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Intermediary topic modelling analysis results: Mapping the tech world using text mining methods

Kristóf Gyódi; Łukasz Nawaro; Michał Paliński


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  <identifier identifierType="DOI">10.5281/zenodo.5796271</identifier>
  <creators>
    <creator>
      <creatorName>Kristóf Gyódi</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-2999-8444</nameIdentifier>
      <affiliation>University of Warsaw</affiliation>
    </creator>
    <creator>
      <creatorName>Łukasz Nawaro</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-1995-4894</nameIdentifier>
      <affiliation>University of Warsaw</affiliation>
    </creator>
    <creator>
      <creatorName>Michał Paliński</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-0075-3585</nameIdentifier>
      <affiliation>University of Warsaw</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Intermediary topic modelling analysis results: Mapping the tech world using text mining methods</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <subjects>
    <subject>Human-centric, future, technology, data-driven, policy, collective intelligence, news</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2021-12-21</date>
  </dates>
  <resourceType resourceTypeGeneral="Report"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5796271</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.5796270</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/ngi_forward</relatedIdentifier>
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  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;This study presents an innovative methodology for analysing technology news using various text mining methods. News articles provide a rich source of information to track promising emerging technologies, relevant social challenges or policy issues. Our goal is to support the Next Generation Internet initiative by providing data-science tools to map and analyse the developments of the tech word.&lt;/p&gt;

&lt;p&gt;Based on more than 200 000 articles from major media outlets, we are going to identify widely discussed topics, focusing on emerging technologies and policy issues and dive deeper in selected areas and highlight key focal points of recent developments.&lt;/p&gt;

&lt;p&gt;To meet these goals, a number of machine learning techniques are combined. The major steps can be summarised as follows:&lt;/p&gt;

&lt;p&gt;● 17 general umbrella topics are explored&lt;/p&gt;

&lt;p&gt;● 5 topics are selected for further analysis&lt;/p&gt;

&lt;p&gt;● Deep dives are presented with 2D interactive maps&lt;/p&gt;

&lt;p&gt;More specifically, the topics selected for the deep dives are:&lt;/p&gt;

&lt;p&gt;1. AI and Robots&lt;/p&gt;

&lt;p&gt;2. Policy (sums up 3 relevant areas)&lt;/p&gt;

&lt;p&gt;3. Media&lt;/p&gt;

&lt;p&gt;4. Business&lt;/p&gt;

&lt;p&gt;5. Cybersecurity&lt;/p&gt;

&lt;p&gt;With the Policy topic grouping together 3 areas: Social media crisis, Privacy and 5G.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/825652/">825652</awardNumber>
      <awardTitle>NGI FORWARD</awardTitle>
    </fundingReference>
  </fundingReferences>
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