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Writer Identificati...
Writer Identification Using Microblogging Texts for Social Media Forensics
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- Alonso-Fernandez, Fernando, 1978- (författare)
- Högskolan i Halmstad,CAISR Centrum för tillämpade intelligenta system (IS-lab)
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- Sharon Belvisi, Nicole Mariah (författare)
- Högskolan i Halmstad,Akademin för informationsteknologi
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- Hernandez-Diaz, Kevin, 1992- (författare)
- Högskolan i Halmstad,CAISR Centrum för tillämpade intelligenta system (IS-lab)
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- Muhammad, Naveed (författare)
- Institute of Computer Science, University of Tartu, Tartu , Estonia
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- Bigun, Josef, 1961- (författare)
- Högskolan i Halmstad,CAISR Centrum för tillämpade intelligenta system (IS-lab)
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(creator_code:org_t)
- Piscataway, NJ : IEEE, 2021
- 2021
- Engelska.
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Ingår i: IEEE Transactions on Biometrics, Behavior, and Identity Science. - Piscataway, NJ : IEEE. - 2637-6407. ; 3:3, s. 405-426
- Relaterad länk:
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https://arxiv.org/ab...
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https://hh.diva-port... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Establishing authorship of online texts is fundamental to combat cybercrimes. Unfortunately, text length is limited on some platforms, making the challenge harder. We aim at identifying the authorship of Twitter messages limited to 140 characters. We evaluate popular stylometric features, widely used in literary analysis, and specific Twitter features like URLs, hashtags, replies or quotes. We use two databases with 93 and 3957 authors, respectively. We test varying sized author sets and varying amounts of training/test texts per author. Performance is further improved by feature combination via automatic selection. With a large amount of training Tweets (>500), a good accuracy (Rank-5>80%) is achievable with only a few dozens of test Tweets, even with several thousands of authors. With smaller sample sizes (10-20 training Tweets), the search space can be diminished by 9-15% while keeping a high chance that the correct author is retrieved among the candidates. In such cases, automatic attribution can provide significant time savings to experts in suspect search. For completeness, we report verification results. With few training/test Tweets, the EER is above 20-25%, which is reduced to < 15% if hundreds of training Tweets are available. We also quantify the computational complexity and time permanence of the employed features. © 2019 IEEE.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)
Nyckelord
- Authorship identification
- stylometry
- social media forensics
- writer identification
- writer verification
- biometrics
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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