A Dashboard for Mitigating the COVID -19 Misinfodemic (2021)

Abstract: This paper describes the current milestones achieved in our ongoing project that aims to understand the surveillance of, impact of and intervention on COVID-19 misinfodemic on Twitter. Specifically, it introduces a public dashboard which, in addition to displaying case counts in an interactive map and a navigational panel, also provides some unique features not found in other places. Particularly, the dashboard uses a curated catalog of COVID-19 related facts and debunks of misinformation, and it displays the most prevalent information from the catalog among Twitter users in user-selected U.S. geographic regions. The paper explains how to use BERT models to match tweets with the facts and misinformation and to detect their stance towards such information. The paper also discusses the results of preliminary experiments on analyzing the spatio-temporal spread of misinformation.

## introduction alongside the covid-19 pandemic, there is a raging global misinfodemic @xcite just as deadly. as fear grows, false information related to the pandemic goes viral on social media and threatens to affect an overwhelmed population. such misinformation misleads the public on how the virus is transmitted, how authorities and people are responding to the pandemic, as well as its symptoms, treatments, and so on. this onslaught exacerbates the vicious impact of the virus, as the misinformation drowns out credible information, interferes with measures to contain the outbreak, depletes resources needed by those at risk, and overloads the health care system. although health misinformation is not new @xcite , such a dangerous interplay between a pandemic and a misinfodemic is unprecedented. it calls for studying not only the outbreak but also its related misinformation; the fight on these two fronts must go hand-in-hand. this demo paper describes the current milestones achieved in our ongoing project that aims to understand the surveillance of, impact of, and effective interventions against the covid-19 misinfodemic. 1) for surveillance, we seek to discover the patterns by which different types of covid-19 misinformation spread. 2) to understand the impact of misinformation, we aim to compare the spreading of the sars-cov-2 virus and misinformation and derive their correlations. 3) to understand what types of interventions are effective in containing misinformation, we will contrast the spreading of misinformation before and after debunking efforts. 4) to understand whether the outcomes related to 1), 2) and 3) differ by geographical locations and demographic groups, we will study the variability of misinformation and debunking efforts across geographical and demographic groups. while we continue to pursue these directions, we have built an online dashboard at https:// idir.uta.edu/covid-19/ to directly benefit the public. a screencast video of the dashboard is at bit.ly/3c6v5xf. the dashboard provides a map, a navigation panel, and timeline charts for looking up numbers of cases, deaths, and recoveries, similar to a number of covid-19 tracking dashboards. 123 however, our dashboard also provides several features not found in other places. 1) it displays the most prevalent factual information among twitter users in any user-selected u.s. geographic region. the codebase of the dashboard's frontend, backend, and data collection tools are opensourced at https://github.com/idirlab/covid19 . all collected data are at https://github.com/ idirlab/covid19data . particularly, the catalog and taxonomy of facts are also available through a sparql endpoint at https://cokn.org/ deliverables/7-covid19-kg/ and the corresponding rdf dataset can be requested there. what is particularly worth noting about the underlying implementation of the dashboard is the adaptation of state-of-the-art textual semantic similarity and stance detection models. tweets are first passed through a claim-matching model, which selects the tweets that semantically match the facts in our catalog. then, the stance detection model determines whether the tweets agree with, disagree with, or merely discuss these facts. this enables us to pinpoint pieces of misinformation (i.e., tweets that disagree with known facts) and analyze their spread. a few studies analyzed and quantified the spread of covid-19 misinformation on twitter @xcite ; memon and carley, 2020; al-rakhami and al-amri, 2020) and other social media platforms @xcite . however, these studies conducted mostly manual inspection of small datasets, while our system automatically sifts through millions of tweets and matches tweets with our catalog of facts. ## the dashboard figure 1 shows the dashboard's user interface, with its components highlighted. ## the datasets the dashboard uses the following three datasets. the manually curated catalog currently has 9,512 entries from 21 credible websites, including statements from authoritative organizations (e.g., who, cdc), verdicts, debunks, and explanations of factual claims (of which the truthfulness varies) from fact-checking websites (e.g., the ifcn coronavirusfacts alliance database, 8 politifact), and faqs both from credible sources (e.g., fda, nyt) and a dataset curated by @xcite . we organized the entries in this catalog into a taxonomy of categories, by integrating and consolidating the available categories from a number of source websites, placing entries from other websites into these categories or creating new categories, and organizing the categories into a hierarchical structure based on their inclusion relationship. the taxonomy is as follows, in the format of {level-1 categories [level-2 categories (level-3 categories)]}: coronavirus cannot be passed by dogs or cats but they can test positive. there has been no evidence that pets such as dogs or cats can spread the coronavirus. animals, spreading 0.817 agree more people die from the flu in the u.s. in 1 day than have died of the coronavirus across the world ever. right now, it appears that covid-19, the disease caused by the new coronavirus, causes more cases of severe disease and more deaths than the seasonal flu. cases 0.816 disagree ## matching tweets with facts and stance detection given the catalog of covid-19 related facts f and the tweets t , we first employ claim-matching to locate a set of tweets t @xmath0. next, we apply stance detection on pairs p @xmath1, f ) | @xmath2, disagreeing with, or neutrally discussing f . finally, aggregate results are displayed on component 4 of the dashboard to summarize the public's view on each fact. figure 2 depicts the overall claim-matching 10 https://virtuoso.openlinksw.com/ and stance detection pipeline. for both tasks, we employed bidirectional encoder representations from transformers (bert) @xcite . given r t,f , we model claim-matching as a ranking task on the relevance between facts and tweets. thus, the output of this stage is t @xmath3,@xmath4, where the threshold θ is 0.8 in our implementation. stance detection. given t f , we detect the stance that each tweet t takes toward fact f . there could be 3 classes of stance: agree (t supports f ), discuss (t neutrally discusses f ), and disagree (t refutes f ). for this task, we obtained a pre-trained bert base model 11 and trained it on the fake-news challenge stage 1 (fnc-1) dataset. 12 we denote this model stance-bert. we first pre-process p f to conform with bert input conventions by 1) applying w (•), the word-piece tokenizer @xcite , 2) applying c(a 1 , a 2 , . . . , a n ), a function that concatenates arguments in appearance order, and 3) inserting specialized bert tokens [cls] and @xcite . since bert has a maximum input length of @xmath5, we propose a sliding-window approach inspired by @xcite to form input x f : for each fact f , the stance detection results are accumulated to generate scores s f c , where @xmath6, discuss, disagree} that denote the percentage of tweets that agree, discuss, and disagree with f : 13 ## conclusion this paper introduces an information dashboard constructed in the context of our ongoing project regarding the covid-19 misinfodemic. going forward, we will focus on developing the dashboard at scale, including more comprehensive tweet collection and catalog discovery and collection. we will also introduce more functions into the dashboard that are aligned with our project goal of studying the surveillance of, impact of, and intervention on covid-19 misinfodemic.