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<?covid-19-tdm?>
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  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">JMIR</journal-id>
      <journal-id journal-id-type="nlm-ta">J Med Internet Res</journal-id>
      <journal-title>Journal of Medical Internet Research</journal-title>
      <issn pub-type="epub">1438-8871</issn>
      <publisher>
        <publisher-name>JMIR Publications</publisher-name>
        <publisher-loc>Toronto, Canada</publisher-loc>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="publisher-id">v22i11e20044</article-id>
      <article-id pub-id-type="pmid">33151895</article-id>
      <article-id pub-id-type="doi">10.2196/20044</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Original Paper</subject>
        </subj-group>
        <subj-group subj-group-type="article-type">
          <subject>Original Paper</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Impact of Trump's Promotion of Unproven COVID-19 Treatments and Subsequent Internet Trends: Observational Study</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="editor">
          <name>
            <surname>Eysenbach</surname>
            <given-names>Gunther</given-names>
          </name>
        </contrib>
      </contrib-group>
      <contrib-group>
        <contrib contrib-type="reviewer">
          <name>
            <surname>Budhwani</surname>
            <given-names>Henna</given-names>
          </name>
        </contrib>
        <contrib contrib-type="reviewer">
          <name>
            <surname>Kakashekh</surname>
            <given-names>Hardawan</given-names>
          </name>
        </contrib>
      </contrib-group>
      <contrib-group>
        <contrib id="contrib1" contrib-type="author" corresp="yes">
          <name name-style="western">
            <surname>Niburski</surname>
            <given-names>Kacper</given-names>
          </name>
          <degrees>BSc, MA</degrees>
          <xref rid="aff1" ref-type="aff">1</xref>
          <address>
            <institution>McGill University</institution>
            <addr-line>3655 Promenade Sir William Osler</addr-line>
            <addr-line>Montreal, QC, H3G 0B1</addr-line>
            <country>Canada</country>
            <phone>1 9055162020</phone>
            <email>kacperniburski@gmail.com</email>
          </address>
          <ext-link ext-link-type="orcid">https://orcid.org/0000-0003-1519-1842</ext-link>
        </contrib>
        <contrib id="contrib2" contrib-type="author">
          <name name-style="western">
            <surname>Niburski</surname>
            <given-names>Oskar</given-names>
          </name>
          <degrees>BSc</degrees>
          <xref rid="aff2" ref-type="aff">2</xref>
          <ext-link ext-link-type="orcid">https://orcid.org/0000-0003-2383-9426</ext-link>
        </contrib>
      </contrib-group>
      <aff id="aff1">
        <label>1</label>
        <institution>McGill University</institution>
        <addr-line>Montreal, QC</addr-line>
        <country>Canada</country>
      </aff>
      <aff id="aff2">
        <label>2</label>
        <institution>York University</institution>
        <addr-line>Toronto, ON</addr-line>
        <country>Canada</country>
      </aff>
      <author-notes>
        <corresp>Corresponding Author: Kacper Niburski <email>kacperniburski@gmail.com</email></corresp>
      </author-notes>
      <pub-date pub-type="collection">
        <month>11</month>
        <year>2020</year>
      </pub-date>
      <pub-date pub-type="epub">
        <day>20</day>
        <month>11</month>
        <year>2020</year>
      </pub-date>
      <volume>22</volume>
      <issue>11</issue>
      <elocation-id>e20044</elocation-id>
      <history>
        <date date-type="received">
          <day>10</day>
          <month>5</month>
          <year>2020</year>
        </date>
        <date date-type="rev-request">
          <day>13</day>
          <month>7</month>
          <year>2020</year>
        </date>
        <date date-type="rev-recd">
          <day>4</day>
          <month>8</month>
          <year>2020</year>
        </date>
        <date date-type="accepted">
          <day>31</day>
          <month>10</month>
          <year>2020</year>
        </date>
      </history>
      <copyright-statement>©Kacper Niburski, Oskar Niburski. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 22.11.2020.</copyright-statement>
      <copyright-year>2020</copyright-year>
      <license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/">
        <p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.</p>
      </license>
      <self-uri xlink:href="http://www.jmir.org/2020/11/e20044/" xlink:type="simple"/>
      <abstract>
        <sec sec-type="background">
          <title>Background</title>
          <p>Individuals with large followings can influence public opinions and behaviors, especially during a pandemic. In the early days of the pandemic, US president Donald J Trump has endorsed the use of unproven therapies. Subsequently, a death attributed to the wrongful ingestion of a chloroquine-containing compound occurred.</p>
        </sec>
        <sec sec-type="objective">
          <title>Objective</title>
          <p>We investigated Donald J Trump’s speeches and Twitter posts, as well as Google searches and Amazon purchases, and television airtime for mentions of hydroxychloroquine, chloroquine, azithromycin, and remdesivir.</p>
        </sec>
        <sec sec-type="methods">
          <title>Methods</title>
          <p>Twitter sourcing was catalogued with Factba.se, and analytics data, both past and present, were analyzed with Tweet Binder to assess average analytics data on key metrics. Donald J Trump’s time spent discussing unverified treatments on the United States’ 5 largest TV stations was catalogued with the Global Database of Events, Language, and Tone, and his speech transcripts were obtained from White House briefings. Google searches and shopping trends were analyzed with Google Trends. Amazon purchases were assessed using Helium 10 software.</p>
        </sec>
        <sec sec-type="results">
          <title>Results</title>
          <p>From March 1 to April 30, 2020, Donald J Trump made 11 tweets about unproven therapies and mentioned these therapies 65 times in White House briefings, especially touting hydroxychloroquine and chloroquine. These tweets had an impression reach of 300% above Donald J Trump’s average. Following these tweets, at least 2% of airtime on conservative networks for treatment modalities like azithromycin and continuous mentions of such treatments were observed on stations like Fox News. Google searches and purchases increased following his first press conference on March 19, 2020, and increased again following his tweets on March 21, 2020. The same is true for medications on Amazon, with purchases for medicine substitutes, such as hydroxychloroquine, increasing by 200%.</p>
        </sec>
        <sec sec-type="conclusions">
          <title>Conclusions</title>
          <p>Individuals in positions of power can sway public purchasing, resulting in undesired effects when the individuals’ claims are unverified. Public health officials must work to dissuade the use of unproven treatments for COVID-19.</p>
        </sec>
      </abstract>
      <kwd-group>
        <kwd>COVID-19</kwd>
        <kwd>behavioral economics</kwd>
        <kwd>public health</kwd>
        <kwd>behavior</kwd>
        <kwd>economics</kwd>
        <kwd>media</kwd>
        <kwd>influence</kwd>
        <kwd>infodemic</kwd>
        <kwd>infodemiology</kwd>
        <kwd>infoveillance</kwd>
        <kwd>Twitter</kwd>
        <kwd>analysis</kwd>
        <kwd>trend</kwd>
      </kwd-group>
    </article-meta>
  </front>
  <body>
    <sec sec-type="introduction">
      <title>Introduction</title>
      <p>Numerous treatments have been suggested for the novel COVID-19 disease, with only remdesivir recently showing some potential efficacy prior to its approval on May 1, 2020 [<xref ref-type="bibr" rid="ref1">1</xref>]. The efficacy of other therapies, such as hydroxychloroquine, chloroquine, and azithromycin, remains unproven, though they have been praised by numerous public figures, such as US president Donald J Trump.</p>
      <p>Previous studies have shown that individuals with high influence (ie, individuals with large social capital or political power) [<xref ref-type="bibr" rid="ref2">2</xref>] can affect public decisions and purchasing power [<xref ref-type="bibr" rid="ref3">3</xref>]. Moreover, many of these individuals have the ability to affect content, sentiment, and public attention by using social media to disseminate information [<xref ref-type="bibr" rid="ref4">4</xref>]. Donald J Trump is one such individual who has previously influenced behavior through his high social presence on the internet and his political capital as the president of the United States [<xref ref-type="bibr" rid="ref5">5</xref>].</p>
      <p>In 2020, during the initial period of the COVID-19 pandemic, many people were focused on uninvestigated therapies, especially Donald J Trump. However, it has become increasingly clear that some therapies, such as hydroxychloroquine, had numerous concerns surrounding them. Studies have reported that hydroxychloroquine can cause conduction disturbances and fatal arrhythmias [<xref ref-type="bibr" rid="ref6">6</xref>], the supply of hydroxychloroquine may decrease for approved conditions like rheumatoid arthritis [<xref ref-type="bibr" rid="ref7">7</xref>], and chloroquine is similar to chloroquine products that are used as aquarium cleaners, which may be toxic if ingested in large quantities [<xref ref-type="bibr" rid="ref8">8</xref>]. A death has already occurred due to the ingestion of chloroquine products [<xref ref-type="bibr" rid="ref9">9</xref>].</p>
      <p>A recent study has noted that there was an influx of internet searches for hydroxychloroquine and chloroquine after being tweeted about by US president Donald J Trump [<xref ref-type="bibr" rid="ref10">10</xref>]. However, the study did not investigate all therapies that were initially suggested in the United States, the purchasing amounts on Google and Amazon, or the television airtime for each respective medication. The study also did not associate these purchasing amounts with Donald J Trump’s discussions and tweets, which are 2 avenues where his reach is in the millions. This would have better shown a relationship between his influence and the impact he has on his supporters and the general public.</p>
      <p>We therefore investigated the relationship between Donald J Trump’s advocacy for unproven treatments and the media landscape of COVID-19 treatments. We also investigated how these coalesced into behavioral changes in individuals. We analyzed the time Donald J Trump spent discussing hydroxychloroquine, chloroquine, azithromycin, and remdesivir on the 5 largest US television stations, the total number of searches and purchases on Google and Amazon for these treatments, and the Twitter statistics and fallout of Donald J Trump’s advocacy for these therapies.</p>
    </sec>
    <sec sec-type="methods">
      <title>Methods</title>
      <sec>
        <title>Twitter</title>
        <p>Tweets were catalogued based on mentions of hydroxychloroquine, chloroquine, azithromycin, and remdesivir. Factba.se was used to note if any Twitter posts were archived or otherwise hidden by Donald J Trump [<xref ref-type="bibr" rid="ref11">11</xref>]. Tweet Binder was used to assess average analytics data on key metrics, such as likes, retweets, and comments [<xref ref-type="bibr" rid="ref12">12</xref>]. Tweet Binder further aggregated all tweets and averaged longitudinal tweeting patterns.</p>
      </sec>
      <sec>
        <title>Television</title>
        <p>The time Donald J Trump spent discussing each treatment on television was recorded from the 5 largest US television stations: CNN (Cable News Network), C-SPAN (Cable-Satellite Public Affairs Network), Fox News, MSNBC (Microsoft/National Broadcasting Company), and BBC (British Broadcasting Corporation) News. The Global Database of Events, Language, and Tone was used to note White House briefings and the total monitored airtime of each station for broadcasts that aired from March 1 to April 30, 2020 [<xref ref-type="bibr" rid="ref13">13</xref>]. Airtime data are presented as percentages of 15-second airtime blocks, which is the maximum the software can crawl its historical database. Manual crawling of television airtimes or news headings was not performed.</p>
      </sec>
      <sec>
        <title>Google</title>
        <p>Google searches from March 1 to April 30, 2020 were indexed with Google Trends, focusing on searches performed within the United States [<xref ref-type="bibr" rid="ref14">14</xref>]. Searches including the words “hydroxychloroquine,” “chloroquine,” “azithromycin,” “remdesivir,” and “covid treatment” were catalogued, along with the Google purchasing patterns for these items. Analyses for these searches were performed concurrently with cataloguing. However, the results for this period were not large enough to report. The data obtained were the proportional patterns of searches instead of exact amounts, given Google’s data agreement [<xref ref-type="bibr" rid="ref15">15</xref>].</p>
      </sec>
      <sec>
        <title>Amazon</title>
        <p>Amazon purchases were indexed based on the following words: “hydroxychloroquine,” “chloroquine,” “azithromycin,” and “remdesivir.” Search volume was assessed using Helium 10 software [<xref ref-type="bibr" rid="ref16">16</xref>]. Estimated purchase amounts were calculated using Ahrefs [<xref ref-type="bibr" rid="ref17">17</xref>].</p>
      </sec>
      <sec>
        <title>Data Analysis</title>
        <p>Data were analyzed with Microsoft Excel. This included aggregating the data, cataloguing data, and formatting data with key points, such as Donald J Trump’s first press conference on March 19, 2020 or his numerous tweeting dates.</p>
        <p>The wide variety of sampling, numerous software-defined metrics, and methods of measuring outcomes, such as gross estimates and proportions instead of true samples, did not lend themselves to statistical analysis [<xref ref-type="bibr" rid="ref18">18</xref>]. The key assumption of transitivity, which assumes no important differences in the distribution of either potential effect modifiers or sampling techniques (eg, individual comparisons of price points between Google and Amazon), would be high for indirect comparisons.</p>
      </sec>
    </sec>
    <sec sec-type="results">
      <title>Results</title>
      <sec>
        <title>Twitter</title>
        <p><xref ref-type="table" rid="table1">Table 1</xref> notes the characteristics of US president Donald J Trump’s tweets. Donald J Trump mainly focused on hydroxychloroquine, as per his own tweets (eg, his tweets on March 21, 2020) and his retweets of Twitter posts from other people who supported his claims regarding the efficacy of hydroxychloroquine. Various tweets included news articles (eg, the tweet on April 4, 2020), which were mostly from Fox News (4/6, 67%).</p>
        <p>Based on our analysis with Tweet Binder [<xref ref-type="bibr" rid="ref19">19</xref>], with an average proportion of nearly 100,000 likes and 20,000 retweets, Donald J Trump’s first tweet on March 21, 2020 advocating for the use of hydroxychloroquine and azithromycin was one of his most popular tweets. With 385,700 likes and 103,200 retweets at the time of this study, the tweet had a potential estimated impression reach (ie, the number of potential user views on the individual tweet) of 78,800,580.</p>
        <table-wrap position="float" id="table1">
          <label>Table 1</label>
          <caption>
            <p>Characteristics Donald J Trump’s tweets that mention hydroxychloroquine, chloroquine, chloroquine, and remdesivir, including key metrics such as retweets, likes, and whether the tweets were retweets or self-composed tweets by Donald J Trump.</p>
          </caption>
          <table width="1000" cellpadding="5" cellspacing="0" border="1" rules="groups" frame="hsides">
            <col width="30"/>
            <col width="110"/>
            <col width="120"/>
            <col width="200"/>
            <col width="150"/>
            <col width="170"/>
            <col width="130"/>
            <col width="90"/>
            <thead>
              <tr valign="top">
                <td colspan="2">Therapy/Mention</td>
                <td>Date</td>
                <td>Tweet sources</td>
                <td>Number of retweets of other posts</td>
                <td>Number of self-retweets</td>
                <td>Number of retweets</td>
                <td>Number of likes</td>
              </tr>
            </thead>
            <tbody>
              <tr valign="top">
                <td colspan="8"><bold>Hydroxychloroquine</bold></td>
              </tr>
              <tr valign="top">
                <td>
                  <break/>
                </td>
                <td>1</td>
                <td>April 18, 2020</td>
                <td>@paulsperry_ [<xref ref-type="bibr" rid="ref20">20</xref>]</td>
                <td>1</td>
                <td>0</td>
                <td>12,600</td>
                <td>30,500</td>
              </tr>
              <tr valign="top">
                <td>
                  <break/>
                </td>
                <td>2</td>
                <td>April 10, 2020</td>
                <td>@cybergenica [<xref ref-type="bibr" rid="ref21">21</xref>]</td>
                <td>1</td>
                <td>0</td>
                <td>5800</td>
                <td>16,900</td>
              </tr>
              <tr valign="top">
                <td>
                  <break/>
                </td>
                <td>3</td>
                <td>April 4, 2020</td>
                <td>@OANN [<xref ref-type="bibr" rid="ref22">22</xref>]</td>
                <td>1</td>
                <td>0</td>
                <td>7800</td>
                <td>21,600</td>
              </tr>
              <tr valign="top">
                <td>
                  <break/>
                </td>
                <td>4</td>
                <td>April 4, 2020</td>
                <td>@jsolomonReports [<xref ref-type="bibr" rid="ref23">23</xref>]</td>
                <td>1</td>
                <td>0</td>
                <td>11,300</td>
                <td>28,200</td>
              </tr>
              <tr valign="top">
                <td>
                  <break/>
                </td>
                <td>5</td>
                <td>March 23, 2020</td>
                <td>@AndrewCMcCarthy [<xref ref-type="bibr" rid="ref24">24</xref>]</td>
                <td>1</td>
                <td>0</td>
                <td>9100</td>
                <td>23,700</td>
              </tr>
              <tr valign="top">
                <td>
                  <break/>
                </td>
                <td>6</td>
                <td>March 21, 2020</td>
                <td>@realDonaldTrump [<xref ref-type="bibr" rid="ref25">25</xref>]</td>
                <td>0</td>
                <td>1</td>
                <td>103,200</td>
                <td>385,700</td>
              </tr>
              <tr valign="top">
                <td>
                  <break/>
                </td>
                <td>7</td>
                <td>March 21, 2020</td>
                <td>@MichaelCoudrey [<xref ref-type="bibr" rid="ref26">26</xref>]</td>
                <td>1</td>
                <td>0</td>
                <td>27,300</td>
                <td>53,900</td>
              </tr>
              <tr valign="top">
                <td>
                  <break/>
                </td>
                <td>8</td>
                <td>March 21, 2020</td>
                <td>@realDonaldTrump [<xref ref-type="bibr" rid="ref27">27</xref>]</td>
                <td>1</td>
                <td>0</td>
                <td>103,200</td>
                <td>385,700</td>
              </tr>
              <tr valign="top">
                <td colspan="8"><bold>Azithromycin</bold></td>
              </tr>
              <tr valign="top">
                <td>
                  <break/>
                </td>
                <td>1</td>
                <td>March 21, 2020</td>
                <td>@realDonaldTrump [<xref ref-type="bibr" rid="ref25">25</xref>]</td>
                <td>0</td>
                <td>1</td>
                <td>103,200</td>
                <td>385,700</td>
              </tr>
              <tr valign="top">
                <td>
                  <break/>
                </td>
                <td>2</td>
                <td>March 21, 2020</td>
                <td>@MichaelCoudrey [<xref ref-type="bibr" rid="ref26">26</xref>]</td>
                <td>1</td>
                <td>0</td>
                <td>27,300</td>
                <td>53,900</td>
              </tr>
              <tr valign="top">
                <td>
                  <break/>
                </td>
                <td>3</td>
                <td>March 21, 2020</td>
                <td>@realDonaldTrump [<xref ref-type="bibr" rid="ref27">27</xref>]</td>
                <td>0</td>
                <td>0</td>
                <td>103,200</td>
                <td>385,700</td>
              </tr>
              <tr valign="top">
                <td colspan="8"><bold>Chloroquine</bold></td>
              </tr>
              <tr valign="top">
                <td>
                  <break/>
                </td>
                <td>0</td>
                <td>N/A<sup>a</sup></td>
                <td>0</td>
                <td>0</td>
                <td>0</td>
                <td>0</td>
                <td>0</td>
              </tr>
              <tr valign="top">
                <td colspan="8"><bold>Remdesivir</bold></td>
              </tr>
              <tr valign="top">
                <td>
                  <break/>
                </td>
                <td>0</td>
                <td>N/A</td>
                <td>0</td>
                <td>0</td>
                <td>0</td>
                <td>0</td>
                <td>0</td>
              </tr>
            </tbody>
          </table>
          <table-wrap-foot>
            <fn id="table1fn1">
              <p><sup>a</sup>N/A: not applicable.</p>
            </fn>
          </table-wrap-foot>
        </table-wrap>
      </sec>
      <sec>
        <title>Television Mentions and Broadcasts</title>
        <p><xref ref-type="table" rid="table2">Table 2</xref> notes the number of times Donald J Trump mentioned COVID-19 treatments during White House briefings. He mentions hydroxychloroquine 37 times in total, most frequently on April 4, 2020 (9 times); chloroquine 12 times in total, most frequently on March 19, 2020 (7 times); azithromycin 8 times in total, most frequently on March 19, 2020 (3 times, while also mentioning hydroxychloroquine); and remdesivir 8 times in total, most frequently on March 19, 2020 (4 times).</p>
        <table-wrap position="float" id="table2">
          <label>Table 2</label>
          <caption>
            <p>Dates of Donald J Trump’s mentions of unproven therapies during televised appearances.</p>
          </caption>
          <table width="1000" cellpadding="5" cellspacing="0" border="1" rules="groups" frame="hsides">
            <col width="280"/>
            <col width="180"/>
            <col width="180"/>
            <col width="180"/>
            <col width="180"/>
            <thead>
              <tr valign="top">
                <td>Date</td>
                <td colspan="4">Number of mentions on television</td>
              </tr>
              <tr valign="top">
                <td>
                  <break/>
                </td>
                <td>Hydroxychloroquine</td>
                <td>Chloroquine</td>
                <td>Azithromycin</td>
                <td>Remdesivir</td>
              </tr>
            </thead>
            <tbody>
              <tr valign="top">
                <td>March 19, 2020</td>
                <td>4</td>
                <td>7</td>
                <td>3</td>
                <td>4</td>
              </tr>
              <tr valign="top">
                <td>March 21, 2020</td>
                <td>3</td>
                <td>0</td>
                <td>0</td>
                <td>1</td>
              </tr>
              <tr valign="top">
                <td>March 23, 2020</td>
                <td>2</td>
                <td>0</td>
                <td>0</td>
                <td>0</td>
              </tr>
              <tr valign="top">
                <td>March 27, 2020</td>
                <td>1</td>
                <td>1</td>
                <td>1</td>
                <td>0</td>
              </tr>
              <tr valign="top">
                <td>March 29, 2020</td>
                <td>1</td>
                <td>0</td>
                <td>0</td>
                <td>0</td>
              </tr>
              <tr valign="top">
                <td>March 31, 2020</td>
                <td>4</td>
                <td>2</td>
                <td>0</td>
                <td>0</td>
              </tr>
              <tr valign="top">
                <td>April 1, 2020</td>
                <td>1</td>
                <td>1</td>
                <td>0</td>
                <td>1</td>
              </tr>
              <tr valign="top">
                <td>April 4, 2020</td>
                <td>9</td>
                <td>0</td>
                <td>0</td>
                <td>0</td>
              </tr>
              <tr valign="top">
                <td>April 5, 2020</td>
                <td>3</td>
                <td>0</td>
                <td>0</td>
                <td>1</td>
              </tr>
              <tr valign="top">
                <td>April 6, 2020</td>
                <td>1</td>
                <td>0</td>
                <td>0</td>
                <td>0</td>
              </tr>
              <tr valign="top">
                <td>April 7, 2020</td>
                <td>3</td>
                <td>0</td>
                <td>1</td>
                <td>0</td>
              </tr>
              <tr valign="top">
                <td>April 8, 2020</td>
                <td>1</td>
                <td>0</td>
                <td>0</td>
                <td>0</td>
              </tr>
              <tr valign="top">
                <td>April 9, 2020</td>
                <td>1</td>
                <td>0</td>
                <td>1</td>
                <td>0</td>
              </tr>
              <tr valign="top">
                <td>April 10, 2020</td>
                <td>2</td>
                <td>0</td>
                <td>1</td>
                <td>0</td>
              </tr>
              <tr valign="top">
                <td>April 13, 2020</td>
                <td>1</td>
                <td>1</td>
                <td>1</td>
                <td>1</td>
              </tr>
              <tr valign="top">
                <td>Total</td>
                <td>37</td>
                <td>12</td>
                <td>8</td>
                <td>8</td>
              </tr>
            </tbody>
          </table>
        </table-wrap>
        <p><xref rid="figure1" ref-type="fig">Figure 1</xref> displays the television airtime of broadcasts that mentioned the treatments. Airtime for mentions of chloroquine (<xref rid="figure1" ref-type="fig">Figure 1</xref>A) peaked on March 24, 2020 on Fox News (0.59%), MSNBC (0.78%), and CNN (0.61%), following the chloroquine-related death. Following Donald J Trump’s tweet advocating for the use of hydroxychloroquine, airtime for mentions of hydroxychloroquine (<xref rid="figure1" ref-type="fig">Figure 1</xref>B) peaked on April 4 and April 6, 2020 on Fox News (1.94% and 2.46%, respectively), while peaking on CNN on April 22, 2020 (2.88%). Fox News was one of the single television networks that continually provided coverage on azithromycin (<xref rid="figure1" ref-type="fig">Figure 1</xref>C), as airtime for its mention peaked at 0.45% on April 7, 2020. Remdesivir (<xref rid="figure1" ref-type="fig">Figure 1</xref>D) was not mentioned on any news organizations until April 30, 2020, followed by the decision by the US Food and Drug Administration to approve remdesivir as a COVID-19 treatment on May 1, 2020 [<xref ref-type="bibr" rid="ref28">28</xref>]. At this point, airtime for mentions of remdesivir experienced peaks comparable to those of the other treatments. COVID-19 treatment coverage has been increasing on all networks, especially after the March 19, 2020 press conference.</p>
        <fig id="figure1" position="float">
          <label>Figure 1</label>
          <caption>
            <p>Television airtime of keywords from March 1 to April 30, 2020 during 15-second airtime blocks. Dark blue represents BBC News, orange represents CNN, grey represents C-SPAN, yellow represents Fox News, and light blue represents MSNBC. (A) Airtime for the term "chloroquine". (B) Airtime for the term "hydroxychloroquine." (C) Airtime for the term "azithromycin." (D) Airtime for the term "remdesivir". (E) Airtime for the term "covid treatment". CNN: Cable News Network; C-SPAN: Cable-Satellite Public Affairs Network.</p>
          </caption>
          <graphic xlink:href="jmir_v22i11e20044_fig1.png" alt-version="no" mimetype="image" position="float" xlink:type="simple"/>
        </fig>
      </sec>
      <sec>
        <title>Google and Amazon</title>
        <p>Searches for and purchases (<xref rid="figure2" ref-type="fig">Figure 2</xref>A) of chloroquine on Google peaked on March 19, 2020, followed by a second peak on March 23, 2020, after the story of chloroquine-poisoning was made public. Searches for and purchases of hydroxychloroquine followed a similar trend, though there was a second peak on April 4, 2020 when Donald J Trump retweeted a studying discussing the apparent efficacy of hydroxychloroquine. Peaks in COVID-19 treatment searches (<xref rid="figure1" ref-type="fig">Figure 1</xref>E) largely coincided with Donald J Trump’s White House briefing on March 19, 2020, with 3 peaks correlating with his tweet history in <xref ref-type="table" rid="table1">Table 1</xref> (March 19, March 21, and April 4, 2020).</p>
        <fig id="figure2" position="float">
          <label>Figure 2</label>
          <caption>
            <p>Google searches (blue line) for and purchases (orange line) of keywords. (A) Results for the term "chloroquine." (B) Results for the term "hydroxychloroquine." (C) Results for the term "azithromycin." (D) Results for the term "remdesivir." (E) Results for the term "covid treatment.".</p>
          </caption>
          <graphic xlink:href="jmir_v22i11e20044_fig2.png" alt-version="no" mimetype="image" position="float" xlink:type="simple"/>
        </fig>
        <p>Our Amazon purchase analysis (<xref rid="figure3" ref-type="fig">Figure 3</xref>) showed a peak in purchases for chloroquine, hydroxychloroquine, and azithromycin after the March 19, 2020 White House briefing. This increase in purchases was observed for various forms of these medications, whether it was for purchases of a book about azithromycin [<xref ref-type="bibr" rid="ref29">29</xref>], which increased by 10 sales; herbal elements claiming to have hydroxychloroquine as an active element [<xref ref-type="bibr" rid="ref30">30</xref>], which increased by an estimated 50 sales; or texts on alternative chloroquine-containing compounds, such as chloroquine phosphate, which increased by an estimated 30 sales [<xref ref-type="bibr" rid="ref31">31</xref>]. Secondary peaks for searches on and purchases for hydroxychloroquine were observed after the tweet on April 4, 2020.</p>
        <fig id="figure3" position="float">
          <label>Figure 3</label>
          <caption>
            <p>Amazon purchases of alternate therapies. Grey represents purchases of hydroxychloroquine, blue represents purchases of azithromycin, and orange represents purchases of chloroquine.</p>
          </caption>
          <graphic xlink:href="jmir_v22i11e20044_fig3.png" alt-version="no" mimetype="image" position="float" xlink:type="simple"/>
        </fig>
      </sec>
    </sec>
    <sec sec-type="discussion">
      <title>Discussion</title>
      <p>This study is the first to show the media landscape of COVID-19 therapies prior to gaining approval or complete disregard from the scientific community. Our results show that there was a substantial increase in purchases and searches for previously unpurchased and unsearched therapies by the general public following the backing of US president Donald J Trump. These increases correlated with his discussions in press conferences and personal social media posts advocating for hydroxychloroquine and chloroquine cures. Conservative outlets provided the most airtime to hydroxychloroquine and chloroquine, and airtime for these therapies only peaked on liberal media outlets after the chloroquine-induced death occurred. Many of the increases in the purchases of these products mirror the increases in searches and airtimes that followed the initial press conference on March 19, 2020, during which Donald J Trump touted these therapies, and the subsequent social media endorsements on Twitter by Donald J Trump. Remdesivir, the treatment with the most evidence from small preliminary studies and largescale testing for COVID-19, had the least coverage time from all stations assessed in this study until just before its emergency approval on May 1, 2020. Donald J Trump has not advocated for its use publicly, except during the initial press conference on March 19, 2020, when all the suggested treatments were outlined.</p>
      <p>During unknown medical situations, delicacy is required when determining the best treatments. Previous studies have shown that individuals are susceptible to easy claims and conspiracies without appropriate evidence [<xref ref-type="bibr" rid="ref32">32</xref>], and once these inauthentic claims are given momentum, they are hard to dissuade [<xref ref-type="bibr" rid="ref33">33</xref>]. It is for this reason that individuals often seek out influential figures for guidance and knowledge [<xref ref-type="bibr" rid="ref34">34</xref>]. Providing assurances for unverified claims and treatments is dangerous, given that medications can have numerous side effect profiles. Recent trials have in fact been halted due to their general risk, as was the case with hydroxychloroquine [<xref ref-type="bibr" rid="ref35">35</xref>]. Additionally, their utility for approved conditions, such as malaria, has been compromised due to limited access and hoarding [<xref ref-type="bibr" rid="ref36">36</xref>].</p>
      <p>Efforts need to be made to prevent further harm. In some instances, this has already occurred. Google has decreased access to links that sell chloroquine, whereas Amazon has removed links to chloroquine phosphate and provided COVID-19–related information on associated links. However, this is only true for the US version of the website. Other domains, such as .ca, still provide easy purchasing for medicine substitutes [<xref ref-type="bibr" rid="ref37">37</xref>]. Public heath individuals must do more to advocate for safer, evidenced-based approaches. For example, Twitter has recently provided users the ability to flag COVID-19 misinformation and take down posts. This has occurred to Donald J Trump twice after April 30, 2020 for making nonfactual hydroxychloroquine claims [<xref ref-type="bibr" rid="ref5">5</xref>].</p>
      <p>There are limitations in this study. The estimated number of purchases and searches on Google and Amazon were both estimates, as they were provided by external providers rather than the services themselves. However, given the limited data access, these estimates are the best available to third party providers and researchers. Still, ours is first study to examine the purchasing behaviors of individuals and the media landscape of COVID-19 treatments following Donald J Trump’s endorsements. Future studies will examine the searches and purchases of certain key words/items, such as masks, ultraviolet, and disinfectants.</p>
    </sec>
  </body>
  <back>
    <app-group/>
    <glossary>
      <title>Abbreviations</title>
      <def-list>
        <def-item>
          <term id="abb1">BBC</term>
          <def>
            <p>British Broadcasting Corporation</p>
          </def>
        </def-item>
        <def-item>
          <term id="abb2">CNN</term>
          <def>
            <p>Cable News Network</p>
          </def>
        </def-item>
        <def-item>
          <term id="abb3">C-SPAN</term>
          <def>
            <p>Cable-Satellite Public Affairs Network</p>
          </def>
        </def-item>
        <def-item>
          <term id="abb4">MSNBC</term>
          <def>
            <p>Microsoft/National Broadcasting Company</p>
          </def>
        </def-item>
      </def-list>
    </glossary>
    <fn-group>
      <fn fn-type="conflict">
        <p>None declared.</p>
      </fn>
    </fn-group>
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