I watch a lot of YouTube. In fact, a lot of younger people watch more YouTube than they watch television. If you’re like me, YouTube is not only an endless source of entertainment, but it also serves as a source of knowledge and information. The platform has videos on just about anything from how to make your own fermented food, cracking the Sega Saturn or Joe Rogan interviewing himself. However, if you have been on the platform for as long as I have, you have probably caught on by now just how much clickbait YouTube creators use. For example, creators structure their video thumbnails a particular way to catch the audiences’ attention and the same thing can be said for the titles. This helps with getting their videos picked up by the search engine optimization algorithm.
As a consequence of content creators engaging in these practices, YouTube and its community has unintentionally developed its own unique style and language. Motivated by curiosity, I thought about what it would be like to train an image captioning model based on this strange internet subculture. Using LSTMs and an encoder-decoder model, I trained an image caption model on YouTube thumbnails and their titles to generate new titles. The code can be found on GitHub.
Getting the data
If we want to get a good representation of what YouTube represents today, we look no further than the trending videos found on its homepage. The trending page is essentially the top
watched videos on the platform. YouTube does provide its own API to obtain metadata for its videos, but you’re limited to making a limited number of requests. Doing a quick Google
search, I found an already compiled Kaggle dataset which has trending videos based on different regions. For this project,
I used US videos which have a total of
40,949 records. Not a lot to work with, but we make do.
To caption the images, I used LSTMs with an encoder-decoder model. Model architecture and generation code was adapted from a tutorial on Machine Learning Mastery.
Image features were generated using VGG16 and the titles were minimal preprocessed by just removing punctuation and words shorter than one character.
Stopwords were left in. The vocabulary size was
7,788 words. The dataset was split into a
70/30 test/train ratio and the model was trained for
10 epochs using Adam as our optimizer. During training, the loss was not decreasing after
3 epochs, hence why the model was not trained for long.
After training the model, captions were generated for all thumbnails and compared to the original titles. Immediately we notice something strange…
For a given thumbnail and starting word, we choose the next most probable following word. In our models case, we see that one word has a higher probability of all other words. If we want to generate less non-sensical answers, we need to create some heuristics and hope for the best. Instead of choosing the next most probable word, let’s consider randomly choosing uniformly over all words. In addition, another heuristic we should consider is to eliminate words appearing twice. This will help to make our results appear like proper sentences or titles. Applying these heuristics, we get results that are interesting to say the least.
What originally got me interested about doing this project was just how downright nonsensical video titles can be as they can be clickbaity and contain a lot of internet slang. It’s great to see some of the generated captions contain some of these words.
Another thing we notice is that a lot of videos contain terminology used for music videos or movie trailers, eg. official, hd, music and trailer. This poses a question of just how much YouTube’s trending videos contain trailers and music videos compared to actual videos by content creators.
Despite some of the generated captions being nonsensical, they don’t seem that out of the ordinary. In fact, perhaps if we limit our scope to a few categories of videos and increase the size of our data, we would get some decent results.
Experimenting with an image caption generation model with YouTube thumbnails shows some promise, however the results could be better. A couple of reasons for these not-so-good results being a lack of data and an imbalance of different categories of videos. Some other things we can consider in order to improve this model can be:
Getting better data
For this project, we want to consider only english videos, which are the United States (US), Canada (CA) and Great Britain (GB) regions. Below is a breakdown of the records in each dataset:
- US videos:
- CA videos:
- GB videos:
Eliminating duplicates among all datasets, this gives us a total of
111,394 records. This isn’t a bad number of videos, however not all of them have English only titles.
So for the sake of simplicity, I proceeded with training on just US videos.
Adding more heuristics the text generation process
Specifically I’m talking about how to make the text generated appear as real titles. One possible way would be to incorporate part-of-speech tagging to the generation. This can add some overall structure to the titles, therefore making them appear less nonsensical.
Using more sophisticated models
Attention models are big right now for image captioning. However your model is only as good as the data you train it on, and if we’re only working with
most of which are non-sensical in the first place, attention models might not give us fantastic results, but it would be interesting nonetheless.
Some other things we can do is to limit the categories of the videos used for training. If we happen to create a decent caption generation, then we would like to validate our captions using something like a BLEU score.
YouTube’s titles are not coherent sentences in the first place. In fact, some of them, for example music videos or movie trailers are just key words put together, therefore our crude sentence generator has lended itself very nicely for this problem.