Analysing the Extent of Misinformation in Cancer Related Tweets

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.

Happenstance: Utilizing Semantic Search to Track Russian State Media Narratives about the Russo-Ukrainian War on Reddit

“…However, despite the prevalence of this type of disinformation online, the research community still lacks program-matic approaches for tracking the spread of specific disinformation narratives-like those about Ukraine-across both news sites and social media platforms. Topic modeling tools like LDA fall short in mapping topics across platforms (Min et al 2015), and keyword-based approaches often rely on pre-existing expert knowledge of disinformation campaigns, which often cannot be distilled at the speed at which information campaigns are deployed (Bal et al 2020).…”

Drink Bleach or Do What Now? COVID-HeRA: A Study of Risk-Informed Health Decision Making in the Presence of COVID-19 Misinformation

“…Health-related misinformation research spans a broad range of disciplines including computer science, social science, journalism, psychology, and so on (Dhoju et al 2019;Castelo et al 2019;Fard and Lingeswaran 2020). While healthrelated misinformation is only a facet of misinformation research, there has been much work analysing misinformation in different medical domains, such as cancer (Bal et al 2020;Loeb et al 2019), orthodontics (Kılınç and Sayar 2019), sexually transmitted diseases and infections (Zimet et al 2013;Lohmann et al 2018;Joshi et al 2018;Tomaszewski et al 2021), autism (Baumer and McGee 2019), influenza (Culotta 2010;Signorini, Segre, and Polgreen 2011), and more recently COVID-19 (Garrett 2020;Brennen et al 2020;Cinelli et al 2020;Cui and Lee 2020). Social media data have also been used to monitor influenza prevalence and awareness (Smith et al 2016;Ji, Chun, and Geller 2013;Huang et al 2017).…”

“…While an increasing percentage of the population relies on social media platforms for news consumption, fake news and other types of misinformation have been also widely prevalent, putting audiences at great risk globally. Detecting and mitigating the impact of misinformation is therefore a crucial task that has attracted research interest, with a variety of approaches proposed, from linguistic indicators to deep learning models (Bal et al 2020). Fake news frequently emerges for certain phenomena and topics, e.g., public health issues, politics etc.…”

Prioritising Informed Health Choices Key Concepts for those impacted by cancer: a protocol

“…People are confronted almost daily with information or claims on social media about the effectiveness of specific treatments that may help maintain health or treat ill-health 12 , 13 . However, many of these claims are biased, inaccurate, or unsubstantiated, whether well-intentioned or motivated by commercial or other interests 11 , 14 . Unreliable information and claims about treatments may lead to inadequate or excessive health treatments, which may cause harm for the individual 11 and waste limited resources 12 .…”