Covid or not Covid? Topic Shift in Information Cascades on T witter (2020)

Abstract: Social media have become a valuable source of information. However, its power to shape public opinion can be dangerous, especially in the case of misinformation. The existing studies on misinformation detection hypothesise that the initial message is fake. In contrast, we focus on information distortion occurring in cascades as the initial message is quoted or receives a reply. We show a significant topic shift in information cascades on Twitter during the Covid-19 pandemic providing valuable insights for the automatic analysis of information distortion.

## introduction social media is a valuable resource for all sorts of information. however, its power to shape public opinion can provoke serious societal issues such as misinformation. words or actions by a public figure (pf) generate 69% of misinformation in discussions with ordinary users, while pfs themselves are responsible for 20% of the messages containing distorted information @xcite . pfs post tweets that are likely to be shared by their followers @xcite , thus generating information cascades. the periodic repetitions provoke the mutability of information diffusion in the political domain @xcite . recent studies show a similarity of distorted information dissemination during the pandemic to the distribution of political misinformation @xcite . the repetitions of rumours about conspiracy theories associated with covid-19 led to the mutability of information; nevertheless, many users ridiculed these theories while repeating the rumors @xcite . during the covid-19 pandemic, users look for medical information in pf feeds and follow personal stories of infected people who share unverified information because complicated medical texts deter lay readers @xcite . mass medical information sharing generates cascades where the probability to distort initial information increases due to omissions and paraphrases. as medical discourse is sensitive to any changes in terminology and text structure made by incompetent people @xcite , the impact of medical misinformation on social behaviour means that there is a pressing need to understand how it circulates on social media. to reveal crucial issues about covid-19 that are of importance for lay people we need to understand topic shifts occurring within information cascades about the pandemic. such understanding allows us to discover a particular lack of medical information and demand for clear explanation of the most important public problems of the current pandemic. in this paper, we present a preliminary study on medical information distortion occurring in cascades on twitter due to topic shift. several studies have focused on misinformation during the covid-19 pandemic @xcite @xcite @xcite @xcite , but to the best of our knowledge, they assume that the initial message in a cascade is fake and do not study the mechanism of medical information distortion. we aim to answer two research questions: rq1: what are pf tweets on healthcare topics that generate information cascades? rq2: how does a transformation of the initial tweet involve misinformation? ## materials and methods for detection of topic shift and information distortion we collected 10m tweets in english about the controversy surrounding covid-19 medical treatment published between 30/03/20-13/07/20 by querying twitter api with the keywords, such as [chloroquine, hydroxychloroquine, hcq, hydroxychloroquinum, azithromicyn, raoult, remdesivir, tocilizumab] foot_0 @xcite . the data contained 141,866 original tweets, the rest are retweets foot_1 . as we focused on the analysis of information cascades (2 * 10 4 ), we only considered a subset of the dataset. first, we determined the initial tweets of the cascades among the union of 10 3 the most retweeted and 10 3 the most quoted tweets (1,356 unique tweet ids). then we added cascades hops, i.e. tweets with fields quoted status.id or in reply to status id containing initial tweet ids. the maximal cascade depth with the initial tweet in the resulting dataset is 10 (see examples in fig. 1 ). for further analysis, we considered the field text. we analysed topic shift within information cascades by comparing (1) two neighbouring hops within a cascade ∆ (i-1) (fig. 2(a) ) and (2) each hop within a cascade with the initial tweet ∆ (0) (fig. 2(b) ). to analyse topic shift, we encode tweets with the state-of-the-art sentence embedding model use (universal sentence encoder) @xcite . then, we computed the cosine similarity between use embeddings @xcite and transformed it into distance by subtracting the obtained values from 1. take the red pill initial @elonmusk is the red pill made by the same manufacturers as hydroxychloroquine? maybe you should take one of these instead: https://t.co/z1kklos9xe ∆ (i-1) = 0.51, ∆ (0) = 0.51 1st hop oh! hydroxychloroquine comes in a somewhat red blister pack. thanks @elonmusk https://t.co/kh7uupksuv ∆ (i-1) = 0.8, ∆ (0) = 0.8 1st hop @koyczan @elonmusk the thing is, hydroxychloroquine has been shown with a 98% effective rate to lessen symptoms and keep people from dying by the latest french study of over 1000 patients. (not the earlier incomplete study that the trump haters all jumped on. they would rather see people die.) it appears this dimwit has not gotten the memo. to identify information distortion types in cascades, we manually performed semantic analysis of tweet content. we examined key term distribution in cascades, explored their context in tweets and verified logical relations among medical terminology (see our analysis also leans on topic modelling. we used latent dirichlet allocation, lda @xcite from the scikit-learn tool. as tweets are short, we considered only the first topic. (abd-alrazaq et al., 2020) distinguish four main discussion themes on twitter during the current pandemic: origin of the virus; its sources; its impact on people, countries, and the economy; and ways of mitigating the risk of infection. this set lacks medical disease description and ways to treat covid-19 (symptoms, diagnosis, drugs, etc). thus, we were also interested in references to other disease related terms (drt) within cascades, as they can indicate distortion. to examine them, we extracted a list of hyponyms of the word disease from wordnet corpus accessed via nltk library, to which we added terms like plague, swine.flu, bird.flu, hiv, malaria, cough, wuhanflu, sars, cardiac.disease, china.flu, covid, coronovirus, cancer, obesity, diabete. we checked the appearance of these terms in the texts. in addition, we investigated the context in which these drts were mentioned such as: hydroxychloroquine (hcq), symptom, treatment, prevention, propagation, study, complication, epidemic, side effect, risk group, synonyms, plague reference, other issues. each context is defined by a set of terms (see ## results we identified the top-10 words characterising the first topic of each hop using lda. we represented each hop as a binary vector built over the words of all hops. for visualisation, we applied principle component analysis (pca) @xcite with two variables (see fig. 3 ). note that the first three hops are rather distant from the initial tweet, while the fourth hop is quite close to the initial tweet (its role is not clear yet). based on our analysis, 3,939 out of 21,585 unique tweets of cascades contain drts. information cascades are rarely evoked by healthcare professionals (hs) (identified by their profile information) since they are less active on twitter than politicians. hs quotes are often misinterpreted and their research results are misrepresented. though most of the cascades in which hs took part were initiated by journalists, hs tweets were able to terminate an information cascade foot_3 by providing relevant information and ending discussion. cascade terminators often come from the professional community @xcite . in fig. 2b , distances between initial tweets and the last hops of cascades show essential semantic differences revealing topic shifts. hops in 'heads' of deep cascades are closer to their initial tweets than those in 'tails'. as distances between neighbouring hops show more similarity to each other than to their initial tweet (fig. 2a ), figure 3 : pca on the first lda topic hops accumulate information mutability. the last hop is able to exhaust the cascade new topic. medical information is distorted via erroneous logical conclusions and mental operations of oversimplification, overgeneralisation, exaggeration, substitution, omission of facts, insertion of erroneous conclusions, misuse of medical concepts and distortion of their connections. the instances of the distortions occur in comments on the initial tweet and hops of cascades generated by the pf tweets. in fragments of cascades generated by comments on pf tweets in fig. 1 , distortion and misinformation appear due to oversimplification and distortion of logical links. omission of facts is connected to oversimplification: hcq efficacy in the covid-19 treatment depends on patient anamnesis. misinformation appears when a user did not provide a link to results of a french study he referred to while reacting on a red pill. the red pill meme reveals an unpleasant truth and is derived from a scene in the film the matrix; an insertion of an erroneous conclusion occurred in comments where red pill is associated with hcq. ordinary users exaggerate consequences of government decisions. they politicise and criminalise these actions shifting the topic to political and business disputes (rt @tribeforfreedom: cuomo is dedicated to a vaccine (bill gates) so he does not allow the use of hydroxychloroquine). overgeneralisation often appears in references to a personal experience when a single fact was considered as a trend foot_4 . ## conclusions a topic shift is like the broken telephone effect @xcite when the message is altered during transmission, a typical cascade feature @xcite . thus, we showed that through this effect, pfs influence misinformation distribution on twitter regardless of the quality of the information in their initial tweet. users consider hss as sources of 'raw information', which needs pf evaluation and approval. an interesting finding is that medical experts were able to stop the development of cascades by providing their factual and knowledgeable opinion. intellectuals have the most influence on ordinary users' evaluation of the drugs efficacy research that is similar to the results of the cascades study in @xcite . we see the effect in the cascade evoked by comments on musk's tweet. in the contexts of drts, we discovered the instances of medical information distortion. in contrast to previous works mainly focused on the initial spreading of fake news @xcite , here we clarified the mechanism of the medical information distortion during the covid-19 pandemic by analysing topic shifts within cascades. usually, the medical topic is shifted to political and business disputes. we showed that cascade hops accumulate mutability of information. we found that after a noticeable topic shift occurring in the first 3 hops, there is a return to the original topic. through context analysis, we improved the list of topics of @xcite adding those that are sensitive to medical information distortion. our analysis provides valuable insights for the automatic detection and classification of medical information distortion.