Misinformation detection using multitask learning with mutual learning for novelty detection and emotion recognition
Abstract
Fake news or misinformation is the information or stories intentionally created to deceive or mislead the readers. Nowadays, social media platforms have become the ripe grounds for misinformation, spreading them in a few minutes, which led to chaos, panic, and potential health hazards among people.
The rapid dissemination and a prolific rise in the spread of fake news and misinformation create the most time-critical challenges for the Natural Language Processing (NLP) community. Relevant literature reveals that the presence of an element of surprise in the story is a strong driving force for the rapid dissemination of misinformation, which attracts immediate attention and invokes strong emotional stimulus in the reader.
False stories or fake information are written to arouse interest and activate the emotions of people to spread it. Thus, false stories have a higher level of novelty and emotional content than true stories.
Hence, Novelty of the news item and recognizing the Emotional state of the read