{"id":511,"date":"2020-04-16T20:33:37","date_gmt":"2020-04-17T01:33:37","guid":{"rendered":"http:\/\/joyojeet.people.si.umich.edu\/?p=511"},"modified":"2020-04-16T20:33:37","modified_gmt":"2020-04-17T01:33:37","slug":"temporal-patterns-in-covid-19-misinformation-in-india","status":"publish","type":"post","link":"https:\/\/joyojeet.people.si.umich.edu\/?p=511","title":{"rendered":"Temporal Patterns in COVID-19 misinformation in India"},"content":{"rendered":"

Syeda Zainab Akbar, Divyanshu Kukreti, Somya Sagarika, Joyojeet Pal<\/strong><\/p>\n

(Suggested Citation: Akbar, S., Kukreti, D., Sagarika, S., Pal, J. (2020) Temporal patterns in COVID-19 related digital misinformation in India. Available online at: https:\/\/joyojeet.people.si.umich.edu\/an-archive-of-covid-19-related-fake-news-in-india\/)<\/p>\n

Following up on a preliminary survey of debunked COVID-19 related messages<\/a> online, we sought to understand how the kinds of misinformation have changed over the last few months.<\/p>\n

Methodology<\/strong><\/h2>\n

We sampled 243 unique misinformation instances from an archive maintained by Tattle<\/a> Civic Technology,\u00a0representing all the debunked stories from the following IFCN certified fact-checkers: AltNews<\/a>, BOOMlive<\/a>, Factly<\/a>, Indiatoday Fact Check<\/a>, Quint Webqoof<\/a>, and NewsMobile Fact Checker<\/a>\u00a0during the time period of\u00a0<\/span>Jan 23, 2020 to\u00a0April 12, 2020. We systematically annotated each story for thematic-categories, representative-tags, source of claims & attributions and medium of propagation. Our annotations are limited to the information the fact-checking organisations detail in their debunking reports. This period has been the main period of news and public figure coverage of the novel coronavirus, intersecting with the early reporting around students from India in Wuhan. A detailed study of COVID-related Twitter activity in India around this period is available\u00a0here<\/a>.<\/span><\/p>\n

 <\/p>\n

Findings<\/strong><\/h2>\n

First, we find a rise in the number of debunked\u00a0misinformation, especially following the third week of March. The momentum of misinformation had already started an upward trajectory before PM Narendra Modi’s announcement of the Janata Curfew, though since that week, the increase has been consistent.<\/p>\n

\"COVID-19<\/a>
Figure 1: COVID-19 related Misinformation items debunked since Jan 2020<\/figcaption><\/figure>\n

 <\/p>\n

We expanded our initial classification<\/a>\u00a0to encompass the broad themes that defined the various kinds of misinformation being spread. Table 1 below describes the definitions of the categories we used, and the instances of each kind of misinformation.<\/p>\n\n\n\n\n\n\n\n\n\n\n
<\/td>\nCategory<\/strong><\/td>\nInstances<\/strong><\/td>\nDefinition<\/strong><\/td>\n<\/tr>\n
1<\/td>\nCulture<\/td>\n62<\/td>\nMessages\u00a0with\u00a0cultural references such as to a religious \/ ethnic \/ social group\u00a0or\u00a0a popular culture reference<\/td>\n<\/tr>\n
2<\/td>\nCure, Prevention & Treatment<\/td>\n37<\/td>\nMessages suggesting remedies (alternative or mainstream), preventive measures, and vaccines-related misinformation<\/td>\n<\/tr>\n
3<\/td>\nNature & the Environment<\/td>\n16<\/td>\nMessages that have references to animals and the environment.<\/td>\n<\/tr>\n
4<\/td>\nCasualty<\/td>\n36<\/td>\nMessages relating to deaths, illness of people in the pandemic, including graphic images of suffering (not including doctored statistics)<\/td>\n<\/tr>\n
5<\/td>\nBusiness and economy<\/td>\n15<\/td>\nMessages relating to scams, panic-buying and target businesses with fake positive cases.<\/td>\n<\/tr>\n
6<\/td>\nGovernment<\/td>\n54<\/td>\nMessages have government announcements and advisories\u00a0or refer to police, judiciary, political parties.<\/td>\n<\/tr>\n
7<\/td>\nDoctored statistics<\/td>\n23<\/td>\nMessages that have exaggerated numbers of positive cases or death counts and fake advisories.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

 <\/p>\n

We\u00a0see in the figure below that two categories of misinformation have been rising consistently – stories around culture and the government. This is due to a visible increase in stories around Muslims and\u00a0COVID-19 as well as stories around police brutality. In contrast, stories around casualty – or graphic imagery around COVID-19 infection or death as well as stories around cure peaked and started falling.<\/p>\n

As we see it, we are entering a phase in misinformation which is intended to be affective around identity and emotion rather than around instrumental facts that can be scientifically verified. Thus, from presenting fake cures or fake images of pain – which over time get debunked or appear suspicious to viewers, the misinformation has moved to cultural elements that are harder to verify.<\/p>\n

 <\/p>\n

\"Categories<\/a>
Figure 2: Categories of misinformation and their prevalence over the weeks following initial cases in India<\/figcaption><\/figure>\n

 <\/p>\n

While we see that there is a sudden burst in misinformation related to a few specific categories, consistent data collection over coming weeks can help provide more information on the statistical significance of these changes.<\/div>\n

\u00a0<\/span><\/p>\n

 <\/p>\n

Content patterns<\/strong><\/h3>\n

We find that the\u00a0types of content relied upon to relay different kinds of misinformation varies. For instance, misinformation\u00a0in the category “casualty” relies heavily on visual content, since the goal of these is to invoke a physical reaction, often fear or disgust. In contrast, tweets on cures and misleading statistics use a high amount of text, because the goal with these is to mislead by offering specifics.<\/p>\n

 <\/p>\n

\"Misinformation<\/a>
Figure 3: Misinformation by Mode of Propagation.\u00a0The green region signifies that video clips were used for spreading misinformation. Similarly, yellow, red and blue regions signify the usage of \u2018Text\u2019, \u2018Images\u2019 and \u2018Audio\u2019 respectively<\/span><\/figcaption><\/figure>\n

 <\/p>\n

Temporal Patterns<\/strong><\/h3>\n

We created wordclouds of the tags used to annotate the stories in a key 30-day period between March 14 and April 12 to see if there are any trends. The images below show that while the first period in the lead up to the lockdown was dominated by discussions of a possible lockdown and about infections, gradually the discourse changed to Muslims and religion more significantly.<\/p>\n

\"We<\/a>
Figure 4: Sampled of tags for misinformation in three 10-day periods\u00a0–\u00a0first 10 days, leading up to the Janatha Curfew and ending at the evacuation of the Nizamuddin Markaz on Mar 23. The second period starts with the announcement of the lockdown. The third period starts with PM Narendra Modi’s national address for the 9 PM 9 Minute address and ends on April 12, the end date of our sampling.<\/figcaption><\/figure>\n

The image above helps visualize the ways in which the discourse moved gradually towards Muslims in the misinformation universe.<\/p>\n

 <\/p>\n

Invent or\u00a0Repurpose<\/strong><\/h3>\n

We examined the type of content, based on whether the creator of the misinformation had created entirely new content or repurposed old content. We find\u00a0in figure .. that content categorized as culture or casualty are most likely to be repurposed older content. Since both these\u00a0categories of content seek to create an emotional affect in the viewer, the creators often seek out explicit content that is shocking and repurpose it with a false heading since it is likely to have greater shock value.\u00a0We used this terminology of fabrication and repurposing from a recent study by the Oxford Internet Institute on types, sources, and claims of COVID-19 misinformation<\/a>.<\/p>\n

\"\"<\/a>
Figure 5: Distribution of the categories of misinformation based on the novelty of the artifact of propagation. Red region signifies that the misinformation artifact has media or text that belongs to a different period and has been reconfigured to suit the current discourse. Blue region indicates that the artifact is based on new text or multimedia.<\/figcaption><\/figure>\n

Attribute to claim<\/strong><\/h3>\n

We see that different types of attribution are used for different categories of misinformation. The differences in these help us understand ways in which certain sources may be perceived as more reliable for certain kinds of\u00a0affect.<\/p>\n

 <\/p>\n

\"Different<\/a>
Figure 6: Different kinds of sources used to persuade a reader of misinformational message including attribution to a politician, an institution, a public figure such as a celebrity, or a business entity.<\/figcaption><\/figure>\n

 <\/p>\n

As we see above, politicians or institutions are more likely to be used to\u00a0perform a believable source for government-related misinformation, whereas businesses are seen as more valuable means of misguiding a reader on cure or\u00a0issues of the economy. Likewise celebrities are more likely to be used in culture-related misinformation, while formal institutions are more frequently used to help push doctored statistics.<\/p>\n

 <\/p>\n

Politicians \/ Government as spreaders of misinformation<\/strong><\/p>\n

While we do not have any data on political complicity in the spreading of misinformation, there have been instances where politicians or arms of the government have\u00a0directly passed along information that has been found to be false.<\/p>\n

The above screenshots show that while\u00a0the majority of politicians to have spread misinformation tend to be low-level politicians, we also find instances in the data when formal arms of the government, such as the Press Information Bureau (PIB) have spread misinformation.<\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

Public figures as spreaders of misinformation<\/strong><\/p>\n

Public figures can play an important role in the management of misinformation, as we note in this document, celebrities are often attributed in misinformation to give an artifact more credence. However, there have been times when widely followed public figures have themselves contributed to misinformation.<\/p>\n

\"Misinformation<\/a>
Figure 8: Misinformation spread by public figures or widely followed members of the mainstream media<\/figcaption><\/figure>\n

We see here debunked information from businessperson Kiram Mazumdar Shaw, as well as two anchors of widely consumed news sources. For instance,\u00a0Shaw tweeted that countries in the Southern Hemisphere remain unaffected by the coronavirus outbreak, while this claim was clearly dubious from the start, her engagement with it gave the idea some credence and led to widespread engagement online\u00a0before and after it was debunked.<\/span><\/p>\n

 <\/p>\n

 <\/p>\n

Mainstream media\u00a0as spreaders of misinformation<\/strong><\/p>\n

From our data we found that news sources ranging from less widely consumed, regional digital news to heavily engaged national news have been complicit in spreading misinformation. In the image below, we have\u00a0We screenshots of news channels including ANI, TOI Kochi, TV9, Global Times, OPIndia, News18 all participating in the circulation of misinformation.\u00a0<\/span><\/p>\n

\"Screenshots<\/a>
Figure 9: Screenshots of misinformation spread by mainstream news<\/figcaption><\/figure>\n

The reasons for mainstream media sharing misinformation are unclear, some may simply be out of poor editorial standards in a highly competitive media ecosystem, but it is clear that misinformation travels very fast, and\u00a0that news sources may increase footfalls through deliberate misinformation or through clickbait headlining. Interestingly, the majority of misinformation in this\u00a0are from the “culture” category, suggesting that mainstream news sources have been particularly complicit in Mulsim-baiting.<\/p>\n

 <\/p>\n

 <\/p>\n

Categories of Misinformation<\/strong><\/h3>\n

Business\u00a0& the Economy<\/strong><\/h4>\n

We classify messages under business and economy when messages relate to scams, panic-buying and target businesses with fake positive cases.<\/p>\n

\"Wordcloud<\/a>
Figure 10: Wordcloud of all the tags associated with debunked misinformation stories related to business & the economy<\/figcaption><\/figure>\n

As we see above, the majority of misinformation here is intended to create some form of panic such as stories about essential commodities disappearing, fake shutdown notices of businesses and cash shortages.<\/span><\/p>\n

Casualty<\/strong><\/h4>\n

We classify a message under casualty when they refer to fear-invoking messages related to deaths, suicides and suffering of people in the pandemic. These messages are distinct from death statistics in that they deal with the viscerality of the condition, such as descriptions of symptoms, visuals of death and suffering etc, intended to cause distress in the viewer<\/span>.<\/p>\n

\"Wordcloud<\/a>
Figure 11: Wordcloud of all the tags associated with debunked misinformation stories related to COVID-related casualties (not death statistics)<\/figcaption><\/figure>\n

A large share of the casualty-related messages were driven by the increase in casualties in Italy. As with other forms of content intended to incite fear, there are images\/videos of dead-bodies, people suffering and of women & children in pain.<\/span><\/p>\n

Culture<\/strong><\/h4>\n

We classify messages under\u00a0culture when these are about a\u00a0religious \/ ethnic \/ social group\u00a0or have a popular culture reference.\u00a0As the visualization shows, culture-related misinformation has a very significant Muslim\/Islam-related component.<\/p>\n

\"Wordcloud<\/a>
Figure 12: Wordcloud of all the tags associated with debunked COVID-related misinformation stories related to culture including ethnic groups, religion, popular culture etc.<\/figcaption><\/figure>\n

While there was a significant China-related component in the earlier stories, as the coronavirus spread increased in India, religious references appeared in increasing numbers later. Muslims became an important part of the misinformation spread as spreaders of the coronavirus and provokers of chaos and violence, following the Nizamuddin Markaz incident.<\/span><\/p>\n

A frequently seen feature of culture-related\u00a0misinformation is that they often rely on the use of a publicly recognized figure to grab attention of the reader. In this case below,\u00a0Yoga master Baba Ramdev is presented\u00a0to give credence to a misinformation claim\u00a0that makes a cultural reference at Ayurvedic \/ Hindu practices.<\/p>\n\n\n\n
False claim: Baba Ramdev is hospitalised as he drank excess of cow-urine to avoid being infected by Coronavirus<\/span><\/td>\nThis is actually an image of Baba Ramdev in the hospital after his 9-day hunger strike against black money. Older photographs of celebrities are associated with new events to increase believability and also bring more engagement with posts.\u00a0<\/span><\/td>\nhttps:\/\/www.altnews.in\/fact-check-ramdev-coronavirus-cow-urine\/<\/span><\/a><\/p>\n

Source: AltNews<\/span><\/td>\n

 <\/p>\n

\"\"<\/a><\/p>\n

…………………………………………………………….<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

The rise in \u2018Culture\u2019 related misinformation is not just attributable to\u00a0negative messaging about Muslims. A large number of “positive” misinformation emerged both after PM Modi’s request to citizens to clap for health workers, and thereafter his next request to have citizens light up candles.\u00a0Both led to misinformation about traditional scientific logic behind the move.\u00a0<\/span>The\u00a0increase in culture-related misinformation also has related to ‘cure’ related tweets that lionize traditional knowledge such as Ayurveda, which\u00a0also saw a brief spurt following the PM’s announcement.<\/p>\n

Cure, Prevention & Treatment<\/strong><\/p>\n

We list under Cure, Prevention and Treatment messages suggesting remedies (alternative or mainstream), preventive measures, and vaccines-related misinformation.<\/p>\n

\"Wordcloud<\/a>
Figure 13: Wordcloud of all the tags associated with debunked COVID-related misinformation stories related to cure, treatment, and prevention<\/figcaption><\/figure>\n

We see two distinct trends – first is that of home remedies (including terms such as lemon, vinegar etc.) and alternative medicines\u00a0(Ayush, herbs etc). While cure-related misinformation was very significant early in the COVID spread, it gradually fell with the passage of time, since the remedies did not work, and they reached a saturation point, to use a COVID metaphor, there was herd immunity to some of the claims of alternative cures as it became increasingly clear from official sources that there was none.<\/p>\n

We find that cure-related misinformation\u00a0has a higher share of institutional attribution to support\u00a0its claims. An example from a debunked story below shows\u00a0St. Luke’s hospital of Kansas City was attributed with\u00a0a claim that alcohol helps reducing risk of COVID-19. An official-looking logo and letterhead\u00a0are used to make the claim seem real.<\/p>\n

 <\/p>\n\n\n\n
Claim: Saint Luke\u2019s Hospital advised that consuming alcohol helps reduce risk of Coronavirus<\/span><\/td>\nThe hospital released that the advisory with alcohol as recommended cure is fake. The hospital’s letterhead was used to legitimise the claim.<\/span><\/td>\nhttps:\/\/www.indiatoday.in\/fact-check\/story\/coronavirus-fact-check-claims-on-alcohol-and-covid-19-1654812-2020-03-12<\/span><\/a><\/p>\n

Source: India Today<\/span><\/td>\n

\u00a0 \u00a0\"\"<\/a> …………………………………………………….<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

Doctored statistics<\/strong><\/h4>\n

Messages that have exaggerated numbers of positive cases or death counts and fake advisories. A look at the wordcloud shows that institutions\u00a0are often used to substantiate claims. Thus UNICEF, WHO or specifics about\u00a0places where the statistics are from are cited to make them more believable.<\/p>\n

\"Wordcloud<\/a>
Figure 14: Wordcloud of all the tags associated with debunked COVID-related misinformation stories including statistics or advisories<\/figcaption><\/figure>\n

Institutions are often used to convince a reader of doctored statistics, in\u00a0the case below, the World Health Organization is purported to have sent a message relating to India’s lockdown measures.<\/p>\n\n\n\n
Claim: WHO has released a protocol for Lockdown to control the virus, supporting India\u2019s lockdown measures.\u00a0<\/span><\/td>\nFact: WHO released a statement refuting the attributed messages. The name and stamp of the institution has been used to build false authority.<\/span><\/td>\nhttps:\/\/www.altnews.in\/viral-whatsapp-forward-of-who-lockdown-protocol-is-fake\/<\/span><\/a><\/p>\n

Source: AltNews<\/span><\/td>\n

\"\"<\/a><\/p>\n

……………………………………………………<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Government<\/strong><\/h4>\n

We classify\u00a0here messages have that purport to have government announcements and advisories and refer to police, judiciary\u00a0and official institutions.<\/p>\n

\"Wordcloud<\/a>
Figure 15: Wordcloud of all the tags associated with debunked COVID-related misinformation stories about the government, police and formal institutions<\/figcaption><\/figure>\n

As we see here, there is a significant component of tweets about the police, much of which is about police brutality, posts about which increased significantly after the lockdown began, and some legitimate news of police attacks on citizens started to surface<\/p>\n

In terms of legitimating claims of misinformation related to government agencies, governments or politicians, who\u00a0can be projected as having inside information on the story, are frequently used, as in the story below, where doctored visuals of an interface from a news source alongside a claim that Narendra Modi himself proposed an internet shutdown.<\/p>\n

 <\/p>\n\n\n\n
Claim: PM Narendra Modi announced a shutdown of internet services for a week to avoid panic in public through social media.<\/span><\/td>\nFact: No internet shutdowns have been announced. Fake Graphics with PM\u2019s photograph and Aaj Tak news channel logo has been used to legitimate the claim.<\/span><\/td>\nhttps:\/\/www.boomlive.in\/fake-news\/no-pm-modi-has-not-announced-an-internet-shutdown-during-lockdown-7405<\/span><\/a>\u00a0<\/span><\/p>\n

Source: BoomLive<\/span><\/td>\n

\"\"<\/a><\/p>\n

…………………………………………………….<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

Nature\u00a0& the\u00a0Environment<\/strong><\/h4>\n

Messages that have references to animals and the environment. There were two distinct strands around this heading. The first was around origins of the virus and nature-related potential causes, the second has been on the impact of the virus on nature.<\/p>\n

\"Wordcloud<\/a>
Figure 16: Wordcloud of all the tags associated with debunked COVID-related misinformation stories about nature and the environment<\/figcaption><\/figure>\n

One oddly popular thread of misinformation has been around \u201creappearance\u201d \u2013 ranging from messages about animals roaming the street (again, there have been a small number of actual reported stories of animals out and about in various small towns and villages) such as herds of deer gathered on the Ooty-Coimbatore road to bizarre misinformation about mermaids appearing as a result of reduced human impact on the environment.<\/span><\/p>\n

Link to data<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

Syeda Zainab Akbar, Divyanshu Kukreti, Somya Sagarika, Joyojeet Pal (Suggested Citation: Akbar, S., Kukreti, D., Sagarika, S., Pal, J. (2020) Temporal patterns in COVID-19 related digital misinformation in India. Available online at: https:\/\/joyojeet.people.si.umich.edu\/an-archive-of-covid-19-related-fake-news-in-india\/) Following up on a preliminary survey of debunked COVID-19 related messages online, we sought to understand how the kinds of misinformation have […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2,7],"tags":[],"class_list":["post-511","post","type-post","status-publish","format-standard","hentry","category-2","category-research"],"_links":{"self":[{"href":"https:\/\/joyojeet.people.si.umich.edu\/index.php?rest_route=\/wp\/v2\/posts\/511","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/joyojeet.people.si.umich.edu\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/joyojeet.people.si.umich.edu\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/joyojeet.people.si.umich.edu\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/joyojeet.people.si.umich.edu\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=511"}],"version-history":[{"count":0,"href":"https:\/\/joyojeet.people.si.umich.edu\/index.php?rest_route=\/wp\/v2\/posts\/511\/revisions"}],"wp:attachment":[{"href":"https:\/\/joyojeet.people.si.umich.edu\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=511"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/joyojeet.people.si.umich.edu\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=511"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/joyojeet.people.si.umich.edu\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=511"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}