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Candidate Representation: A
Preliminary Study
Jennifer A. Stark Ph.D.
Nick Diakopoulos Ph.D.
Computational Journalism Lab
...
Images &
Elections
• Competence inferred
from facial appearance
(Todorov et al., 2005)
• Children (5-13 yrs) use
same faci...
Gender Bias
• Stereotype exaggeration and
systematic underrepresentation
of women in search results (Kay et
al., 2015)
• S...
Where is Google directing our attention?
How did Google Image Box images represent each
candidate?
Which news sources deli...
Plan Scrape data: Request timestamp
Search query (‘donald
trump’, ‘hillary clinton’)
Image box Rank
News link
Image path
D...
Google Scraper from here, and customised
Collection once per day, 8 weeks.
Data stored in MySQL database on AWS.
Associate each image with each dataframe row:
Image Hashing
Image Hashing
Image Hashing
Image Hashing
Determine News Source Frequency
Determine News Source of Each Image
Identify unique image file names
Click on an image; where do you go?!
Ugly and useless. Edit SVG in Adobe Illustrator...
What level of importance is each image/news source given?
What is Image Rank?
Gender
Bias?
Microsoft Emotion & Face API
Loop through Emotion JSON files
Our Candidates’ Emotions: HRC
DJT
Donald Trump Hillary
Clinton
Average Age 66.4
(Currently 70)
52.5
(currently 69)
Average Smile
(Intensity?)
0.93 0.96
Gend...
Thank You!
@_JAStark
Candidate Representation: A Preliminary Study
Candidate Representation: A Preliminary Study
Candidate Representation: A Preliminary Study
Candidate Representation: A Preliminary Study
Candidate Representation: A Preliminary Study
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Candidate Representation: A Preliminary Study

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Given at Women Data Scientists DC November 16th 2016. This is a preliminary study into the images surfaced in Google Search related to the US presidential candidates.

Published in: News & Politics
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Candidate Representation: A Preliminary Study

  1. 1. Candidate Representation: A Preliminary Study Jennifer A. Stark Ph.D. Nick Diakopoulos Ph.D. Computational Journalism Lab University of Maryland
  2. 2. Images & Elections • Competence inferred from facial appearance (Todorov et al., 2005) • Children (5-13 yrs) use same facial attributes to predict election outcomes (Antonakis, Delgas, 2009) • Beautiful candidates win votes (Berggren et al, 2010)
  3. 3. Gender Bias • Stereotype exaggeration and systematic underrepresentation of women in search results (Kay et al., 2015) • Sentiment of women in news: Happy, Calm, Submissive; • Sentiment of Men in news: Sad, Excited, Dominant (Referenced in Kwak, An, 2016) • Women are younger and smile more (Kwak, An, 2016)
  4. 4. Where is Google directing our attention? How did Google Image Box images represent each candidate? Which news sources delivering these messages did Google promote?
  5. 5. Plan Scrape data: Request timestamp Search query (‘donald trump’, ‘hillary clinton’) Image box Rank News link Image path Download image to image path Associate each image with each dataframe row Determine image frequency Determine News Source frequency Identify unique images Calculate sentiment for each unique image
  6. 6. Google Scraper from here, and customised Collection once per day, 8 weeks. Data stored in MySQL database on AWS.
  7. 7. Associate each image with each dataframe row: Image Hashing
  8. 8. Image Hashing
  9. 9. Image Hashing
  10. 10. Image Hashing
  11. 11. Determine News Source Frequency
  12. 12. Determine News Source of Each Image
  13. 13. Identify unique image file names
  14. 14. Click on an image; where do you go?!
  15. 15. Ugly and useless. Edit SVG in Adobe Illustrator...
  16. 16. What level of importance is each image/news source given? What is Image Rank?
  17. 17. Gender Bias? Microsoft Emotion & Face API
  18. 18. Loop through Emotion JSON files
  19. 19. Our Candidates’ Emotions: HRC DJT
  20. 20. Donald Trump Hillary Clinton Average Age 66.4 (Currently 70) 52.5 (currently 69) Average Smile (Intensity?) 0.93 0.96 Gender prototypical: Women are younger and smile more
  21. 21. Thank You! @_JAStark

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