revealing the hidden patterns of news photos: analysis of millions of news photos through gdelt and...

Revealing the Hidden Patterns of News Photos: Analysis of Millions of News Photos through GDELT and Deep Learning-based Vision APIs

Haewoon Kwak Jisun An

Qatar Computing Research InstituteHamad Bin Khalifa University

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Roles of News Photos

● Influence people’s perception● Enhance reader’s memory● Deliver emotion otherwise hard to be

conveyed

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Why was this photo not picked?(source: https://www.donaldjtrump.com)

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To Understand Messages of Photos

● We need to know○ What are shown in the photos○ How they are portrayed

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Challenges in News Photo Analysis

● Text mining has been a useful tool for analyzing news text

→ What is the appropriate tool forexamining news photos?

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Conventional Tool for Photo Analysis

● Manual coding … hard to scale

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Deep Learning for Image Recognition

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Object Recognition

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Emotion Detection

https://www.microsoft.com/cognitive-services/en-us/emotion-api 14

Deep learning enables us to study news photos in large-scale

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Goal of This Work

● To offer a general understanding of news photos ○ What are shown in the photos?○ How are people portrayed?

■ From the perspective of emotion■ From the perspective of gender

● Case study: Portrayal of politicians

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● We can crawl photos from news websites and analyze them

● But, setting the deep learning framework and training it take time/money/...

Data Collection

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GDELT Visual GKG (VGKG)

● Collects news articles around the world● Extract photos from the articles● Calls Google Cloud Vision API to analyze

photos

● VGKG is available since 1 Jan 2016 http:

//blog.gdeltproject.org/announcing-the-new-gdelt-visual-global-knowledge-graph-vgkg/

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Example of VGKG

20160101004500 http://www.bbc.co.uk/news/uk-35205943 http://ichef.bbci.co.uk/news/1024/cpsprodpb/B89F/production/_87436274_87436273.jpg profession<FIELD>0.95780987<FIELD>/m/063km<RECORD>person<FIELD>0.85714287<FIELD>/m/01g317<RECORD>close up<FIELD>0.82379222<FIELD>/m/02cqfm<RECORD>bishop<FIELD>0.78259438<FIELD>/m/01b7b<RECORD>bishop<FIELD>0.71334475<FIELD>/m/027k49j<RECORD>diocesan bishop<FIELD>0.64282793<FIELD>/m/09sgrf<RECORD>auxiliary bishop<FIELD>0.57118613<FIELD>/m/05mx3n<RECORD>clergy<FIELD>0.57113737<FIELD>/m/0db79 -2<FIELD>-2<FIELD>-2<FIELD>-2 0.95443642<FIELD>3.199043<FIELD>12.419704<FIELD>-7.179338<FIELD>0.621747<FIELD>433,215;575,215;575,357;433,357<FIELD>-2<FIELD>-2<FIELD>-2<FIELD>2<FIELD>-2<FIELD>-2<FIELD>-2

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Date, Document identifier (URL), Image URL, Labels (description, confidence score, unique id), Geographic Landmarks, Logos, Safe Search, Faces (Angle, Emotion, etc.), OCR

(Potential) Limitations of GDELT

● List of news sources is not explicitly announced (also, growing) - coverage bias might exist

● Our work of comparing GDELT with another news dataset will be presented in the poster session

Two Tales of the World: Comparison of Widely Used World News Datasets - GDELT and EventRegistry Haewoon Kwak and Jisun AnICWSM'16: The 10th International Conference on Web and Social Media (poster), 2016 21

Our Dataset - Full

● GKG and VGKG in January 2016● Popularity measured by Alexa.com

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Our Dataset - 7 Popular News Media

● Top 30 & > 1K records

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Data Preprocessing

● Keep labels whose confidence score ≥ .8

http://i2.cdn.turner.com/cnnnext/dam/assets/160116174054-kerry-handshake-zarif-large-169.jpg

Person 0.84957772Business 0.59667766

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What Are Shown in the Photos?Common Objects in News Photos

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News Topics and Relevant Photos

● News photos should relate with topics of news articles→ Common objects might be different across topics

● CNN has ‘section’ info. in its URLhttp://edition.cnn.com/2016/04/07/travel/japan-best-of-wakayama/index.htmlhttp://edition.cnn.com/2016/05/05/politics/paul-ryan-donald-trump-republican-resistance/index.html 26

Person is the Most Common Object

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But, in Travel, Person is Uncommon

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Region-related Sections

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● Why does this matter?

Western Media and the Third World

● Golan reports that western mass media strengthen the portrayal of the third world by reporting war, poverty, famine, conflicts, violence and conflicts and lead to negative perception (Golan 2008).

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How CNN deals with MENA region?

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How Are People Portrayed? From the Perspective of Emotion

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Classification of Emotions

33https://articulation360.wordpress.com/2011/08/26/emotions-memory-game/

Google API Can Detect 4 Emotions

34https://articulation360.wordpress.com/2011/08/26/emotions-memory-game/

SURPRISE

SORROW ANGER

JOY

Neutral (75%) or Joy (24%)

● Among 11,127 faces (in 7 popular media), 2,740 faces (24.6%) have one of emotions

● Most of them (2,665 faces) express joy

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Nonverbal & Verbal Communication

● Happy faces accelerate the cognitive processing of positive words and slow down that of negative words (Stenberg, Wiking, and Dahl 1998)

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We Use Microsoft Face API

● Measures smiling intensity (0.0~1.0)

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0.998 0.0 (baby)

https://www.microsoft.com/cognitive-services/en-us/face-api

Smile Comes with Positive Text

● Positive correlation between smile intensity and tone (sentiment) of the text

⍴=0.225

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How Are People Portrayed? From the Perspective of Gender

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Previous Studies on News Media

1. Men outnumber women2. Men and women are associated with

particular roles3. More women than men were depicted

as happy and calm.

→ We’ll verify this in large-scale

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Again, We Use Microsoft Face API

41https://www.microsoft.com/cognitive-services/en-us/face-api

● Measures Gender and Age

Unequal Gender Representation

0.5

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Stereotyping: Women in “Living”

0.5

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Women Smile More Than Men

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Younger Women, Older Men

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Case StudyPortrayal of Politicians

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Smiling Politicians

● Goodnow (2010) found that Obama smiles more than Clinton in photos in Time magazine

● Smile gives a positive, non-threatening impression to viewers (Goffman 1979)

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Bias of CNN Toward Sanders?

(Smiling faces / All faces)

* CNN even uses “Sorrow” faces for Sanders

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Pro-Clinton Media Behave Similarly

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Summary and Future Work

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Key Findings

● What are shown in the news photos○ People commonly appear (≥ 40.5% @top500)

● How they are portrayed○ People are neutral (75%) or smiling (24%)○ Gender representation is unequal○ Gender role stereotyping is found○ Women smile more and look younger than men

● Clinton smiles more than Sanders in some media

→We demonstrate the great potential of deep learning for computational journalism

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Deeper Analysis on Text and Photos

● Headline and photos?● Topic and photos?● Keywords and photos?

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Building PhotoBiasMeter.org

● Showing the preference of media outlets toward candidates over time

● Challenges○ Modeling complex dimension of

preference - “Smile” is only one dimension

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@haewoonFull paper is available via

http://arxiv.org/abs/1603.04531

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