Motivation. Motivation. Motivation. Finding Deceptive Opinion Spam by Any Stretch of the Imagination
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1 Finding Deceptive Opinion Spam by Any Stretch of the Imagination Myle Ott, 1 Yejin Choi, 1 Claire Cardie, 1 and Jeff Hancock 2! Dept. of Computer Science, 1 Communication 2! Cornell University, Ithaca, NY! Motivation Consumers increasingly rate, review and research products online! Potential for opinion Disruptive opinion Deceptive opinion Motivation Consumers increasingly rate, review and research products online! Potential for opinion Disruptive opinion Deceptive opinion Motivation Consumers increasingly rate, review and research products online! Potential for opinion Disruptive opinion Deceptive opinion
2 Motivation Consumers increasingly rate, review and research products online! Potential for opinion Disruptive opinion Deceptive opinion Motivation Which of these two hotel reviews is deceptive opinion spam?! Motivation Which of these two hotel reviews is deceptive opinion spam?! Answer:! Overview Motivation! Gathering Data! Human Performance!! Conclusion!
3 Gathering Data Label existing reviews! Can t manually do this! Duplicate detection (Jindal and Liu, 2008)! Create new reviews! Mechanical Turk! Gathering Data Label existing reviews! Can t manually do this! Duplicate detection (Jindal and Liu, 2008)! Create new reviews! Mechanical Turk! Gathering Data Label existing reviews! Can t manually do this! Duplicate detection (Jindal and Liu, 2008)! Create new reviews! Mechanical Turk! Gathering Data Label existing reviews! Can t manually do this! Duplicate detection (Jindal and Liu, 2008)! Create new reviews! Mechanical Turk!
4 Gathering Data Label existing reviews! Can t manually do this! Duplicate detection (Jindal and Liu, 2008)! Create new reviews! Mechanical Turk! Gathering Data Mechanical Turk! 20 hotels! 20 reviews / hotel! Offer $1 / review! 400 reviews! Gathering Data Mechanical Turk! 20 hotels! 20 reviews / hotel! Offer $1 / review! 400 reviews! Gathering Data Mechanical Turk! 20 hotels! 20 reviews / hotel! Offer $1 / review! 400 reviews!
5 Gathering Data Gathering Data Mechanical Turk! 20 hotels! 20 reviews / hotel! Offer $1 / review! 400 reviews! Mechanical Turk! 20 hotels! 20 reviews / hotel! Offer $1 / review! 400 reviews! Average time spent:" > 8 minutes! Average length:" > 115 words! Gathering Data 400 truthful reviews! TripAdvisor.com! Lengths distributed similarly to deceptive reviews! Overview Motivation! Gathering Data! Human Performance!! Conclusion!
6 Human Performance Why bother?! Validates deceptive opinions! Baseline to compare other approaches! Human Performance Why bother?! Validates deceptive opinions! Baseline to compare other approaches! Human Performance Human Performance Why bother?! Validates deceptive opinions! Baseline to compare other approaches! 80 truthful and 80 deceptive reviews! 3 undergraduate judges! Truth bias! 2 meta-judges!
7 Human Performance Human Performance Performed at chance! (p-value = 0.1)! 80 truthful and 80 deceptive reviews! 3 undergraduate judges! Truth bias! 2 meta-judges! Performed at chance! 80 truthful and (p-value 80 deceptive = 0.5)! reviews! 3 undergraduate judges! Truth bias! 2 meta-judges! Human Performance Human Performance 80 truthful and 80 deceptive reviews! 3 undergraduate judges! Truth bias! 2 meta-judges! Classified fewer than 12% of opinions as deceptive!! 80 truthful and 80 deceptive reviews! 3 undergraduate judges! Truth bias! 2 meta-judges!
8 Human Performance Human Performance 80 truthful and 80 deceptive reviews! 3 undergraduate judges! Truth bias! 2 meta-judges! 80 truthful and 80 deceptive reviews! 3 undergraduate judges! Truth bias! 2 meta-judges! Human Performance 80 truthful and 80 deceptive reviews! No more truth bias!! 3 undergraduate judges! Truth bias! 2 meta-judges! Overview Motivation! Gathering Data! Human Performance!! Conclusion!
9 Three feature sets! Genre identification! Psycholinguistic deception detection! Text categorization! Linear SVM! Three feature sets! Genre identification! Psycholinguistic deception detection! Text categorization! Linear SVM! Genre identification! 48 part-of-speech (PoS) features! Baseline automated approach! Expectations! Truth similar to informative writing! Deception similar to imaginative writing! Genre identification! 48 part-of-speech (PoS) features! Baseline automated approach! Expectations! Truth similar to informative writing! Deception similar to imaginative writing!
10 Genre identification! 48 part-of-speech (PoS) features! Baseline automated approach! Expectations! Truth similar to informative writing! Deception similar to imaginative writing! Genre identification! 48 part-of-speech (PoS) features! Baseline automated approach! Expectations! Truth similar to informative writing! Deception similar to imaginative writing! Outperforms human judges!! (p-values = {0.06, 0.01, 0.001})!
11 e.g., best, finest! Rayson et. al. (2001)! Informative on left, imaginative on right! Rayson et. al. (2001)! e.g., most! Informative on left, imaginative on right! Linguistic Inquire and Word Count (Pennebaker et al., 2007)! Counts instances of ~4,500 keywords! Regular expressions, actually! Keywords are divided into 80 dimensions across 4 broad groups! Linguistic Inquire and Word Count (Pennebaker et al., 2007)! Counts instances of ~4,500 keywords! Regular expressions, actually! Keywords are divided into 80 dimensions across 4 broad groups!
12 Linguistic Inquire and Word Count (Pennebaker et al., 2007)! Counts instances of ~4,500 keywords! Regular expressions, actually! Keywords are divided into 80 dimensions across 4 broad groups! Linguistic processes! e.g., average number of words per sentence! Psychological processes! e.g., talk, happy, know, feeling, eat! Personal concerns! e.g., job, cook, family! Spoken categories! e.g., yes, umm, blah! Linguistic processes! e.g., average number of words per sentence! Psychological processes! e.g., talk, happy, know, feeling, eat! Personal concerns! e.g., job, cook, family! Spoken categories! e.g., yes, umm, blah! Linguistic processes! e.g., average number of words per sentence! Psychological processes! e.g., talk, happy, know, feeling, eat! Personal concerns! e.g., job, cook, family! Spoken categories! e.g., yes, umm, blah!
13 Linguistic processes! e.g., average number of words per sentence! Psychological processes! e.g., talk, happy, know, feeling, eat! Personal concerns! e.g., job, cook, family! Spoken categories! e.g., yes, umm, blah! Outperforms PoS!! (p-value = 0.02)! Text categorization (n-grams)! Unigrams! Bigrams +! Includes unigrams! Trigrams +! Includes unigrams and bigrams!
14 Outperforms all other methods!! Spatial difficulties" (Vrij et al., 2009)! Psychological distancing (Newman et al., 2003)! Spatial difficulties" (Vrij et al., 2009)! Psychological distancing (Newman et al., 2003)!
15 Spatial difficulties" (Vrij et al., 2009)! Psychological distancing (Newman et al., 2003)! Spatial difficulties" (Vrij et al., 2009)! Psychological distancing (Newman et al., 2003)! Spatial difficulties" (Vrij et al., 2009)! Psychological distancing (Newman et al., 2003)! Overview Motivation! Gathering Data! Human Performance!! Conclusion!
16 Conclusion First large-scale gold-standard deception dataset! Evaluated human deception detection performance! Developed automated classifiers capable of nearly 90% accuracy! Relationship between deceptive and imaginative text! Importance of moving beyond universal deception cues! Conclusion First large-scale gold-standard deception dataset! Evaluated human deception detection performance! Developed automated classifiers capable of nearly 90% accuracy! Relationship between deceptive and imaginative text! Importance of moving beyond universal deception cues! Conclusion First large-scale gold-standard deception dataset! Evaluated human deception detection performance! Developed automated classifiers capable of nearly 90% accuracy! Relationship between deceptive and imaginative text! Importance of moving beyond universal deception cues! Conclusion First large-scale gold-standard deception dataset! Evaluated human deception detection performance! Developed automated classifiers capable of nearly 90% accuracy! Relationship between deceptive and imaginative text! Importance of moving beyond universal deception cues!
17 Conclusion First large-scale gold-standard deception dataset! Evaluated human deception detection performance! Developed automated classifiers capable of nearly 90% accuracy! Relationship between deceptive and imaginative text! Importance of moving beyond universal deception cues! Thank you. Questions? First large-scale gold-standard deception dataset! Evaluated human deception detection performance! Developed automated classifiers capable of nearly 90% accuracy! Relationship between deceptive and imaginative text! Importance of moving beyond universal deception cues!
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