Fishing for Clickbaits in Social Images and Texts with Linguistically-Infused Neural Network Models
Table 2: Results of AdaBoost (AB) baselines and different combinations of models and inputs when trained on the 20k labeled dataset and tested on the 2k labeled dataset provided. Results are separated by model architecture used (AB, CNN, or LSTM) and sorted by the number of epochs used in training. Lowest MSE for each model is highlighted in bold. Text only models (using only the right sub-network of the LSTM or CNN model architectures shown above) are indicated with a − in the Vector Input column.
['Model', 'Text Input', 'Vector Input', '# epochs', 'MSE', 'RMSE', 'MAE']
After comparing the performance of predicting clickbait scores using different types of inputs, we found the highest performance from models that use the tweet as the text sequence input combined with the linguistic cues (LC) extracted from the tweet and the title, keywords, description, and paragraphs of the linked article. For each neural network model we report the results after training for 3, 5, 10, and 25 epochs. Results are ordered by model, inputs and the number of epochs. We proposed a neural network architecture that used the text of the tweet, the linguistic cues found in the tweet and associated article (e.g., the text of the tweet, article title, article keywords, article description, and article paragraphs) to compete in the Clickbait Challenge 2017. Our model for predicting a clickbait score for a given tweet performed as well or better than the other combinations of features and model architectures that we compared. Among the top-performing models, LSTM-based model also had the most reasonable run-time and computational requirements to be deployed in the challenge. The final test sample contained 18,979 instances with a 1:3.2 clickbait to not-clickbait ratio, similar to the ratio of 1:3.10 of the 20k labelled dataset.