FBAdLibrarian and Pykognition: open science tools for the collection and emotion detection of images in Facebook political ads with computer vision
(2022) In Journal of Information Technology & Politics 19(1). p.118-128- Abstract
- We present a methodological workflow using two open science tools that we developed. The first, FBAdLibrian, collects images from the Facebook Ad Library. The second, Pykognition, simplifies facial and emotion detection in images using computer vision. We provide a methodological workflow for using these tools and apply them to a case study of the 2020 US primary elections. We find that unique images of campaigning candidates are only a fraction (<.1%) of overall ads. Furthermore, we find that candidates most often display happiness and calm in their facial displays, and they rarely attack opponents in image-based ads from their official Facebook pages. When candidates do attack, opponents are portrayed as displaying emotions such as... (More)
- We present a methodological workflow using two open science tools that we developed. The first, FBAdLibrian, collects images from the Facebook Ad Library. The second, Pykognition, simplifies facial and emotion detection in images using computer vision. We provide a methodological workflow for using these tools and apply them to a case study of the 2020 US primary elections. We find that unique images of campaigning candidates are only a fraction (<.1%) of overall ads. Furthermore, we find that candidates most often display happiness and calm in their facial displays, and they rarely attack opponents in image-based ads from their official Facebook pages. When candidates do attack, opponents are portrayed as displaying emotions such as anger, sadness, and fear. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/71cab88c-5252-4036-a160-4efa509a6e9a
- author
- Schmøkel, Rasmus and Bossetta, Michael LU
- organization
- publishing date
- 2022
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- computer vision, emotions, visual political communicaton, political communication, digital methods, computational social science, political campaigning
- in
- Journal of Information Technology & Politics
- volume
- 19
- issue
- 1
- pages
- 118 - 128
- publisher
- Taylor & Francis
- external identifiers
-
- scopus:85107333959
- ISSN
- 1933-1681
- DOI
- 10.1080/19331681.2021.1928579
- language
- English
- LU publication?
- yes
- id
- 71cab88c-5252-4036-a160-4efa509a6e9a
- date added to LUP
- 2021-05-31 16:46:50
- date last changed
- 2024-03-08 13:19:33
@article{71cab88c-5252-4036-a160-4efa509a6e9a, abstract = {{We present a methodological workflow using two open science tools that we developed. The first, FBAdLibrian, collects images from the Facebook Ad Library. The second, Pykognition, simplifies facial and emotion detection in images using computer vision. We provide a methodological workflow for using these tools and apply them to a case study of the 2020 US primary elections. We find that unique images of campaigning candidates are only a fraction (<.1%) of overall ads. Furthermore, we find that candidates most often display happiness and calm in their facial displays, and they rarely attack opponents in image-based ads from their official Facebook pages. When candidates do attack, opponents are portrayed as displaying emotions such as anger, sadness, and fear.}}, author = {{Schmøkel, Rasmus and Bossetta, Michael}}, issn = {{1933-1681}}, keywords = {{computer vision; emotions; visual political communicaton; political communication; digital methods; computational social science; political campaigning}}, language = {{eng}}, number = {{1}}, pages = {{118--128}}, publisher = {{Taylor & Francis}}, series = {{Journal of Information Technology & Politics}}, title = {{FBAdLibrarian and Pykognition: open science tools for the collection and emotion detection of images in Facebook political ads with computer vision}}, url = {{https://lup.lub.lu.se/search/files/98481312/FBAdLibrarian_and_Pykognition_open_science_tools_for_the_collection_and_emotion_detection_of_images_in_Facebook_political_ads_with_computer_vision.pdf}}, doi = {{10.1080/19331681.2021.1928579}}, volume = {{19}}, year = {{2022}}, }