Visible Partisanship - Scholars at Harvard...Visible Partisanship Polmeth XXXIII, Rice University,...
Transcript of Visible Partisanship - Scholars at Harvard...Visible Partisanship Polmeth XXXIII, Rice University,...
Visible PartisanshipPolmeth XXXIII, Rice University, July 22, 2016Convolutional Neural Networks for the Analysis of Political Images
L. Jason Anastasopoulos [email protected] (University of Georgia, Public Admin + Policy, Political Science, Georgia Informatics Institute)Dhruvil Badani (UC Berkeley, EECS)Crystal Lee (UC Berkeley, EECS)Shiry Ginosar (UC Berkeley, EECS)
Outline
▪ Background
▪ Image experiment - “the people you pose with”– how race and gender of people politicians pose with affect perceptions.
▪ Race classifier for images using convolutional neural networks.
▪ Analysis of race in US House of Representative Facebook profile photos.
Visual semantics: image elements
▪ Symbols
▪ Objects
▪ People
▪ Poses
Images convey political meaning: symbols
Images convey political meaning: objects
Source - usa4palin.com: Still from Sarah Palin’s Amazing America
Images convey political meaning: people
Source - haaretz.com: Netanyahu (left), Obama (middle), Abbas (right)
Images convey political meaning: poses
Source - google.com: Image search for “John Boehner”
Political functions of images
For politicians…▪ Signaling
▪ Partisanship/ideology.▪ Policy positions.
▪ Homestyle (Fenno 1978)▪ Qualification – competence.▪ Identification – “I am one of you.”▪ Empathy – “I care about your needs.”
Political functions of images
For politicians…▪ Signaling
▪ Partisanship/ideology.▪ Policy positions.
▪ Homestyle (Fenno 1978)▪ Qualification – competence.▪ Identification – “I am one of you.”▪ Empathy – “I care about your needs.”
Political functions of imagesFor news media…
▪ Issue framing.▪ Persuasion.
Hardware and software limitations
▪ Hardware▪ Even small images are “big data.”
▪ One 200 x 200 image = 3 200x200 matrices or 1 vector of length 120,000.
Hardware and software limitations
▪ Software▪ High dimensional statistical theory developed more recently.
▪ Asymptotics deals with properties of estimators as with a fixed number of parameters, p.
▪ In modern machine learning applications,
Introductionclassical asymptotic theory: sample size n → +∞ with number ofparameters p fixed
modern applications in science and engineering:! large-scale problems: both p and n may be large (possibly p ≫ n)! need for high-dimensional theory that allows (n, p) → +∞
Introductionclassical asymptotic theory: sample size n → +∞ with number ofparameters p fixed
modern applications in science and engineering:! large-scale problems: both p and n may be large (possibly p ≫ n)! need for high-dimensional theory that allows (n, p) → +∞
Image analysis renaissance in social science
Hardware: Powerful CPUs and now GPUs in desktop computers (thanks gamers!)
Image analysis renaissance in social science
▪ Software▪ Statistical theory for computing in high
dimensions.
▪ Advances in numerical computing.
▪ Deep-learning frameworks: Torch, Tensorflow, Theano, Caffe.
Signaling and image features
▪ Symbols
▪ Objects
▪ People
▪ Poses
Signaling and image features
▪ Symbols
▪ Objects
▪ People
▪ Poses
Questions
▪ How do the group characteristics (gender, race, age, etc.) of people that Members of Congress pose with affect how they are perceived?
▪ Social media “homestyle”▪ Is there evidence that Members of Congress use social media images to signal
identification and empathy with constituents using group characteristics?
Questions
▪ How do the group characteristics (gender, race, age, etc.) of people that Members of Congress pose with affect how they are perceived?
▪ Social media “homestyle”▪ Is there evidence that Members of Congress use social media images to signal
identification and empathy with constituents using group characteristics?
“The people you pose with” experiment: Lou Barletta
▪ Lou Barletta (R-PA, 11) chosen for initial experiment because of relative obscurity and similar pictures with different groups of people.
▪ MTurk respondents randomly assigned one of 7 images with Barletta▪ Alone – Barletta by himself.
▪ Woman – Barletta with a woman.
▪ Man – Barletta with a white man.
▪ Black – Barletta with African-American men.
▪ Asked a series of questions based only on the image.
Image treatmentsAlone Man Woman Af. American
What is your best guess of the political party that this politician belongs to?Beliefs about Barletta’s party ID vary significantly by image shown.
Alone: 39% guessed Democrat61% guessed Republican
Black: 58% guessed Democrat42% guessed Republican
Man: 42% guessed Democrat58% guessed Republican
Woman: 43% guessed Democrat57% guessed Republican
What is your best guess of this politician’s ideological orientation?Average by treatment groups
Alone: Moderate.
Black: Liberal.
Man: Moderate.
Woman: Moderate.
Does this politician seem…honest and trustworthy?Perceived to be more trustworthy when pictured next to a woman.
Does the politician seem…like a strong and decisive leader?Perceived to be a stronger leader when pictured next to a woman.
Does the politician seem…knowledgeable about the issues?Perceived to be less knowledgeable when pictured next to an older white man.
Does the politician seem…like someone who shares my values? (non-white respondents)Perceived by non-white respondents to share their values when pictured next to African-American men.
Barletta experiment conclusions
▪ Opinion of Barletta affected by group identity of individuals included in images.
▪ Race affected beliefs about partisanship/ideology and implied “shared values.”
▪ Gender affected beliefs in trustworthiness, honesty and decisiveness.
▪ Survey experiment expanding – goal is to test which aspects of photos most strongly tied to perceptions of candidate ideology and party.
Questions
▪ How do the group characteristics (gender, race, age, etc.) of people that Members of Congress pose with affect how they are perceived?
▪ Social media “homestyle”▪ Is there evidence that Members of Congress use social media images to signal
identification and empathy with constituents using group characteristics?
Questions
▪ How do the group characteristics (gender, race, age, etc.) of people that Members of Congress pose with affect how they are perceived?
▪ Social media “homestyle”▪ Is there evidence that Members of Congress use social media images to signal
identification and empathy with constituents using group characteristics?
Data
300,000+ Facebook images with text posts for accounts of:
300 US House members.
56 US Senate members.
Goals and Methods
▪ Identify race of individuals pictured in Facebook profiles of House Members.▪ Viola-Jones Algorithm▪ Train a convolutional neural network race classifier.
▪ Explore how distribution of racial groups in photos compare to congressional district demographics, partisanship and ideology.▪ Compare Facebook profile “demographics” with district demographics, party id
and DW-Nominate scores.
Results
▪ Democrats and Republicans in the US House of Representatives have very different social media styles.
▪ Evidence that Democrats use Facebook images to elicit racial identification and empathy among constituents.
How you see an image…▪ Image as data.
▪ Eg 620x412 pixel image.
You see:{Donald Trump, blue tie, black suit, blue background, anger}
How a computer sees an image...▪ 640x412 pixel
image.
▪ 3 Channels: Red, Green, Blue
▪ 3 640x412matrices of pixel intensity values.
▪ -or- 3x640x412 = 791,040 x 1 vector
Human image classification is robust
Machine image classification is error prone...
Image Credit: Andrej Karpathy
Theoretical means of image feature extraction limited mostly to faces...
Viola-Jones Object Detection Framework
Data driven approach/supervised machine learning approach – train models utilizing pixel intensity data...
Collect labeled images.
Train a machine learning classifier.
Test classifier accuracy.
CIFAR-10 library of 32x32 labels images benchmark performance.
One layer neural network
X1
X2
X3
▪ Inputs multiplied by weights and added create “hidden” layer.
▪ Hidden layer passed through “activation function” multiplied by another set of weights to generate class probabilities/scores.
▪ Simplest model discussed by psychologist Rosenblatt (1958)
Neural network activation functions
▪ Most common activation functions are sigmoid and tangent.
▪ Optimizing predictions requires▪ Choice of activation
functions and;
▪ Choice of weights.
Backpropagation
▪ Rumelhart, Hinton and Williams (1986)
▪ Selection of weights involves:▪ Forward pass - Calculation of loss function.
▪ Backward pass – Use of chain rule and stochastic gradient descent to iteratively calculate new weights.
Convolutional neural networks for image feature classification
Multi-layer neural network involving series of activation functions on chunks of pixel data.
Convolutional neural networks for image feature classification• Equivalent of
passing pixel data through a number of “filters.”
• Discover which “filter” is activated by which labeled image category.
• Output is highest probability category given filter responses
Image credit: Andrej Karpathy
Convolutional neural network: building a race classifier
• Labeled image data
• 60,000 high school yearbook images, 1960-2013• 6,000 images sampled from Congressional Facebook dataset we collected.• Categories: White, African-American, East Asian, Hispanic.
• 16-layer CNN model for large-scale image recognition from CNN Model Zoo by Simonyan and Zisserman (2015): http://arxiv.org/pdf/1409.1556.pdf and https://gist.github.com/ksimonyan/211839e770f7b538e2d8#file-readme-md
Convolutional neural network: building a race classifier
• Step 1: Identify faces using Viola-Jones algorithm.
• Image on the right is a Facebook photo from Representative Tammy Duckworth ‘s (D-IL) profile.
Convolutional neural network: building a race classifier
• Step 2: Label race of faces.
Convolutional neural network: building a race classifier
• Step 3: Train CNN on labeled data.
Convolutional neural network: building a race classifier
• Step 4: Test classifier accuracy
• Avg. cross-validated accuracy rates of 90% for whites, 85% for African-American, 75% for Asian, 65% for HIspanic.
Convolutional neural network: building a race classifier
• Step 5: Estimate race of individuals in Congressional Facebook image set using trained model.
Race and partisanship in House Facebook image posts (white House members)
White Democrats post Facebook photos of …
African-Americans at 4x the rate of white Republicans
Hispanics at 1.2x the rate of white Republicans.
Asians at 2x the rate of white Republicans.
Race and partisanship in House Facebook image posts (white House members)Even conditional on relevant district demographics, state and regionfixed effects, evidence of conscious efforts by partisans to include/exclude racial groups in Facebook image posts.
Democrats (white): +6% more African-Americans in posts.
Republicans (white): +6% more whites in posts.
Identification and empathy: district demographics and Facebook image posts
Do MCs strategically post photos of racial groups to engender identification and empathy from constituents?
Overall strong evidence that they do.
Strong relationship between % of racial group in district and % of racial group posted in Facebook profiles.
Identification and empathy: district demographics and Facebook image posts by party
Strategic use of race in image posts much more evident among Democrats than Republicans
Identification and empathy: district demographics and Facebook image posts by party
Y = % white in Facebook profile photos
White Democrats more “race conscious” whenposting FB photos.
After conditioning on state and region fixed effects and district demographics, Democrats Facebook photos more likely to reflectracial/ethnic mix of district.
Identification and empathy: district demographics and Facebook image posts by party
Representation = % White in Facebook profile photos –% White in Congressional District
Whites over-represented in Facebook photosof white Democrats and Republicans…
Identification and empathy: district demographics and Facebook image posts by party
Representation = % Black in Facebook profile photos –% Black in Congressional District
African-Americans under-representedin Facebook photos of Republican MCsby an average of about 3.8%
Identification and empathy: district demographics and Facebook image posts by party
Representation = % Hispanic in Facebook profile photos% Hispanic in Congressional District
Hispanics under-representedIn Facebook photos of both parties, more so among Democrats
Identification and empathy: district demographics and Facebook image posts by party
Representation = % Asian in Facebook profile photos% Asian in Congressional District
Asians under-represented in Facebookphotos of white Democrats.
Discussion
• Modern computational methods allow for the large scale analysis of images.
• Here we build a race classifier for images using convolutional neural networks.
Discussion
▪ Characteristics of people that politicians pose with shape perceptions.
▪ Democrats and Republicans in the US House of Representatives have very different social media styles.
▪ Evidence that Democrats use Facebook images to elicit racial identification and empathy among constituents.