COVID -19 and Misinformation: A Large-Scale Lexical Analysis on T witter (2021)

Abstract: Social media is often used by individuals and organisations as a platform to spread misinformation. With the recent coronavirus pandemic we have seen a surge of misinformation on Twitter, posing a danger to public health. In this paper, we compile a large COVID-19 Twitter misinformation corpus and perform an analysis to discover patterns with respect to vocabulary usage. Among others, our analysis reveals that the variety of topics and vocabulary usage are considerably more limited and negative in tweets related to misinformation than in randomly extracted tweets. In addition to our qualitative analysis, our experimental results show that a simple linear model based only on lexical features is effective in identifying misinformation-related tweets (with accuracy over 80%), providing evidence to the fact that the vocabulary used in misinformation largely differs from generic tweets.

## introduction social media has created a landscape where vast amounts of information on various topics is shared daily between users all around the world. unfortunately, not all information shared is legitimate. as seen in recent events such as the brexit referendum in the uk @xcite and the 2016 us presidential election @xcite , there are many cases where people, either unintentionally or deliberately @xcite , share unreliable information which causes confusion and suspicion in the general population. for instance, individuals and organisations share 'facts' on how the earth is flat, that vaccines cause autism, or that chlorine is treatment against covid-19. the spread of misinformation through social networks is made easier by the structure of these platforms. by personalising their users' news feeds and creating echo chambers, where users share believes and biases, social media provide the perfect field for spreading misinformation. moreover, the fact that most social media platforms either do not filter misinformation or filter it inefficiently @xcite means that there is no essential check on what people share online. examples of misinformation include fabricated content, where the information is completely false; manipulated content, where there has been some distortion of genuine information; and imposter content, where someone is impersonating genuine sources (publications.parliament.uk, 2018). even though misinformation spread is not only related to scientific facts, health related misinformation holds an immediate danger to the public @xcite . specifically, public health misinformation can be defined as a health-related claim that is currently unsupported by scientific evidence, with detrimental effects on public health @xcite . along with the recent emergence of the covid-19 pandemic, a number of conspiracy theories have arisen in social media; from fake and dangerous treatments to schemes that the virus is a part of a plan of the global elite to take over the world @xcite . the main aim of this paper is to explore whether there is a recognisable difference in the vocabulary usage between tweets conveying misinformation and random tweets present within covid-19 discourse. to this end, we collected two corpora, one corpus consisting of misinformation-related tweets and a balancing corpus consisting of 'generic' (i.e., randomly-selected tweets) where we ran a comparative analysis. this analysis is complemented with a machine learning experiment in which we analyse to what extent misinformation-related tweets can be retrieved by using lexical features only. ## lexical analysis of covid-19 misinformation tweets in this section, we describe our corpus collection efforts (section 2.1) and provide a qualitative analysis on the same collected corpus (section 2.2). ## identifying covid-19 related misinformation tweets upon collecting our dataset we aimed to explore whether the lexical features of tweets can provide a strong signal for identifying misinformation. to test our hypothesis, we built multiple models using different classification approaches based on lexical features to distinguish the misinformation-related and generic sets of tweets. ## conclusion in this paper, we have presented an analysis on the lexical features present in misinformation about covid-19 in social media, and compare it with those present in generic or random tweets. to this end, we compiled two different twitter corpora from early 2020 when the pandemic emerged. our analysis shows that there is a clear distinction in the general vocabulary used in each type of corpus and that a simple linear classifier based on lexical features can retrieve misinformation-related tweets to a high degree of accuracy. while this paper represents an initial reference point in this aspect, further analysis would be required to investigate the main features present in misinformation. on this respect, our work can also be added to the increasing evidence that shows that misinformation focuses on a specific vocabulary that does not reflect on the overall distribution of what can be found in general social media content for a certain topic @xcite . finally, it would be interesting to evaluate and compare the models' performance on other datasets that are manually labelled and are not collected based on the "call out" principle @xcite .