Detecting Deception through Linguistic Analysis

Abstract

Tools to detect deceit from language use pose a promising avenue for increasing the ability to distinguish truthful transmissions,
transcripts, intercepted messages, informant reports and the like from deceptive ones. This investigation presents preliminary
tests of 16 linguistic features that can be automated to return assessments of the likely truthful or deceptiveness of a piece
of text. Results from a mock theft experiment demonstrate that deceivers do utilize language differently than truth tellers
and that combinations of cues can improve the ability to predict which texts may contain deception.

Full text

Detecting Deception through Linguistic Analysis
Judee K. Burgoon
1
, J. P. Blair
2
, Tiantian Qin
1
, Jay F. Nunamaker, Jr
1
,
1
Center for the Management of Information, University of Arizona
2
Department of Criminal Justice, Michigan State University
Submitted to the Intelligence and Security Informatics Symposium, Tucson, June 2003.
Portions of this research were supported by funding from the U. S. Air Force Office of Scientific
Research under the U. S. Department of Defense University Research Initiative (Grant #F49620-01-1-
0394). The views, opinions, and/or findings in this report are those of the authors and should not be
construed as an official Department of Defense position, policy, or decision.
Abstract.
Tools to detect deceit from language use pose a promising avenue for increasing the ability to
distinguish truthful transmissions, transcripts, intercepted messages, informant reports and the like from deceptive ones.
This investigation presents preliminary tests of 16 linguistic features that can be automated to return assessments of the
likely truthful or deceptiveness of a piece of text. Results from a mock theft experiment demonstrate that deceivers do
utilize language differently than truth tellers and that combinations of cues can improve the ability to predict which
texts may contain deception.
1 Introduction
One of the most daunting challenges facing intelligence and law enforcement communities in the wake
of 9/11 is anticipating and preventing terrorist attacks. Doing so requires sifting through a daily avalanche
of information and communications to discriminate good information from bad, and deceptive
communications from truthful ones. Much of this task falls to human analysts. Aside from the enormity of
the task, the empirical evidence is compelling that humans, even professionally trained ones, are typically
very poor at detecting deception and fallacious information [1, 2, 3]. Moreover, evidence is building that
detection abilities are even more faulty when communication is computer-mediated, especially if is text-
based such as with e-mail [4, 5]. Increasing reliance worldwide on electronic communications thus carries
with it the very real likelihood of further decrements in detection accuracy and heightened vulnerabilities to
national and international security. Human information processors are in serious need of tools that can
assist them in filtering information and alerting them to suspicious information. Aside from the security
implications, such tools would also be of great benefit to law enforcement in dealing with a wide array of
criminal investigations.
Our program of research, funded by the Department of Defense, has begun to develop such tools
by examining linguistic features and content characteristics of texts for reliable indicators of deceit. These
indicators can then be incorporated into software tools that automate their detection and subject them to
statistical analysis for probabilistic estimates of truthfulness or deceptiveness. Decades of research have
confirmed that there are few indicators of deceit that remain invariant across genres of communication,
situations, communicators, cultures, and other features of communication contexts. Yet combinations of
indicators have shown to have good predictive ability in specified contexts [6]. The research to be reported
here was guided by the objectives of identifying those indicators that are (1) the least context-sensitive and
(2) the most amenable to automation. We present preliminary results from a mock theft experiment that is
still in progress, our purposes being to illustrate the promise of examining text-based linguistic indicators

and of examining such indicators using a particular statistical approach that examines combinations of cues.
2 Background
Two experiments from our research program predate the one to be reported here. One was
modeled after the familiar Desert Survival Problem, in which pairs of participants were given a scenario in
which their jeep had crashed in the Kuwaiti desert, read material we developed from an Army field manual
that we entitled, “Imperative Information for Surviving in the Desert,” and then were asked to arrive at a
consensus on the rank-ordering of salvageable items in terms of their importance to survival. The task was
conducted via email over the course of several days. In half of the pairs, one person was asked to deceive
the partner by advocating choices opposite of what the experts recommend (e.g., discarding bulky clothing
and protective materials so as to make walking more manageable). Partners discussed the rankings and
their recommendations either face-to-face or using a computer-mediated form of communication such as
text chat, audioconferencing, or videoconferencing. All discussions were recorded and transcribed then
subjected to linguistic analysis of such features as number of words, number of sentences, number of
unique words (lexical diversity), emotiveness, and pronoun usage. Of the 27 indicators that were examined,
several proved to reliably distinguish truth tellers from deceivers. Deceivers were more likely to use longer
messages but with less diversity and complexity, and greater uncertainty and “distancing” in language use
than truth tellers. These results revealed that systematic differences in language use could help predict
which messages originated from deceivers and which, from those telling the truth.
The second experiment was designed as a pilot effort for the experiment to be reported below. In
this experiment, participants staged a mock theft and were subsequently interviewed by untrained and
trained interviewers via text chat or face-to-face (FtF) interaction [4, 5]. The FtF interactions were later
transcribed, and the transcripts and chats were submitted to linguistic analysis on the same features as noted
above, plus several others that are available in the Grammatik tool within WordPerfect. Due to the small
sample size, none of the differences between innocents (truth tellers) and thieves (deceivers) were
statistically significant, but patterns were suggestive of deceivers tending toward briefer messages (fewer
syllables, words, and sentences; shorter and simpler sentences) of greater complexity (e.g., greater
vocabulary and sentence complexity, lower readability scores) than truth tellers (higher Flesch-Kincaid,
sentence complexity, vocabulary complexity, syllables per word).
The patterns found in these first efforts suggested that we should expect to find many linguistic
differences between deceivers and truth tellers with a larger, and well-designed experiment. We therefore
hypothesized that deceptive senders display higher (a) quantity, (b) non
immediacy, (c) expressiveness,
(d) informality, and (e) affect; and less (f)
complexity, (g) diversity, and (h) specificity of language
in their messages than truthful senders.
3 Method
Students were recruited from a multi-sectioned communication class by offering them credit for
participation and the chance to win money if they were successful at their task. Half of the students were
randomly assigned to be “thieves,” i.e., those who would be deceiving about a theft, and the other half
became “innocents,” i.e., those who would be telling the truth.
Interviewees in the deceptive condition were assigned to “steal” a wallet that was left in a
classroom. In the truthful condition, interviewees were told that a “theft” would occur in class on an
assigned day. All of the interviewees and interviewers then appeared for interviews according to a pre-
assigned schedule. We attempted to motivate serious engagement in the task by offering interviewers $10
if they could successfully detect whether their interviewee was innocent or guilty and successfully detect
whether they were deceiving or telling the truth on a series of the interview questions. In turn, we offered
interviewees $10 if they convinced a trained interviewer that they were innocent and that their answers to
several questions were truthful. An additional incentive was a $50 prize to be awarded to the most
successful interviewee.
Interviewees were then interviewed by one of three trained interviewers under one of three
modalities— Face to Face (FtF), text chat, or audioconferencing. The interviews followed a standardized

Behavioral Analysis Interview format that is taught to criminal investigators [7]. Interviews were
subsequently transcribed and submitted to linguistic analysis. Clusters of potential indicators, all of which
could be automatically calculated with a shallow parser (Grok or Iskim) or could use a look-up dictionary,
were included. The specific classes of cues and respective indicators were as follows:
1. Quantity (number of syllables, number of words, number of sentences)
2. Vocabulary Complexity (number of big words, number of syllables per word)
3. Grammatical Complexity (number of short sentences, number of long sentences, Flesh-Kincaid grade
level, average number of words per sentence, sentence complexity, number of conjunctions)
4. Specificity and Expressiveness (emotiveness index, rate of adjectives and adverbs, number of affective
terms)
4 Results
In the following two subsections, we investigate data from two perspectives: analysis of individual cues
and cluster analysis. To analyze how well individual cues distinguish messages of deceivers from those of
truth tellers, we conducted multivariate analyses of related groups of cues followed by directional t-tests on
individual cues to identify which ones contribute most to differentiating deceivers and truthful tellers. The
cluster analysis answers the question of whether combinations of cues (in a hierarchy structure) can
improve overall ability to differentiate deceivers from truth tellers. Furthermore, unlike the traditional
statistical cluster analysis, we used a data-mining algorithm -- C4.5 [8] -- to cluster the cues and obtain a
hierarchical tree structure. In this way, we fulfilled the “automatic” requirement of automating deception
detection.
4.1 Individual cue analysis
Results were based on data of 41 subjects whose modality was text chat (txt) or audio. (In the future,
data for face-to-face (FtF) will be included after those sessions have been completed and all video files are
transcribed). Among 41 subjects, 29 interacted via txt and 20, via audio; 26 were “thieves” (i.e., deceivers)
and 23 were “innocents (i.e., truth tellers). Table 1 presents descriptive statistics for the 16 cues that were
analyzed.
Table 1. Means (Deviations) for 16 cues
Deceiver Truthful-teller Cues
Txt Audio Txt Audio
Syllables 121.06(82.723) 146.44(75.583) 144.58(86.732) 189.27(86.383)
Words 86.06(58.297) 106.00(53.708) 106.25(64.435) 140.36(62.094)
Sentences 5.88(3.621) 7.11(4.372) 6.00(5.117) 6.91(2.948)
Short sentences 2.82(2.298) 3.11(3.140) 2.58(3.872) 2.09(1.57)
Long sentences 0.24(0.437) 0.44(0.520) 0.92(1.16) 1.09(1.22)
Simple sentences 1.29(1.21) 2.11(2.205) 2.20(2.64) 1.36(0.920)
Big words 6.29(6.64) 6.78(4.32) 6.55(3.77) 8.28(5.71)
Average syllables per word 1.42(0.107) 1.36(0.089 1.36(0.068) 1.34(.096)
Average words per sentence 15.45(6.53) 16.93(9.82) 21.02(7.76) 22.94(13.36)
Flesch-Kincaid grade level 7.28(2.839) 7.32(3.986) 8.91(3.223) 8.82(3.087)
Sentence complexity 41.12(16.86) 43.33(18.66) 52.91(26.44) 49.73(24.08)
Vocabulary complexity 9.88(7.54) 7.11(4.48) 7.27(4.60) 7.27(4.22)
Conjunctions 4.47(3.48) 5.30(4.03) 5.00(3.82) 9.27(7.08)
Rate of Adjectives & Adverbs 0.12(0.014) 0.12(0.016) 0.12(0.014) 0.13(0.014)
Emotiveness 0.29(0.100) 0.29(0.141) 0.29(.073) 0.27(.160)
Affect 0.24(0.562) 0(0) 0.55(0.820) 0.33(0.651)
Results of the multivariate tests and t-tests are shown in Table 2. (For plots of means by deception
condition and modality, see the figures in the appendix.) The multivariate analysis on indicators of quantity

of language produced a significant multivariate effect for deception (p = .033) and no modality by
deception interaction. Deceivers said or wrote less than truth tellers.
The multivariate analysis of indicators of complexity at both the sentence level (simple sentences,
long sentences, short sentences, sentence complexity, Flesch-Kincaid grade level, number of conjunctions,
average-words-per-sentence (AWS)) and vocabulary level (vocabulary complexity, number of big words,
average-syllables-per-word (ASW)) did not produce overall multivariate effects, but several individual
variables did show the effects of deception condition. Deceivers had significantly fewer long sentences,
AWS and sentence complexity than truth tellers; and a lower Flesch-Kincaid grade level than truth tellers.
This meant their language was less complex and easier to comprehend. The t-tests also provided weak
support for deceivers having fewer ASW (p = .102) and conjunctions (p = .149) in messages than truth
tellers. Thus, deceivers used less complex language at both the lexical (vocabulary) and grammatical
(sentence and phrase) levels. Modality effects also showed that subjects in text chat used fewer
conjunctions than in audio chat, indicating that that modality was less likely to exhibit compound and
complex sentences.
For the analyses of message specificity and expressiveness (adjectives and adverbs, emotiveness,
and affect), the multivariate test showed a trend toward a main effect for the deception condition (p = .101).
There was a significant univariate difference on affect, such that deceivers used less language referring to
emotions and feelings than did truth tellers.
Table 2. Univariate F-tests (p-values) and t-tests for individual cue analysis
Test of between-subject effects
Cues
Modality Condition Modality*Condition
Independent Samples t-Test
Syllables 2.054(.159) 1.842(.182) .156(.695) 1.502(.140)
Words 2.363(.131) 2.407(.128) .162(.689) 1.702(.096)*
Sentences .810(.373) .001(.972) .018(.894) .111(.912)
Short sentences .122(.016)* .588(.447) .225(.637) -.725(.472)
Long sentences .547(.464) 6.566(.014)* .005(.947) 2.781(.008)*
Simple sentences .029(.886) .002(.969) 1.874(.178) .061(.951)
Big words .462(.500) .288(.594) .146(.704) .616(.541)
Average syllables per word 1.949(.17) 1.703(.199) .413(.524) -1.668(.102)
Average words per sentence .374(.544) 4.368(.042)* .006(.936) 2.414(.021)*
Flesch-Kincaid grade level .001(.979) 2.690(.108) .005(.943) 1.958(.056)*
Sentence complexity .006(.940) 2.055(.159) .181(.673) 1.779(.082)*
Vocabulary complexity .657(.422) .512(.478) .657(.422) -.997(.324)
# of Conjunctions 3.393(.072)* 2.569(.116) 1.496(.228) 1.426(.163)
Rate Adjectives and Adverbs 0.150(.700) .329(.569) .301(.586) -.596(.554)
Emotiveness 0.020(.889) .054(.818) .060(.808) -.233(.817)
Affect 1.591(.214) 3.291(.214) .004(.948) 1.630(.110)
* p < .05, one-tailed.
4.2 Cluster analysis by C4.5
Although many linguistic cues were not significant as shown in section 1, they can form a
hierarchy tree that performs relatively well in discriminating deceptive communicators from truthful ones.
Among many data-mining algorithms, we chose C4.5 because it provides a clear cluster structure
(compared with neural network), as well as satisfactory precision [9]. C4.5 used a pruned tree to cluster the
cues. This algorithm cuts off redundant branches while constraining error rates. We used software of Weka
(University of Waikato in New Zealand; Witten and Frank, 2000) to implement the C4.5.
Figure 1 is the
output of a pruned tree. “1” stands for truthful condition, “2” stands for deception condition.
The correct prediction rate using 15-fold cross-validation is 60.72%, which is reasonably
satisfactory given the small size of the data set.
As shown in the Figure 1, a combination of linguistic cues can well categorize deception
behaviors. For example, sentence level complexity combined with vocabulary or affect acted as good
classifier. Those significant linguistic cues in section 1 also played important roles in the cluster
classification: number of conjunctions, FK grade level, AWS, affect. On the other hand, the cluster
structure also showed consistency with multivariate tests: not all linguistic cues contribute in identifying

deceptions. There were “unhelpful” cues, such as emotiveness, which showed no significance in both the
single level structure and cluster analysis (hierarchy structure). However, it is premature to conclude the
ineffectiveness of any linguistic cues at this point. Further investigations with larger data sets will give us
deeper insight into the intra-relations of cues.
The confusion matrix shows the number of misclassifications: 10 out of 37 true conditions are
misclassified as deceptive, and 19 out of 35 deceptive conditions are misclassified. The tree mentioned
above produced less misclassifications in the truth condition than in the deceptive condition, which implies
a “truth-biased” judgment. In other words, the cluster method is cautious in designating messages as
untrustworthy.
#conjunction <= 10
| #conjunction <= 3
| | #bigwds <= 7
| | | FK_grade <= 8.383333
| | | | #bigwds <= 3
| | | | | Voc_comp <= 12
| | | | | | number_of_sentences <= 2
| | | | | | | Average_words_per_sentence <= 7.625: 1 (2.0)
| | | | | | | Average_words_per_sentence > 7.625: 2 (3.0)
| | | | | | number_of_sentences > 2: 2 (7.0)
| | | | | Voc_comp > 12: 1 (2.0)
| | | | #bigwds > 3: 2 (9.0)
| | | FK_grade > 8.383333: 1 (7.0/1.0)
| | #bigwds > 7: 1 (5.0/1.0)
| #conjunction > 3
| | Average_words_per_sentence <= 31.25
| | | Affect <= 1: 1 (23.0/4.0)
| | | Affect > 1
| | | | Average_words_per_sentence <= 14.166667: 2 (3.0)
| | | | Average_words_per_sentence > 14.166667: 1 (4.0)
| | Average_words_per_sentence > 31.25: 2 (3.0)
#conjunction > 10: 2 (4.0)
=== Confusion Matrix ===
a b <-- classified as
27 10 | a = truthful
19 16 | b = deceptive
Fig. 1. Pruned tree output from C4.5
5 Discussion
This investigation was undertaken largely to demonstrate the efficacy of utilizing linguistic cues,
especially ones that can be automated, to flag potentially deceptive discourse, and to use statistical
clustering techniques to select the best set of cues to reliably distinguish truthful from deceptive
communication. This investigation demonstrates the potential of both the general focus on language
indicators and the use of hierarchical clustering techniques to improve the ability to predict what texts
might be deceptive.
As for the specific indicators that might prove promising, these results provide some evidence for
the hypothesis that deceivers behave differently than truth tellers in communications via text chat and/or
audio chat. Although many tests were not significant due to the small sample size, there was a trend shown
in the profile plots demonstrating that: deceivers’ messages were briefer (i.e., lower on quantity of
language), were less complex in their choice of vocabulary and sentence structure, and lack specificity or
expressiveness in their text-based chats. This is consistent with profiles found in nonverbal deception
research showing deceivers tend to adopt, at least initially, a fairly inexpressive, rigid communication style
with “flat” affect. It appears that their linguistic behavior follows suit and also demonstrates their inability
to create messages rich with the details and complexities that characterize truthful discourse. Over time,
deceivers may alter these patterns, more closely approximating normal speech in many respects. But it is

possible that language choice and complexity may fail to show changes because deceivers are not accessing
real memories and real details, and thus will not have the same resources in memory upon which to draw.
Unlike asynchronous experiments such as the Desert Survival experiment (DSP), subjects did not
have sufficient time to provide detailed lies that contained more quantity and complexities [10]. The
differences in synchronicity in these two tasks points to time for planning, rehearsal, and editing as a major
factor that may alter the linguistic patterns of deceivers and truth tellers. As a consequence, no single
profile of deception language across tasks is likely to emerge. Rather, it is likely that different cue models
will be required for different tasks. Consistent with interpersonal deception theory [11], deceivers may
adapt their language style deliberately according to the task at hand and their interpersonal goals. If the
situation does not afford adequate time for more elaborate deceits, one should expect deceivers to say less.
But if time permits elaboration, and/or the situation is one in which persuasive efforts may prove beneficial,
deceivers may actually produce longer messages. What may not change, however, is their ability to draw
upon more complex representations of reality because they are not accessing reality. In this respect,
complexity measures may prove less variant across tasks and other contextual features. The issue of context
invariance thus becomes an extremely important one to investigate as this line of work proceeds.
Modality also plays a role in communication. Subjects talked more than they wrote, but message
complexity did not seem to be much different between the text and audio modalities. Future research will
explore the effect of different communication modalities on the characteristics of truthful and deceptive
messages.
Although clustering analysis did not consider modality effects, it provided a hierarchy tree
structure to capture the combined characteristics of the cues. It also provided an exploratory threshold
value to separate deceptive and true messages. It should also be noted that the analysis in this study used
the absolute values of linguistic characteristics to classify statements as truthful or deceptive. Because
people vary greatly in their usage of the language (e.g. some people naturally use more or less complex
language than others), the use of cue values that are relative to the sender of the message may result in
greater classification accuracy. This would require building a model of the baseline speech patterns of an
individual and then comparing an individual message to this model.
Future research will consider intra-connections among linguistic cues, tasks, and modalities. More
data will also enhance reliability of current results, but it is clear from these results alone that linguistic
cues that are amenable to automation may prove valuable in the arsenal of tools to detect deceit.
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Appendix: Individual cue comparisons by modality and deception condition*
Estimated Marginal Means of total number of syllables
Modality
21
Estimated Marginal Means
200
180
160
140
120
100
GUILT
guilty
innocent
Estimated Marginal Means of total number of words
Modality
21
Estimated Marginal Means
150
140
130
120
110
100
90
80
GUILT
guilty
innocent
Estimated Marginal Means of number of sentences
Modality
21
Estimated Marginal Means
7.2
7.0
6.8
6.6
6.4
6.2
6.0
5.8
GUILT
guilty
innocent
Estimated Marginal Means of short sentences
Modality
21
Estimated Marginal Means
3.2
3.0
2.8
2.6
2.4
2.2
2.0
GUILT
guilty
innocent

Estimated Marginal Means of long sentences
Modality
21
Estimated Marginal Means
1.2
1.0
.8
.6
.4
.2
0.0
GUILT
guilty
innocent
Estimated Marginal Means of simple sentences
GUILT
innocentguilty
Estimated Marginal Means
2.2
2.0
1.8
1.6
1.4
1.2
Modality
1
2
Estimated Marginal Means of big words
GUILT
innocentguilty
Estimated Marginal Means
8.5
8.0
7.5
7.0
6.5
6.0
Modality
1
2
Estimated Marginal Means of average syllables per word
GUILT
innocentguilty
Estimated Marginal Means
1.44
1.42
1.40
1.38
1.36
1.34
1.32
Modality
1
2
Estimated Marginal Means of average words per sentence
GUILT
innocentguilty
Estimated Marginal Means
24
22
20
18
16
14
Modality
1
2
Estimated Marginal Means of Flesch-Kincaid grade level
GUILT
innocentguilty
Estimated Marginal Means
9.5
9.0
8.5
8.0
7.5
7.0
Modality
1
2
Estimated Marginal Means of sentence complexity
GUILT
innocentguilty
54
52
50
48
46
44
42
40
Modality
1
2
Estimated Marginal Means of vocabulary complexity
GUILT
innocentguilty
Estimated Marginal Means
10.5
10.0
9.5
9.0
8.5
8.0
7.5
7.0
6.5
Modality
1
2

Estimated Marginal Means of #conjunction
GUILT
innocentguilty
Estimated Marginal Means
10
9
8
7
6
5
4
Modality
1
2
Estimated Marginal Means of RateAdAdj
GUILT
innocentguilty
Estimated Marginal Means
.13
.12
.11
.10
Modality
1
2
Estimated Marginal Means of Emotive
GUILT
innocentguilty
Estimated Marginal Means
.30
.29
.28
.27
Modality
1
2
Estimated Marginal Means of Affect
GUILT
innocentguilty
Estimated Marginal Means
.6
.5
.4
.3
.2
.1
0.0
Modality
1
2
* Modality 1 = Text, Modality 2 = Audio