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A Visualizable Evidence-Driven Approach for Authorship Attribution
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In: https://dl.acm.org/citation.cfm?doid=2744298.2699910 (2015)
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Abstract:
The Internet provides an ideal anonymous channel for concealing computer-mediated malicious activities ; as the network-based origins of critical electronic textual evidence (e.g. ; emails ; blogs ; forum posts ; chat logs ; etc.) can be easily repudiated. Authorship attribution is the study of identifying the actual author of the given anonymous documents based on the text itself ; and for decades ; many linguistic stylometry and computational techniques have been extensively studied for this purpose. However ; most of the previous research emphasizes promoting the authorship attribution accuracy ; and few works have been done for the purpose of constructing and visualizing the evidential traits. In addition ; these sophisticated techniques are difficult for cyber investigators or linguistic experts to interpret. In this article ; based on the End-to-End Digital Investigation (EEDI) framework ; we propose a visualizable evidence-driven approach ; namely VEA ; which aims at facilitating the work of cyber investigation. Our comprehensive controlled experiment and the stratified experiment on the real-life Enron email dataset demonstrate that our approach can achieve even higher accuracy than traditional methods ; meanwhile ; its output can be easily visualized and interpreted as evidential traits. In addition to identifying the most plausible author of a given text ; our approach also estimates the confidence for the predicted result based on a given identification context and presents visualizable linguistic evidence for each candidate.
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Keyword:
Algorithms; Design; Experimentation
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URL: http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=139629
https://doi.org/10.1145/2699910
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| 2 |
Ten Years of Rich Internet Applications: A Systematic Mapping Study, and Beyond
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| 3 |
On the localness of software
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In: http://macbeth.cs.ucdavis.edu/cache-model.pdf (2014)
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| 4 |
Approximate Semantic Matching of Events for the Internet of Things
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| 5 |
Q.: Scale based region growing for scene text detection
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In: http://www.stat.ucla.edu/%7Ejunhua.mao/papers/Scale_based_region_growing_ACM_MM13.pdf (2013)
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| 6 |
Gigatensor: scaling tensor analysis up by 100 times - algorithms and discoveries
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In: http://www.cs.cmu.edu/~christos/PUBLICATIONS/kdd12-gigatensor.pdf (2012)
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| 7 |
Distributional Semantics with Eyes: Using Image Analysis to Improve Computational Representations of Word Meaning
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In: http://clic.cimec.unitn.it/marco/publications/bruni-etal-acmmm-2012.pdf (2012)
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| 8 |
Integrating document clustering and . . .
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In: http://users.cis.fiu.edu/~taoli/pub/a14-wang.pdf (2011)
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| 9 |
Folks in folksonomies: social link prediction from shared metadata
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In: http://hal.archives-ouvertes.fr/docs/00/42/98/86/PDF/wsdm141-schifanella.pdf (2010)
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| 10 |
Semantic lexicon adaptation for use in query interpretation
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In: http://www.ra.ethz.ch/cdstore/www2010/www/p1167.pdf (2010)
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| 12 |
Classifying latent user attributes in twitter
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In: https://csc-869-mlog.googlecode.com/files/p37-rao.pdf (2010)
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| 13 |
Spatiotemporal mapping of Wikipedia concepts
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In: http://comupedia.org/adrian/articles/jcdl75-popescu.pdf (2010)
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| 14 |
An Information-extraction system for Urdu—a resource-poor language
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In: http://www.cedar.buffalo.edu/~rohini/Papers/ACM-TALIP.pdf (2010)
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| 15 |
Transliteration for resource-scarce languages
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In: http://www.cse.iitb.ac.in/~damani/papers/TALIP10/transliterationTALIP10.pdf (2010)
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| 16 |
MorphoNet: Exploring the Use of Community Structure for Unsupervised Morpheme Analysis
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In: http://clef.isti.cnr.it/2009/working_notes/morpho-papers/bernhard-paperCLEF2009.pdf (2009)
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| 17 |
OpinionMiner: a novel machine learning system for web opinion mining and extraction
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In: http://www.cedar.buffalo.edu/~rohini/Papers/KDD_Jin.pdf (2009)
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| 18 |
Word sense disambiguation: a survey
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In: http://www.dsi.uniroma1.it/~navigli/pubs/ACM_Survey_2009_Navigli.pdf (2009)
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| 19 |
Sentiment analysis of blogs by combining lexical knowledge with text classification
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In: http://www.prem-melville.com/publications/pooling-multinomials-kdd09.pdf (2009)
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| 20 |
A 2-poisson model for probabilistic coreference of named entities for improved text retrieval
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In: http://www.comp.nus.edu.sg/~nght/pubs/sigir09.pdf (2009)
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