognize media bias in both traditional and social media • Even such “coarse-grain” approach of labelling news agencies can perform reasonably well in identifying political leaning Dmytro Karamshuk follow me on Twitter: @karamshuk