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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    <subfield code="a">Actor identity resolution</subfield>
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    <subfield code="a">Abusive and Illegal content</subfield>
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    <subfield code="a">Twitter</subfield>
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    <subfield code="d">6-10 July, 2020</subfield>
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    <subfield code="a">12th ACM Conference on Web Science</subfield>
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    <subfield code="u">Pompeu Fabra University</subfield>
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    <subfield code="u">Information Technologies Institute, Centre for Research and Technology Hellas</subfield>
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    <subfield code="u">Pompeu Fabra University, ICREA</subfield>
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    <subfield code="u">Information Technologies Institute, Centre for Research and Technology Hellas</subfield>
    <subfield code="a">Stefanos Vrochidis</subfield>
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    <subfield code="u">Information Technologies Institute, Centre for Research and Technology Hellas</subfield>
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    <subfield code="u">Information Technologies Institute, Centre for Research and Technology Hellas</subfield>
    <subfield code="a">Despoina Chatzakou</subfield>
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    <subfield code="a">User Identity Linkage in Social Media Using Linguistic and Social Interaction Features</subfield>
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    <subfield code="a">&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;</subfield>
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