FBAdLibrarian and Pykognition: open science tools for the collection and emotion detection of images in Facebook political ads with computer vision

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

Cross-Platform Emotions and Audience Engagement in Social Media Political Campaigning : Comparing Candidates’ Facebook and Instagram Images in the 2020 US Election

This study provides a cross-platform, longitudinal investigation of pictures depicting political candidates posted to Facebook and Instagram over a 15-month period during the 2020 US election(n = 4,977). After motivating an exploratory research design, we set out to expound: the extent of cross-platform image posting across Facebook and Instagram; the emotion expression of politicians across the two platforms; and the relationship between these emotions and post performance. Our analysis of eight political campaigns (seven Democratic challengers and the Republican incumbent) finds relatively high and stable levels of cross-posting candidate pictures across the two platforms. The exception is the incumbent campaign, where cross-posting activity rose in proximity to the primary elections.Regarding emotions, we utilize both computer vision and crowd coding to identify happiness as the dominant emotion on Facebook and Instagram. Overall, we detect little variation in candidate emotionexpressions – across campaigns and across platforms. However, we do find differences in how platform audiences respond to emotions, proxied here through post performance. Results from binomial logistic regressions show that in comparison with Calm, posts exhibiting Anger are less likely to overperform on both Facebook and Instagram. Most interestingly, we find diverging patterns for Happiness, which performs better than Calm on Instagram but not on Facebook. We interpret these findings to suggest first, that Instagram users reward emotionality from politicians. Second andmore importantly, we argue that differing audience responses to emotions – captured through social media metrics – may reveal a generation polarization in what different segments of the electorateprefer their political leaders to be

FBAdLibrarian

The FBAdLibrarian is a simple, command line tool that archives images from unique hyperlinks offered by the Facebook Ad Library API.At the time of writing, Facebook permits the downloading of individual ad creatives such as images for academic research purposes. The Librarian assists researchers with this process. Users must have verified their identity with Facebook, received the appropriate access tokens for the API, and use this tool only for academic research purposes.The Librarian works as follows. First, the Librarian takes the ad_snapshot_url of an ad and creates a unique hyperlink, using the researcher's access tokens. Then, the Librarian looks up each ad individually. If the ad includes an image, the Librarian saves the image to an output folder and names the image according to the post’s unique ad identification number (or "adlib_id"). If the ad includes an embedded video, the Librarian will pass over the ad, but it will document the adlib_id as a video in an accompanying "metadata.txt" file.PrerequisitesPython 3 (Currently tested on 3.7 but might work on other versions)Verified access to Facebook Ad LibraryOutput data from Facebook Ad Library in xlsx format with semi-colon seperator (other formats and seperators are not supported as of now

Pykognition

Python wrapper for AWS Rekognition APIPykognition is a Python wrapper for the Amazon Web Service (AWS) Rekognition API, which provides industry-grade face and emotion detection. For facial detection, the algorithm provides a score for the predicted probability that the image includes a face (or multiple faces). Each face is categorized in a FaceDetail object, which carries a host of metadata such as predicted age, gender, and the emotion predicted to be displayed by each face.The emotion classifications provided by the algorithm are: Happy, Sad, Angry, Confused, Disgusted, Surprised, Calm, Fear, and Unknown. Each emotion classification is accompanied by a confidence score ranging up to 99.9%.Pykognition simplifies the process of classifying images with the Rekognition API. Once the researcher establishes an AWS account, they only need to insert their access tokens and an input path where the images are stored. The ‘ifa’ function (short for Image Face Analysis) sends images for classification to the Rekognition API and returns both the classification and metadata.PrerequisitesPython 3 (Is tested on 3.7 but might work on other versions)Access to AWS Rekogntion AP

Constrained Excited-State Structure in Molecular Crystals by Means of the QM/MM Approach: Toward the Prediction of Photocrystallographic Results

The QM/MM method is applied to predict the excited triplet structure of a molecule embedded in a crystal. In agreement with experimental observation, it is found that conformation changes on excitation are severely restricted compared with geometry changes predicted for the isolated molecule. The results are of importance for understanding the photophysical properties of molecular solids