Analysing the Extent of Misinformation in Cancer Related Tweets

TL;DR

This paper investigates the spread of misinformation about cancer on Twitter by collecting data, developing an attention-based deep learning model for detection, and analyzing linguistic differences between misinformation and truthful content.

Contribution

It introduces a new dataset of cancer-related tweets, proposes a novel deep learning model for misinformation detection, and provides insights into linguistic variations in social media misinformation.

Findings

Effective detection of cancer misinformation using deep learning.

Identification of linguistic patterns distinguishing misinformation from truthful tweets.

Insights into social aspects influencing misinformation spread.

Abstract

Twitter has become one of the most sought after places to discuss a wide variety of topics, including medically relevant issues such as cancer. This helps spread awareness regarding the various causes, cures and prevention methods of cancer. However, no proper analysis has been performed, which discusses the validity of such claims. In this work, we aim to tackle the misinformation spread in such platforms. We collect and present a dataset regarding tweets which talk specifically about cancer and propose an attention-based deep learning model for automated detection of misinformation along with its spread. We then do a comparative analysis of the linguistic variation in the text corresponding to misinformation and truth. This analysis helps us gather relevant insights on various social aspects related to misinformed tweets.