Sadia Afroz: Detecting Hoaxes, Frauds, and Deception in Writing Style Online

Detec%ng Decep%on in  Wri%ng Style Sadia Afroz, Michael Brennan and Rachel Greenstadt.  Privacy, Security and Automa%on Lab  Drexel University 

Overview •  Authorship recogni%on •  Authorship recogni%on in adversarial  environment •  Decep%on detec%on •  Experiments on diﬀerent datasets 

Authorship recogni%on                  Who wrote the document? 

Authorship recogni%on Stylometry:    –  An authorship recogni%on system based solely on  wri%ng style.  –  Not handwri%ng  –  Only linguis%c style: word choice, sentence length,  parts‐of‐speech usage, … 

Why it works? •  Everybody has learned language diﬀerently 

How regular authorship recogni%on  works  Extract features  Machine Learning  System 

Extract   Determine  features  authorship  Machine Learning  System Document of  unknown authorship 

Assump%ons •  Wri%ng style is invariant.  –  It’s like a ﬁngerprint, you can’t really change it.  –  Authorship recogni%on can iden%fy you if there  are suﬃcient wri%ng samples and a set of  suspects. 

Adversarial AUacks •  Imita%on or framing aUack  –  Where one author imitates another author  –  Par%cipants were asked to imitate Cormac  McCarthy in wri%ng about their day •  Obfusca%on aUack  –  Where an author hides his regular style  –  Par%cipants were asked to write about their  neighborhood in a diﬀerent style M. Brennan and R. Greenstadt. Prac%cal aUacks against authorship recogni%on techniques. In Proceedings of the Twenty‐First Conference on Innova%ve Applica%ons of Ar%ﬁcial Intelligence (IAAI), Pasadena, CA, 2009. 

Accuracy in detec6ng authorship of regular  documents  1 0.9 0.8 0.7 0.6 0.5  More than 80% accurate in detec%ng  9‐Feature (NN)  authorship with 40 authors in regular  Synonym‐Based 0.4  document  Writeprints Baseline (SVM) 0.3  Random 0.2 0.1  0  5  10  15  20  25  30  35  40  Number of Authors 

Accuracy in detec6ng authorship of  Obfuscated documents  1 0.9 0.8 0.7 0.6  9‐Feature (NN) 0.5  Accuracy is less than random chance   Synonym‐Based 0.4  in obfuscated documents  Writeprints Baseline (SVM) 0.3  Random 0.2 0.1  0  5  10  15  20  25  30  35  40  Number of Authors 

Accuracy in detec6ng authorship of  Imitated documents  1 0.9 0.8 0.7 0.6  9‐Feature (NN) 0.5  Accuracy is less than random chance   Synonym‐Based 0.4  in imitated documents  Writeprints Baseline (SVM) 0.3  Random 0.2 0.1  0  5  10  15  20  25  30  35  40  Number of Authors 

Can we detect Stylis%c Decep%on?  Imitated  Regular  Obfuscated 

Extended‐Brennan‐Greenstadt   Corpus •  56 authors   –  12 of the par%cipants are from Drexel University  –  The rest are paid workers from Amazon mechanical turk •  Three kinds of wri%ng samples  –  Regular wri%ngs (5000 words)  –  Imitated wri%ng  •  A 500‐word ar%cle describing a day   •  Imitate Cormac McCarthy from `The Road’  –  Obfuscated wri%ng  •  A 500‐word ar%cle  describing neighborhood  •  Hide own wri%ng style 

Detec%ng stylis%c decep%on is possible 100  98  94.5  89.5  90  95.7  85  80  75.3  70  59.9  60  48  Writeprint, SVM  50  43  Lying‐detec%on, J48  40  9‐feature set, J48  30  20  10  0  Regular  Imita%on  Obfusca%on 

Feature Changes in Imita6on and Obfusca6on  Personal pronoun  Sentence count  Par%cle  Short Words  Verb  Unique words  Adverb  Existen%al there  Imita%on  Average syllables per word  Obfusca%on  Average word length  Adjec%ve  Cardinal number Gunning‐Fog readability index  Average sentence length  ‐80  ‐60  ‐40  ‐20  0  20  40  60  80  100 

Problem with the dataset:  Topic Similarity •  All the decep%ve documents were of same  topic.  5,$6.)78)9+,$($-.)8$%.($)&$.)+-)9$.$60-1) %9:$(&%(+%4)%.;7(&;+3) $"•  Non‐content‐specific  !#," !#+" !#*" !"#$%&($) features have same   !#)" !#(" =>3/0<1<" !#" ?5@-<08" !#&"effect as content‐specific   A23/53/" !#%" !#$" !"features.  -.-/0123" 4567804" *+,$($-.)/(+0-1)2%#34$&) 29:7;<0123"

Hemingway‐Faulkner Imita%on   Corpus •  Ar%cles from the Interna%onal Imita%on  Hemingway Contest (2000‐2005) •  Ar%cles from the Faux Faulkner Contest  (2001‐2005) •  Original excerpts of Ernest Hemingway and  William Faulkner 

Decep%on detec%on is possible even when the topic is not similar •  81.2% accurate in detec%ng imitated  documents. 

Long term decep%on:  A Gay Girl In Damascus Thomas MacMaster.  Fake picture of Amina Arraf. –  Original author was a 40‐year old American ci%zen,  Thomas MacMaster. –  Pretended to be a Syrian gay woman, Amina Arraf. –  The author worked for at least 5 years to create a  new style. 

Long term decep%on is hard to detect •  None of the blog posts were found to be  decep%ve. •  But regular authorship recogni%on can help. •  We tried to aUribute authorship of the blog  posts using Thomas (as himself), Thomas (as  Amina), BriUa (Thomas’s wife). 

Long term decep%on  Authorship recogni%on of the blog  posts Thomas MacMaster.  Amina Arraf  BriUa (Thomas’s wife)  54%  43%  3% 

Future works •  Intrusion detec%on •  Social spam detec%on •  Iden%fying quality discourse 

Two Tools •  JStylo: Authorship Recogni%on Analysis Tool. •  Anonymouth: Authorship Recogni%on Evasion  Tool. •  Free, Open Source. (GNU GPL) •  Alpha releases available today at  hUps://psal.cs.drexel.edu  –  Migra%ng to GitHub soon. 

Privacy, Security and Automa%on Lab  (hUps://psal.cs.drexel.edu) •  Faculty  –  Dr. Rachel Greenstadt •  Graduate Students  –  Sadia Afroz (Decep%on Detec%on Lead)  –  Diamond Bishop  –  Michael Brennan  –  Aylin Caliskan  –  Ariel Stolerman (JStylo Lead Developer) •  Undergraduate Students  –  Pavan Kantharaju  –  Andrew McDonald (Anonymouth Lead Developer) 

Sadia Afroz: Detecting Hoaxes, Frauds, and Deception in Writing Style Online

by pamselle on Feb 21, 2012

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Sadia Afroz: Detecting Hoaxes, Frauds, and Deception in Writing Style Online Presentation Transcript