Fishing for Clickbaits in Social Images and Texts with Linguistically-Infused Neural Network Models

Understand

This paper presents the results and conclusions of our participation in the Clickbait Challenge 2017 on automatic clickbait detection in social media.

• We first describe linguistically-infused neural network models and identify informative representations to predict the level of clickbaiting present in Twitter posts.
• Our models allow to answer the question not only whether a post is a clickbait or not, but to what extent it is a clickbait post e.g., not at all, slightly, considerably, or heavily clickbaity using a score ranging from 0 to 1.
• We evaluate the predictive power of models trained on varied text and image representations extracted from tweets.