Hackathon Solidaire 2022

Coding competition focused on gender inequalities in cinema. Organized by DataForGood and Eleven

This team was awarded the first place

The team:

• Martin Alexandre
• Camille Ballu
• Vincent Delale
• Thomas Kientz
• Alexandre Quéant
• Lucas Saban

The goal:

Given basic data about a movie (IMDb id), determine whether it will pass the Bechdel test or not. The classes to predict are as such:

Level	Description
0	(None of the above)
1	There are two women characters in the movie.
2	They talk to each other.
3	They talk to each other about something other than a man.

The data:

• We gathered data from The Movie DataBase (TMDB) to get info about actors, producers, writers and such.
• We used the movies' posters to determine the number of women on them and the relative size of women on the posters using DeepFace and more especially using RetinaNet Backend and FaceNet128.
• We used audio recognition on youtube videos of the movies' trailers to get the proportion of women's speech in them. Audio was scrapped using fixed pytube and the audio segmentation was done with inaSpeechSegmenter.
• We used NLP on the movies' synopsis and PCA analysis to get insights from the plots.

The model:

We used XGBoost and hyperparameters tuning in sklearn for the final model.

Results:

Confusion matrix of the XGBClassifier on the test set (20% of the 8000+ movies):

Most relevant features:

Feature name	Description	Importance
PCA_0	First vector in the PCA of the NLP analysis of the synopsis	0.05241
Is_War	Dummy variable - Is the movie a war movie ?	0.04633
writers_female	Number of female writers	0.04234
cast_female	Number of female actors	0.03934
area_women	Proportional area of womens' faces in the poster	0.03709
Is_Horror	Dummy variable - Is the movie a horror movie ?	0.03567
nb_women	Number of women on the poster	0.02856
Is_Romance	Dummy variable - Is the movie a romance movie ?	0.02456
cast_male	Number of male actors	0.02075