Conference paper Open Access

User Identity Linkage in Social Media Using Linguistic and Social Interaction Features

Despoina Chatzakou; Juan Soler-Company; Theodora Tsikrika; Leo Wanner; Stefanos Vrochidis; Ioannis Kompatsiaris


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  <identifier identifierType="URL">https://zenodo.org/record/3862116</identifier>
  <creators>
    <creator>
      <creatorName>Despoina Chatzakou</creatorName>
      <affiliation>Information Technologies Institute, Centre for Research and Technology Hellas</affiliation>
    </creator>
    <creator>
      <creatorName>Juan Soler-Company</creatorName>
      <affiliation>Pompeu Fabra University</affiliation>
    </creator>
    <creator>
      <creatorName>Theodora Tsikrika</creatorName>
      <affiliation>Information Technologies Institute, Centre for Research and Technology Hellas</affiliation>
    </creator>
    <creator>
      <creatorName>Leo Wanner</creatorName>
      <affiliation>Pompeu Fabra University, ICREA</affiliation>
    </creator>
    <creator>
      <creatorName>Stefanos Vrochidis</creatorName>
      <affiliation>Information Technologies Institute, Centre for Research and Technology Hellas</affiliation>
    </creator>
    <creator>
      <creatorName>Ioannis Kompatsiaris</creatorName>
      <affiliation>Information Technologies Institute, Centre for Research and Technology Hellas</affiliation>
    </creator>
  </creators>
  <titles>
    <title>User Identity Linkage in Social Media Using Linguistic and Social Interaction Features</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>Actor identity resolution</subject>
    <subject>Abusive and Illegal content</subject>
    <subject>Twitter</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-05-28</date>
  </dates>
  <resourceType resourceTypeGeneral="ConferencePaper"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3862116</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1145/3394231.3397920</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/connexions-h2020</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;Social media users often hold several accounts in their effort to multiply the spread of their thoughts, ideas, and viewpoints. In the particular case of objectionable content, users tend to create multiple accounts to bypass the combating measures enforced by social media platforms and thus retain their online identity even if some of their accounts are suspended.&amp;nbsp;User identity linkage aims to reveal social media accounts likely to belong to the same natural person so as to prevent the spread of abusive/illegal activities. To this end, this work proposes a machine learning-based detection model, which uses multiple attributes of users&amp;#39; online activity in order to identify whether two or more virtual identities belong to the same real natural person. The models efficacy is demonstrated on two cases on abusive and terrorism-related Twitter content.&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/H2020/833115/">833115</awardNumber>
      <awardTitle>Prediction and Visual Intelligence for Security Information</awardTitle>
    </fundingReference>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/786731/">786731</awardNumber>
      <awardTitle>InterCONnected NEXt-Generation Immersive IoT Platform of Crime and Terrorism DetectiON, PredictiON, InvestigatiON, and PreventiON Services</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
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