DECEPTION DETECTION COMPUTATIONAL APPROACHES

Transcription

1 DECEPTION DETECTION COMPUTATIONAL APPROACHES

2 OVERVIEW

3 DEFINING DECEPTION To intentionally cause another person to have or continue to have a false belief that is truly believed to be false by the person intentionally causing the false belief by brining about evidence on the basis of which the other person has or continues to have that false belief

4 WHO STUDIES DECEPTION Experts in many fields: Psychology Psychiatry Sociology Criminology Philosophy Anthropology

5 WHERE IS DECEPTION DETECTION AN ISSUE? police security border crossing, customs, asylum interviews congressional hearings financial reporting legal depositions human resource evaluations

6 WHERE IS DECEPTION DETECTION AN ISSUE? predatory communication: internet scams and fraud fake reviews & opinion spam identity theft cyber-pedofilia internet trolls fake news

7 TYPES OF DECEPTION Two general types of deceptive behavior: unplanned: people not fully aware of the person they will interact with, can t guarantee outcomes planned: people have time to think, plan, rehearse in order to appear truthful. Harder to detect than unplanned.

8 MEDIUMS OF COMMUNICATION Deceptiveness in media relates to three elements: Synchronicity: to what extend the medium provides real time communication? Distribution: are the people communicating in the same physical location? Recordability: is the medium automatically recordable?

9 DECEPTION CUES Being involved in deceptive behavior is more challenging than being truthful: emotionally: fear & threat, guilt & shame, duping delight mentally: creating a believable and consistent story and remembering it physically: deceivers attempt to control physical signs of deceptive behavior

10 TYPES OF DECEPTION CUES Visual: any physical behavior Vocal: elements that accompany verbal communications (tone/tension, pith, rhythm) Verbal: wording and structure

11 WHERE DOES NLP COME IN? Verbal (especially written) deception cues Identifying deception in text - no visual/vocal cues Deceiver must make words and patterns of words do the deception work This leads to different language patterns Researchers argue that verbal cues are most promising

12 DIFFERENCE FROM OTHER NLP DOMAINS Human judgement!= Gold Standard: Computational methods can produce systems that detect deception better than humans Humans are notoriously bad at detecting deception - and are often biased This also makes obtaining data extremely difficult

13 COMPUTATIONAL DECEPTION DETECTION WORKSHOPS EACL 2012: Workshop on Computational Approaches to Deception Detection. Avignon, France. April 23, 2012 NAACL 2016: Workshop on Computational Approaches to Deception Detection. San Diego CA, USA. June 17, 2016

14 MAIN CHALLENGES

15 CHALLENGE: DATA The ground truth problem: to be able to recognize the lie, the researcher must not only identify distinctive behavior when someone is lying but must ascertain whether the statement being made is true or not

16 TRADITIONAL APPROACHES: SANCTIONED DECEPTION ex. participants are asked about their beliefs concerning a given topic, such as abortion, and then have to convince a partner that they hold the opposite belief ex. participants instructed to engage in a mock crime and then lie about it ex. participants asked to describe a person they cannot stand as if it was their best friend

17 TRADITIONAL APPROACHES: SANCTIONED DECEPTION PROS: large degree of experimental control no need to identify unknown lies

18 TRADITIONAL APPROACHES: SANCTIONED DECEPTION CONS: people given permission to lie, thus not an actual lie unless high stakes are involved the data produced does not replicate a typical lying situation if high stakes are involved - serious ethics violations lack of generalizability across studies

19 TRADITIONAL APPROACHES: UNSANCTIONED DECEPTION Diary & survey studies Require self-reported recall of deception Several biases likely Doesn t collect the language of the lie

20 TRADITIONAL APPROACHES: UNSANCTIONED DECEPTION Cheating procedures participants incentivized (not instructed) to cheat on a task and then asked about it most admit to cheating, a small fraction lie extremely effort-intensive given number of deceptions produced

21 NON-GOLD STANDARD APPROACHES Manual annotation: ex. annotation of online reviews as in other domains - expensive, especially for high stakes human ability to detect detection is very poor, often not better than chance truth bias - human judges overtrust

22 NON-GOLD STANDARD APPROACHES Heuristically labeled: i.e. based on assumptions specific to domain ex. duplicate reviews acceptable approximation for certain domains

23 OTHER APPROACHES Crowdsourcing using platforms such as Mechanical Turk ex. participants asked to prepare speeches for debate on a controversial topic, first with their true opinion then with the opposite view ex. participants recruited in a manner that replicates actual recruitment for writing fake reviews

24 OTHER CORPORA ISSUES Privacy: ex. chat logs, social media, etc. ex. high stakes data - even if identifiable data is removed, subjects wouldn t want their data public Amenability to other domains: data collected for one domain likely not useful for others

25 CASE STUDY 1 MATTHEW L NEWMAN, JAMES W. PENNEBAKER, DIANE S. BERRY, JANE M. RICHARDS, LYING WORDS: PREDICTING DECEPTION FROM LINGUISTIC STYLES.

26 NEWMAN ET AL (2003) One of the corner-stone studies in this domain The first (of many) studies of deception that used the LIWC program Sanctioned (laboratory) deception data approach

27 DATA COLLECTION 5 subcorpora created: videotaped abortion attitudes typed abortion attitudes handwritten abortion attitutes feelings about friends mock crime

28 DATA PREPARATION & LIWC Data analyzed using Linguistic Inquiry and Word Count (Pennebaker et al, 2001), a.k.a. LIWC Text analysis program that counts words in psychologically meaningful categories 2200 words and word stems grouped into 72 categories relevant to psychological processes Widely used in psychology and psycholinguists

29 TRAINING & RESULTS Used 29 of the 72 LIWC categories Logistic regression trained on 4 of the 5 subcorpora and tested on the 5th 67% accuracy when topic constant, 61% overall Poor performance due to mixed modes of communication

30 CHARACTERISTICS OF DECEPTIVE LANGUAGE: fewer 1st person pronouns (attempt to disassociate ) more negative emotion words (due to tension & guilt) fewer exclusive words, such as but, except, without (lower cognitive complexity) more motion verbs (simple descriptions)

31 CASE STUDY 2 Myle Ott, Yejin Choi, Claire Cardi, and Jeff Hancock, Finding Deceptive Opinion Spam by Any Stretch of the Imagination.

32 OTT ET AL 2011 detecting deceptive opinion spam - fictitious opinions that have been deliberately written to sound authentic, in order to deceive the reader human judges perform roughly at chance with opinion spam detection, thus no gold standard data

33 DATA COLLECTION First study to use crowdsourcing to manufacture gold standard data Developed a prompt for mechanical turk that mimics actual prompts that solicit fake reviews: Imagine you work for the marketing department of a hotel. Your boss asks you to write a fake review for the hotel (as if you were a customer) to be posted on a travel review website. The review needs to sound realistic and portray the hotel in a positive light. Look at their website if you re not familiar with the hotel

34 THREE FRAMINGS OF THE PROBLEM Text categorization task: using n-gram based Naive Bayes and SVM classifiers Psycholinguistic deception detection: using LIWC categories A problem of genre identification: imaginative vs informative genres through frequency distributions of POS tags

35 RESULTS!!!!

36 FINDINGS Truthful Deceptive more sensorial and concrete language increased focus on external aspects (husband, business, etc) more specific about spatial configurations more positive emotion terms * increased 1st person singular * * in contrast to previous studies

37 CASE STUDY 3 Svitlana Volkova and Eric Bell, Account Deletion Prediction on RuNet: A Case Study of Suspicious Twitter Accounts Active During the Russian-Ukrainian Crisis.

38 VOLKOVA ET AL, 2016 Automatic prediction of deleted accounts on RuNet during the Russian-Ukranian crisis, i.e. fraudulent (troll) accounts Legions of pro-russia trolls aka Kremlin troll army Unlike social bots or spam accounts, troll profiles are created to look like real users

39 DATA COLLECTION Sampled Twitter accounts with crisis-related keywords in Russian and Ukranian from Mar Mar 2015 Re-crawled several months later - 30% of these accounts have been deleted Two data sets: deleted and non-deleted accounts

40 DEEP LINGUISTIC ANALYSIS OF CONTENT BoW features processed to account for morphology LSA(Latent Semantic Analysis) features - dimensionality reduction of BoW features LDA features - topic analysis Embeddings using Word2Vec Affect features obtained through sentiment classification

41 FEATURE TYPES Profile/account, ex. number of friends/followers Visual, ex. profile background color Syntactic, ex. aver. tweet length, hashtags, etc Network, ex. retweets and mentions Lexical Affect

42 RESULTS

43 FINDINGS Lexical features most predictive In tweets frequency based ftrs > binary: not only important what users say, but how much they say it In hashtags and mentions frequency based = binary ftrs: not important how much the users use some hashtags or mentions but whether they use them or not

44 CHARACTERISTICS OF DELETED ACCOUNTS Less followers, more friends Shorter bios, longer user names Shorter tweets buts elongated words, capitalized words, and repeated punctuation Less positive tweets, more negative tweets

45 CASE STUDY 4 Victoria L. Rubin and Tatiana Vashchilko, Identification of Truth and Deception in Text: Application of Vector Space Model to Rhetorical Structure Theory.

46 RUBIN ET AL, 2012 The first study to consider discourse/pragmatic features of deception What is the impact of the relations between discourse constituent parts on the discourse composition of deceptive and truthful messages?

47 RUBIN ET AL, 2012 Use the Rhetorical Structure Theory (RST) framework to identify differences between truthful and deceptive stories in terms of coherence and structure Uses Vector Space Model to assess each story s position in multi-dimensional RST space with respect to truth/deception centers

48 RST-VSM METHODOLOGY RST analysis captures coherence of a story in terms of functional relations among different meaningful text units, and describes a hierarchical structure of each story Examples of relations: Volitional-cause, elaboration, circumstance, concession, etc. Each story is converted to a set of RST relations connected in a hierarchical manner

49 RST-VSM METHODOLOGY 36 elicited personal stories, self-ranked as completely truthful or completely deceptive Manually analyzed by assigning RST discourse relations RST relations transformed into vector space description and similarity space description The similar space description is mapped into clusters

50 RESULTS!! RST dimensions systematically differentiate truthful and deceptive stories: 2 clusters are completely truthful/ deceptive, other 2 are a mixture with one prevailing

51 FINDINGS Truthful stories have a lower average of text units per statement Frequencies differ for about 1/3 of RST relations!! Deceptive stories conjunction, elaboration, evaluation, list, means, non-volitional cause, nonvolitional result, sequence, solutionhood Truthful stories volitional result, volitional cause, purpose, interpretation, concession, circumstance, antithesis

52 CASE STUDY 5 Victoria Rubin, Niall Conroy, Yimin Chen and Sarah Cornwell, Fake News or Truth? Using Satirical Cues to Detect Potentially Misleading News.

53 RUBIN ET AL, 2016 Built a system that detects satirical news (i.e. The Onion, the Beaverton) Satire - a type of deception that intentionally incorporates cues revealing its deceptiveness Intended to be found out by at least a subset of audience

54 METHODOLOGY 360 news articles from satirical and legitimate sources, 12 topics across 4 domains SVM classification on unigrams and bigrams plus selected features

55 ENGINEERED FEATURES Absurdity: unexpected introduction of new named entities in the final sentence of satirical news Humor: punchline detection method based on distance between first and last sentences Negative affect based on LIWC Grammar and punctuation

56 RESULTS!!! shallow syntax features (punctuation and grammar) most predictive, possibly due to more complex sentence structure in satirical content

57 SUMMARY

58 SUMMARY Data is the main challenge Data is difficult to produce and often not applicable across domains/mediums In the recent years, some efforts to create large corpora Characteristics of deception also differ by domain

59 SUMMARY In light of recent events, there s a pressing need to distinguish between fake (non-satirical) and real news, actual and alternative facts, etc. Does this type of deception lend itself to computational approaches? If so, how could we get the data? Could we use other heuristics (i.e. news sources)?