  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. Our best performing model that
relies on the tweet text and linguistic markers of biased language extracted
from the tweet and the corresponding page yields mean squared error (MSE) of
0.04, mean absolute error (MAE) of 0.16 and R2 of 0.43 on the held-out test
data. For the binary classification setup (clickbait vs. non-clickbait), our
model achieved F1 score of 0.69. We have not found that image representations
combined with text yield significant performance improvement yet. Nevertheless,
this work is the first to present preliminary analysis of objects extracted
using Google Tensorflow object detection API from images in clickbait vs.
non-clickbait Twitter posts. Finally, we outline several steps to improve model
performance as a part of the future work.
