<?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>1405-5546</journal-id>
<journal-title><![CDATA[Computación y Sistemas]]></journal-title>
<abbrev-journal-title><![CDATA[Comp. y Sist.]]></abbrev-journal-title>
<issn>1405-5546</issn>
<publisher>
<publisher-name><![CDATA[Instituto Politécnico Nacional, Centro de Investigación en Computación]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1405-55462024000402231</article-id>
<article-id pub-id-type="doi">10.13053/cys-28-4-5295</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Exploring Political Polarization in México: Automatic Classification of Comments on You Tube]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Sánchez-Gálvez]]></surname>
<given-names><![CDATA[Alba Maribel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Álvarez-González]]></surname>
<given-names><![CDATA[Ricardo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Molina-Iturbide]]></surname>
<given-names><![CDATA[Santiago Alejandro]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Albores-Velasco]]></surname>
<given-names><![CDATA[Francisco Javier]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Benemérita Universidad Autónoma de Puebla  ]]></institution>
<addr-line><![CDATA[Puebla ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Autónoma de Tlaxcala  ]]></institution>
<addr-line><![CDATA[Tlaxcala ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2024</year>
</pub-date>
<volume>28</volume>
<numero>4</numero>
<fpage>2231</fpage>
<lpage>2242</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-55462024000402231&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-55462024000402231&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-55462024000402231&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: YouTube, the second-largest social network globally, hosts over two and a half billion monthly users, with content surpassing five hundred hours uploaded every minute [1]. Channels dedicated to news and political discourse facilitate interactive communication, enabling users to critique, express viewpoints, and protest anonymously. Partisan engagement on social media is highly controversial and can influence the attitudes and behaviors of individuals and organizations opposing views [2]. Amid growing concerns about political polarization in Mexico, the fourth country with the highest number of YouTube users, this study aims to understand digital communication patterns and their impact on user attitudes. Web Scraping and Natural Language Processing techniques were employed to gather and analyze comments from two antagonistic political channels on YouTube: "El Chapucero" and "Atypical TV". The objective was to identify key aspects of polarization in the comments of users of these YouTube channels to create a Machine Learning model capable of predicting a user's political stance. Distinct features in the dataset were highlighted to train four Machine Learning and Deep Learning classifiers: Naive Bayes, Logistic Regression, CNN, and Bidirectional LSTM. These classifiers were able to automatically infer the political leanings of users, the one that performed the best was CNN with a precisión of 96%. The main contribution of this study lies in the word analysis that provide insights into the Mexican partisan dynamics on YouTube and in the precisión of comment classification, which is achieved due to the polarization existing between these political opinion channels.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Natural language processing]]></kwd>
<kwd lng="en"><![CDATA[text classification]]></kwd>
<kwd lng="en"><![CDATA[web scraping]]></kwd>
<kwd lng="en"><![CDATA[youtube]]></kwd>
<kwd lng="en"><![CDATA[political polarization]]></kwd>
</kwd-group>
</article-meta>
</front><back>
<ref-list>
<ref id="B1">
<nlm-citation citation-type="">
<collab>YouTube for press</collab>
<source><![CDATA[]]></source>
<year>2024</year>
</nlm-citation>
</ref>
<ref id="B2">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Picanço-Rodrigues]]></surname>
<given-names><![CDATA[V.]]></given-names>
</name>
<name>
<surname><![CDATA[Leonel-Caetano]]></surname>
<given-names><![CDATA[M. A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[The impacts of political activity on fires and deforestation in the Brazilian Amazon rainforest: An analysis of social media and satellite data]]></article-title>
<source><![CDATA[Heliyon]]></source>
<year>2023</year>
<volume>9</volume>
<numero>12</numero>
<issue>12</issue>
</nlm-citation>
</ref>
<ref id="B3">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Flamino]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Galeazzi]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Feldman]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Macy]]></surname>
<given-names><![CDATA[M. W.]]></given-names>
</name>
<name>
<surname><![CDATA[Cross]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhou]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Szymanski]]></surname>
<given-names><![CDATA[B. K.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Political polarization of news media and influencers on Twitter in the 2016 and 2020 US presidential elections]]></article-title>
<source><![CDATA[Nature Human Behaviour]]></source>
<year>2023</year>
<volume>7</volume>
<numero>6</numero>
<issue>6</issue>
<page-range>904-16</page-range></nlm-citation>
</ref>
<ref id="B4">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Magdaci]]></surname>
<given-names><![CDATA[O.]]></given-names>
</name>
<name>
<surname><![CDATA[Matalon]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Yamin]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Modeling the debate dynamics of political communication in social media networks]]></article-title>
<source><![CDATA[Expert Systems with Applications]]></source>
<year>2022</year>
<volume>206</volume>
</nlm-citation>
</ref>
<ref id="B5">
<nlm-citation citation-type="">
<collab>Statista Homepage</collab>
<source><![CDATA[]]></source>
<year>2023</year>
</nlm-citation>
</ref>
<ref id="B6">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zimmermann]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Noll]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Gräßer]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Hugger]]></surname>
<given-names><![CDATA[K. U.]]></given-names>
</name>
<name>
<surname><![CDATA[Braun]]></surname>
<given-names><![CDATA[L. M.]]></given-names>
</name>
<name>
<surname><![CDATA[Nowak]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Kaspar]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Influencers on YouTube: a quantitative study on young people&#8217;s use and perception of videos about political and societal topics]]></article-title>
<source><![CDATA[Current Psychology]]></source>
<year>2020</year>
<volume>41</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>6808-24</page-range></nlm-citation>
</ref>
<ref id="B7">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Chowdhury]]></surname>
<given-names><![CDATA[L. H.]]></given-names>
</name>
<name>
<surname><![CDATA[Islam]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Shatabda]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A bengali news and public opinion dataset from YouTube]]></article-title>
<source><![CDATA[Data in Brief]]></source>
<year>2024</year>
<volume>52</volume>
</nlm-citation>
</ref>
<ref id="B8">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Tran]]></surname>
<given-names><![CDATA[G. T.]]></given-names>
</name>
<name>
<surname><![CDATA[Nguyen]]></surname>
<given-names><![CDATA[L. V.]]></given-names>
</name>
<name>
<surname><![CDATA[Jung]]></surname>
<given-names><![CDATA[J. J.]]></given-names>
</name>
<name>
<surname><![CDATA[Han]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Understanding political polarization based on user activity: a case study in korean political YouTube channels]]></article-title>
<source><![CDATA[Sage Open]]></source>
<year>2022</year>
<volume>12</volume>
<numero>2</numero>
<issue>2</issue>
</nlm-citation>
</ref>
<ref id="B9">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Bail]]></surname>
<given-names><![CDATA[C. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Argyle]]></surname>
<given-names><![CDATA[L. P.]]></given-names>
</name>
<name>
<surname><![CDATA[Brown]]></surname>
<given-names><![CDATA[T. W.]]></given-names>
</name>
<name>
<surname><![CDATA[Bumpus]]></surname>
<given-names><![CDATA[J. P.]]></given-names>
</name>
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Hunzaker]]></surname>
<given-names><![CDATA[M. F.]]></given-names>
</name>
<name>
<surname><![CDATA[Volfovsky]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Exposure to opposing views on social media can increase political polarization]]></article-title>
<source><![CDATA[Proceedings of the National Academy of Sciences]]></source>
<year>2018</year>
<volume>115</volume>
<numero>37</numero>
<issue>37</issue>
<page-range>9216-9221.DOI:</page-range></nlm-citation>
</ref>
<ref id="B10">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Pookpanich]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Siriborvornratanakul]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Offensive language and hate speech detection using deep learning in football news live streaming chat on YouTube in Thailand]]></article-title>
<source><![CDATA[Social Network Analysis and Mining]]></source>
<year>2024</year>
<volume>14</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>18</page-range></nlm-citation>
</ref>
<ref id="B11">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Nokkaew]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Nongpong]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
<name>
<surname><![CDATA[Yeophantong]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Ploykitikoon]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Arjharn]]></surname>
<given-names><![CDATA[W.]]></given-names>
</name>
<name>
<surname><![CDATA[Siritaratiwat]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Surawanitkun]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Analyzing online public opinion on Thailand-China high-speed train and Laos-China railway mega-projects using advanced machine learning for sentiment analysis]]></article-title>
<source><![CDATA[Social Network Analysis and Mining]]></source>
<year>2023</year>
<volume>14</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>15</page-range></nlm-citation>
</ref>
<ref id="B12">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Hasan]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Islam]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Jahan]]></surname>
<given-names><![CDATA[I.]]></given-names>
</name>
<name>
<surname><![CDATA[Meem]]></surname>
<given-names><![CDATA[S. M.]]></given-names>
</name>
<name>
<surname><![CDATA[Rahman]]></surname>
<given-names><![CDATA[R. M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Natural language processing and sentiment analysis on bangla social media comments on Russia&#8211;Ukraine war using transformers]]></article-title>
<source><![CDATA[Vietnam Journal of Computer Science]]></source>
<year>2023</year>
<volume>10</volume>
<numero>03</numero>
<issue>03</issue>
<page-range>329-56</page-range></nlm-citation>
</ref>
<ref id="B13">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Hou]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Han]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[To WeChat or to more chat during learning? The relationship between WeChat and learning from the perspective of university students]]></article-title>
<source><![CDATA[Education and Information Technologies]]></source>
<year>2021</year>
<volume>26</volume>
<page-range>1813-32</page-range></nlm-citation>
</ref>
<ref id="B14">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Behzadidoost]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Mahan]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
<name>
<surname><![CDATA[Izadkhah]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Granular computing-based deep learning for text classification]]></article-title>
<source><![CDATA[Information Sciences]]></source>
<year>2024</year>
<volume>652</volume>
</nlm-citation>
</ref>
<ref id="B15">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Bengesi]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Oladunni]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Olusegun]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Audu]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A machine learning-sentiment analysis on monkeypox outbreak: An extensive dataset to show the polarity of public opinion from Twitter tweets]]></article-title>
<source><![CDATA[IEEE]]></source>
<year>2023</year>
<volume>11</volume>
<page-range>11811-26</page-range></nlm-citation>
</ref>
<ref id="B16">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Kazbekova]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
<name>
<surname><![CDATA[Ismagulova]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Kemelbekova]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Tileubay]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Baimurzayev]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[Bazarbayeva]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Offensive language detection on online social networks using hybrid deep learning architecture]]></article-title>
<source><![CDATA[International Journal of Advanced Computer Science &amp; Applications]]></source>
<year>2023</year>
<volume>14</volume>
<numero>11</numero>
<issue>11</issue>
</nlm-citation>
</ref>
<ref id="B17">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Karwa]]></surname>
<given-names><![CDATA[R. R.]]></given-names>
</name>
<name>
<surname><![CDATA[Gupta]]></surname>
<given-names><![CDATA[S. R.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Automated hybrid deep neural network model for fake news identification and classification in social networks]]></article-title>
<source><![CDATA[Journal of Integrated Science and Technology]]></source>
<year>2022</year>
<volume>10</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>110-9</page-range></nlm-citation>
</ref>
<ref id="B18">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Kumar]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Sachdeva]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Multi-input integrative learning using deep neural networks and transfer learning for cyberbullying detection in real-time code-mix data]]></article-title>
<source><![CDATA[Multimedia systems]]></source>
<year>2022</year>
<volume>28</volume>
<numero>6</numero>
<issue>6</issue>
<page-range>2027-41</page-range></nlm-citation>
</ref>
<ref id="B19">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Igual]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Seguí]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
</person-group>
<source><![CDATA[Introduction to data science]]></source>
<year>2017</year>
<publisher-loc><![CDATA[Berlin, Germany ]]></publisher-loc>
<publisher-name><![CDATA[Springer]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B20">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Chatterjee]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Krystyanczuk]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<source><![CDATA[Python social media analytics]]></source>
<year>2017</year>
<publisher-name><![CDATA[Packt Publishing Ltd.]]></publisher-name>
</nlm-citation>
</ref>
</ref-list>
</back>
</article>
