<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>2594-0082</journal-id>
<journal-title><![CDATA[Estudios en derecho a la información]]></journal-title>
<abbrev-journal-title><![CDATA[Estud. derecho inf.]]></abbrev-journal-title>
<issn>2594-0082</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Instituto de Investigaciones Jurídicas]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2594-00822023000200051</article-id>
<article-id pub-id-type="doi">10.22201/iij.25940082e.2023.16.18071</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Polarización y similitud en las representaciones de mensajes oficiales en medios ante la pandemia de covid-19]]></article-title>
<article-title xml:lang="en"><![CDATA[Polarization and simmilarity in media and mexican government in facing the covid-19 pandemia]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Huerta Wong]]></surname>
<given-names><![CDATA[Juan Enrique]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Castañeda Valencia]]></surname>
<given-names><![CDATA[Alejandro Miguel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Manzano Mora]]></surname>
<given-names><![CDATA[Francisco Javier]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,jehuerta848@gmail.com  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>México</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,alcastan.eco@gmail.com  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>México</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,alcastan.eco@gmail.com  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>México</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2023</year>
</pub-date>
<numero>16</numero>
<fpage>51</fpage>
<lpage>77</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2594-00822023000200051&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2594-00822023000200051&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2594-00822023000200051&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen: ¿Cuál fue el nivel de información disponible a la ciudadanía un día cualquiera de 2020 respecto a la pandemia de COVID-19? Se analiza aquí el contenido en medios de la Ciudad de México y conferencias del gobierno en la pandemia de COVID, con tres técnicas de aprendizaje automático, a dos millones de caracteres de conferencias, y 28,127 notas y columnas de impresos y noticieros. Los principales resultados indican que hubo similitud en noticias y polarización en opinión. Esto es, la superficie narrativa de los medios consideró seguir la información que el Consejo General de Salubridad proporcionó con el objetivo de salvar más vidas. En cambio, la superficie de comentarios en los medios muestra una tendencia a discrepar de la opinión del conjunto de profesionales de la medicina cuya función principal fue salvaguardar vidas. Se discuten implicaciones de la responsabilidad social de los medios, en el contexto de una emergencia sanitaria.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: By using a triple machine learning approach, we conduct a content analysis on dos million bites of Mexican government information, and 28, 127 news and opinion columns published in 2020, relating to COVID pandemia. Findings report similarity in news and polarization in opinion columns. This means that news covered accurately Health General Council recommendations, but comments in media tended to distort what this group of professionals in Medicine provided with the objective of protect Mexicans&#8217; lives. Those findings bring media social responsibility and right to information to the further discussions of COVID-19, the most serious health crisis in Mexico history.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[COVID-19]]></kwd>
<kwd lng="es"><![CDATA[medios de comunicación]]></kwd>
<kwd lng="es"><![CDATA[comunicación y medios]]></kwd>
<kwd lng="es"><![CDATA[estudios de medios de comunicación]]></kwd>
<kwd lng="es"><![CDATA[métodos]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje máquina]]></kwd>
<kwd lng="es"><![CDATA[polarización de la información]]></kwd>
<kwd lng="en"><![CDATA[COVID-19]]></kwd>
<kwd lng="en"><![CDATA[media]]></kwd>
<kwd lng="en"><![CDATA[communication and media]]></kwd>
<kwd lng="en"><![CDATA[mass media studies]]></kwd>
<kwd lng="en"><![CDATA[methods]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
<kwd lng="en"><![CDATA[information polarization]]></kwd>
</kwd-group>
</article-meta>
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