  Deception is the intentional practice of twisting information. It is a
nuanced societal practice deeply intertwined with human societal evolution,
characterized by a multitude of facets. This research explores the problem of
deception through the lens of psychology, employing a framework that
categorizes deception into three forms: lies of omission, lies of commission,
and lies of influence. The primary focus of this study is specifically on
investigating only lies of omission. We propose a novel framework for deception
detection leveraging NLP techniques. We curated an annotated dataset of 876,784
samples by amalgamating a popular large-scale fake news dataset and scraped
news headlines from the Twitter handle of Times of India, a well-known Indian
news media house. Each sample has been labeled with four layers, namely: (i)
the type of omission (speculation, bias, distortion, sounds factual, and
opinion), (ii) colors of lies(black, white, etc), and (iii) the intention of
such lies (to influence, etc) (iv) topic of lies (political, educational,
religious, etc). We present a novel multi-task learning pipeline that leverages
the dataless merging of fine-tuned language models to address the deception
detection task mentioned earlier. Our proposed model achieved an F1 score of
0.87, demonstrating strong performance across all layers including the type,
color, intent, and topic aspects of deceptive content. Finally, our research
explores the relationship between lies of omission and propaganda techniques.
To accomplish this, we conducted an in-depth analysis, uncovering compelling
findings. For instance, our analysis revealed a significant correlation between
loaded language and opinion, shedding light on their interconnectedness. To
encourage further research in this field, we will be making the models and
dataset available with the MIT License, making it favorable for open-source
research.
