Journal article Open Access

Identifying and characterizing social media communities: a socio-semantic network approach to altmetrics

Arroyo-Machado, Wenceslao; Torres-Salinas, Daniel; Robinson-García, Nicolás


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  <identifier identifierType="DOI">10.5281/zenodo.5518932</identifier>
  <creators>
    <creator>
      <creatorName>Arroyo-Machado, Wenceslao</creatorName>
      <givenName>Wenceslao</givenName>
      <familyName>Arroyo-Machado</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-9437-8757</nameIdentifier>
      <affiliation>University of Granada</affiliation>
    </creator>
    <creator>
      <creatorName>Torres-Salinas, Daniel</creatorName>
      <givenName>Daniel</givenName>
      <familyName>Torres-Salinas</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-0585-7359</nameIdentifier>
      <affiliation>University of Granada</affiliation>
    </creator>
    <creator>
      <creatorName>Robinson-García, Nicolás</creatorName>
      <givenName>Nicolás</givenName>
      <familyName>Robinson-García</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-0585-7359</nameIdentifier>
      <affiliation>University of Granada</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Identifying and characterizing social media communities: a socio-semantic network approach to altmetrics</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <subjects>
    <subject>Network analysis</subject>
    <subject>Socio-semantic networks</subject>
    <subject>Altmetrics</subject>
    <subject>Twitter</subject>
    <subject>Information Science &amp; Library Science</subject>
    <subject>Microbiology</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2021-09-21</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5518932</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsPreviousVersionOf" resourceTypeGeneral="JournalArticle">10.1007/s11192-021-04167-8</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.5518931</relatedIdentifier>
  </relatedIdentifiers>
  <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;Altmetric indicators allow exploring and profiling individuals who discuss and share scientific literature in social media. But it is still a challenge to identify and characterize communities based on the research topics in which they are interested as social and geographic proximity also influence interactions. This paper proposes a new method which profiles social media users based on their interest on research topics using altmetric data. Social media users are clustered based on the topics related to the research publications they share in social media. This allows removing linkages which respond to social or personal proximity and identifying disconnected users who may have similar research interests. We test this method for users tweeting publications from the fields of Information Science &amp;amp; Library Science, and Microbiology. We conclude by discussing the potential application of this method and how it can assist information professionals, policy managers and academics to understand and identify the main actors discussing research literature in social media.&lt;/p&gt;</description>
    <description descriptionType="Other">Paper accepted for publication in Scientometrics</description>
  </descriptions>
</resource>
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