Are Computers Effective Lie Detectors? A Meta-Analysis of Linguistic Cues to Deception

Predictions

From a cognitive load/working memory perspective, we predict that compared with true accounts, false accounts will be (a) shorter as indicated by word and sentence quantity cues; (b) less precisely elaborated as indicated by fewer content words (expressing lexical meaning), a lower type-token ratio (number of distinct content words, for example, house, walk, mother) divided by total number of words), and shorter words (i.e., less than six letters; average word length); (c) involve less complex stories as indicated by fewer verbs, fewer causation words (because, effect, hence) and fewer exclusive words (but, except, without), and (d) include more writing errors (possibly moderated by mode of production [orally telling a lie, handwriting, or typing]). (For a list of the operational definition of all cues included, see Appendices A and B.)

From a different perspective, based on DePaulo’s self-presentational perspective (DePaulo et al., 2003), one would expect that liars are less likely than truth-tellers to take their credibility for granted and therefore may take a greater effort and deliberately edit their communication (cf. Derrick et al., 2012). Note, however, that this editing process will also usurp cognitive resources detracting from successful lie constructions.

Predictions

From these perspectives, it is expected that liars will be less certain and definite than truth-tellers. Consequently, deceptive accounts should contain fewer certainty words (always, clear, never) and more tentative words (guess, maybe, perhaps, seem) and modal verbs (can, shall, should) than truthful accounts. (It should be noted that modal verbs also include the verb must that expresses more certainty and purposiveness whereas all other modal verbs indicate more uncertainty.)

It may be argued that liars are aware that uncertainty indicates deception and thus may strategically incorporate certainty indicators to evade detection (e.g., Bender, 1987). However, research does not support this contention. To our knowledge, around ten reports have been published so far on liars’ and truth-tellers’ strategies to be convincing (for a brief review, see Masip & Herrero, 2013). Only rarely has certainty (or any related construct) emerged as a strategy, and in these instances, it has been mentioned (a) only infrequently and (b) equally often by liars and truth-tellers (e.g., Hines et al., 2010: “admit uncertainty”; for an exception, see Strömwall et al., 2006).

Emotional approach

¹ When people lie, they may experience feelings of guilt and fear of getting caught (Ekman, 1988, 2001). ² Even when telling everyday lies of little consequence, people report feeling uncomfortable (DePaulo et al., 2003). Vrij (2008a) also noted that liars might make negative comments or use negative words that reflect negative affect induced by guilt and fear.

Numerous studies have shown that arousal is associated with specific emotions (see the meta-analysis by Lench, Flores, & Bench, 2011), some of which are likely to be experienced by liars, such as guilt and fear of punishment (Ekman, 2001; Zuckerman, DePaulo, & Rosenthal, 1981). These emotional states may elicit specific nonverbal and verbal cues to deception (see DePaulo et al., 2003; Sporer & Schwandt, 2006; Vrij, 2008a). Recent studies have used brain-imaging technology to specifically investigate the role of emotion in deception (for a review, see Abe, 2011). For example, Abe, Suzuki, Mori, Itoh, and Fujii (2007) found that neural structures associated with heightened emotions were also uniquely associated with deceiving an interrogator and that self-reported feelings of immorality (sense of sin) and anxiety were higher in deceptive conditions than in truth-telling conditions. These results support the notion that deception is associated with negative emotions.

Predictions

From an emotional approach perspective, we predict that compared with true accounts, lies will include (a) more negation words (no, never, not) because these reveal a more defensive tone or denial of wrongdoing, which is likely to be accompanied by negative emotions of the liar, and (b) more words denoting overall negative emotions (enemy, worthless, skeptic), anger (hate, kill, weapon), anxiety (unsure, vulnerable), and sadness (tears, useless, unhappy).

Predictions

Because truth-tellers have a specific memory of the event, whereas liars cannot draw on such an episodic memory, we predict that compared with true accounts, lies will contain fewer words denoting positive emotions (happy, pretty, good) or positive feelings (luck, joy).

Immediacy

A possible way to express ownership and take responsibility for an action or event is to tell a story from a first-person perspective, where the sender is reporting an event where he or she is the actor, not an observer-bystander. Evidence for this assumption comes from the long tradition of research on verbal and nonverbal communication that has investigated immediacy as a cue to truthful messages (Cody et al., 1984; Knapp et al., 1974; Kuiken, 1981; Mehrabian, 1972; Wagner & Pease, 1976; Wiener & Mehrabian, 1968; Zhou, Burgoon, Nunamaker, & Twitchell, 2004; Zhou, Burgoon, Twitchell, et al., 2004). In these studies, one aspect of immediacy has been operationalized as the psychological distance between the speaker and his or her communication. More specifically, immediacy can indicate the degree to which there is directness and intensity between the communicator and the event being communicated (Wiener & Mehrabian, 1968, p. 4). Taking this aspect of the definition of immediacy, deception researchers consider nonimmediacy as an indicator of deceptive communication by way of the speaker distancing from his or her own statement (e.g., Buller et al., 1996; Kuiken, 1981; Wagner & Pease, 1976; Zhou et al., 2004).

However, evidence for nonverbal and verbal indicators of the relationship between immediacy and deception is mixed. In the meta-analysis by DePaulo et al. (2003), there were no significant effects for self- or other-references, but more general indices of verbal immediacy (all categories) as well as verbal and vocal immediacy (impressions) were observed significantly more frequently or to a higher extent in truthful than fabricated messages. This latter effect appeared to be stronger when immediacy was measured subjectively than when assessed via more objective measures.

The self as an organizational structure

Another line of research we consider is social-psychological theorizing on social memory, which has emphasized the role of the self as an organizational structure. In fact, one of the primary distinctions between episodic and autobiographical memory is that the self provides an organizing principle that relates experiences to one’s self-schema. Experimental evidence comes from research on the self-reference effect (Rogers, Kuiper, & Kirker, 1977), which demonstrated that information is particularly well remembered when it has been encoded in relation to oneself, or when the person plays an active, rather than passive role (e.g., Slamecka & Graf, 1978). Variations on this theme are discussed under ego-defensive, self-serving, egocentric, or egotistic biases (see Greenwald, 1980). Greenwald (1980) went as far as referring to the self as a “totalitarian ego” that puts itself in the foreground, assuming a central role and ownership when talking about self-experienced past events and actions. This prevailing tendency should lead to more frequent uses of first-person pronouns (I, me, we, us, our, etc.) when telling the truth relative to lying.

However, while the egocentric bias may play a role when reporting (complex) autobiographical events, it may be restricted to positive outcomes and reversed for negative outcomes (Greenwald, 1980). Also, the so-called “better than average effect” refers to the tendency to evaluate oneself more favorably than an average peer (e.g., J. D. Brown, 2012). For instance, 70% of high school seniors estimated that they had above average leadership skills, whereas only 2% said their leadership skills were below average (College Board, 1976-1977). Another example of the positive outcome bias is a classic study by Bahrick, Hall, and Berger (1996; see also Bahrick, 1996) who found that students accurately recalled better high school grades than worse ones. Relatedly, in a classic study on the self-enhancing bias by Cialdini et al. (1976, Experiment 2), college students not only donned their school colors on Monday after their team had won but also identified, or distanced, themselves by use of different personal pronouns (“we won”; “they lost”). This suggests that first-person pronouns and statements of personal responsibility will be more prevalent among truth-tellers than liars, particularly for positive outcomes.

Predictions

In summary, from different theoretical perspectives, we assume more frequent use of first-person pronouns, and less frequent use of third-person pronouns for reports of self-experienced events. Self-experienced events should also be characterized by more statements of own responsibility, at least for positive outcomes. This prediction is more likely to hold for first-person singular than first-person plural because the plural may designate both the group the storyteller belongs to, and identifies with, as well as a communication partner who acts as an antagonist in an interaction (e.g., “we quarreled”). Thus, with plural pronouns, ownership and responsibility are less clear-cut than with singular pronouns. On the contrary, passive voice or generalizing terms in phrases like “one has to . . .” or “everybody does this . . .” signal less personal involvement and hence should be found more frequently in lies than truthful accounts.

Narrative conventions and verb tense shifts

Communication about past events follow narrative conventions (acquired during childhood) that require the storyteller to talk about who, what, when, where, and why (R. Brown & Kulik, 1977; Neisser, 1982) and to adhere to a temporal structure (Bruner, 1990). Anecdotal evidence from research on autobiographical memory for significant life events shows that people sometimes switch from telling a story in the past tense to the present tense at crucial moments of the event (Pillemer, Desrochers, & Ebanks, 1998). In many of these examples, it appears that the protagonist is reliving the past event, describing his or her sensory and perceptual experiences, making the accounts to appear more vivid (cf. the RM approach described in Research Question 5). Although present tense may be less concrete than past tense when it refers to repeated or routine actions (e.g., “I [usually] go to church on Sunday” versus “I went to church on Sunday”), when talking about a specific past event, present tense is more vivid than past tense. Whether verb tense shifts occur involuntarily or unconsciously, or are strategically used by skillful storytellers (like fiction writers) to communicate intensity and feeling to a recipient, cannot be answered by these archival type studies, nor by our meta-analyses.

Predictions

We expect reports of true events to be more likely to contain present tense verbs than lies, at least in accounts of personally significant events. For other types of lies, this prediction may not hold. The live character of these narratives may also diminish with repeated retellings of a story. Conversely, lies should contain more past tense verbs than true accounts.

RM framework applied to deception

The RM model by M. K. Johnson and Raye (1981) describes how individuals differentiate between externally generated memories of actual experiences versus memories of internally generated events that involve thoughts, fantasies, or dreams. In contrast to imagined events, experienced events are encoded and embedded in memory within an elaborate network of information that typically includes more perceptual details, and contextual and semantic information. Conversely, internally generated memories are characterized by cognitive inferences or reasoning processes.

People differentiate between their own external and internal memories on the basis of these phenomenal characteristics (M. K. Johnson, Hashtroudi, & Lindsay, 1993), and similar features are also useful to differentiate between accounts of external and internal memories of other people (an attribution process that has been tagged “interpersonal reality monitoring”; M. K. Johnson, Bush, & Mitchell, 1998; M. K. Johnson & Suengas, 1989; Sporer, 2004; Sporer & Sharman, 2006).

Deceptive accounts can be characterized as representing internally generated memories, because in a deceptive situation, people imagine the event at the time of its construction (Sporer, 2004). Even if people lie by borrowing from actual experience, the time and place or the context in which the event occurred may be changed during construction (Sporer, 2004; Vrij, 2008a). Therefore, even partially true deceptive accounts may lack the typical characteristics of true accounts. With these considerations in mind, researchers have extrapolated from the RM model to make predictions about specific sets of criteria that may discriminate between true and deceptive accounts (e.g., Granhag, Strömwall, & Olsson, 2001; Sporer, 1997; for reviews, see Masip, Sporer, Garrido, & Herrero, 2005; Sporer, 2004; Vrij, 2008a). DePaulo et al.’s (2003) meta-analysis, which only included a few studies available then, showed small and nonsignificant effect sizes for RM criteria. However, in a more comprehensive review of studies, Masip et al. (2005) found that some of the RM criteria involving perceptual processes, contextual (including time) information, and realism/plausibility of the story were useful to discriminate between truth and deception.

Predictions

From a RM perspective, we predict that compared with true accounts, false accounts will (a) contain fewer perceptual details as indicated by sensory and perceptual word cues (taste, touch, smell), (b) be less contextually embedded as indicated by space (around, under) and time word cues (hour, year), and (c) include fewer descriptive words as indicated by prepositions (on, to), numbers (first, three), quantifiers (all, bit, few), modifiers (adverbs and adjectives), and motion verbs (walk, run, go). This latter set of cues involves words that describe events and actions in the story in more specific terms (e.g., “I took every short cut to get to work”). The lack of these words (e.g., “I went to work”) would make the account seem less real or vivid as would be predicted from the RM perspective (Sporer, 1997, 2004).

Predictions

Consequently, we predict that linguistic cues referring to cognitive operations including memory processes are more likely to be found in truths than in lies. The two cues under this research question are cognitive processes (cause, ought) and insight words (think, know, consider).

Dependencies of effect sizes

In some studies, in addition to accounts’ truth status, other independent variables were manipulated as between- or within-participants factors and the data were reported separately for these subgroups (e.g., Schelleman-Offermans & Merckelbach, 2010: high- vs. low-fantasy-proneness). In studies with additional within-participants factors, dependency was avoided by calculating effect sizes separately for each subgroup and averaging them to ensure that only one effect size per study per linguistic cue was included (Lipsey & Wilson, 2001). In two other studies, a second between-participants factor (Ali & Levine, 2008: denials or confessions; Qin et al., 2005: text chat, audio, face-to-face) was examined; here, we included each of these subgroups (with different stimulus persons) as independent data sets.

Superordinate categories and sub-cues

Sometimes a linguistic category of cues had differentiated effect sizes that seemed to represent a single construct. As an example, we defined cue 19 with the superordinate category (“umbrella term”) positive emotions and feelings including results from positive emotions only and positive feelings only. In studies using LIWC 2001, positive feelings and positive emotions/affects are treated as two different linguistic cues—and the data are reported separately for each (in LIWC 2007, they are combined). To ensure that only one effect size per construct is included, we combined sub-cues to a superordinate category (by averaging their effect sizes). However, we also calculated separate meta-analyses for each of these sub-cues (here: cue 19.1 positive emotions only and 19.2 positive feelings only) to investigate whether the results are more differentiated, or if merging these cues was justifiable. The same procedure was applied to cue 18 negative emotions and to cue 28 sensory–perceptual processes (see Table 1). These superordinate categories make results from LIWC more comparable with studies using other computer programs that did not differentiate between different sub-cues (e.g., anger, anxiety, sadness).

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Table 1.

Meta-Analyses of Linguistic Cues under Research Questions 1 to 6.

Linguistic cue	Pred. DOE	k	n	Minimum gu	Maximum gu	gu	CI	Q	I 2
Research Question 1: Do liars experience greater cognitive load?
(a) Length of accounts
01 Word quantityM	T	42	6,713	−1.25	1.43	0.24	[0.19, 0.29]	315.85	87.02
07 Sentence quantity	T	9	1,334	−1.31	0.28	−0.33	[−0.44, −0.21]	104.01	93.31
08 Average sentence lengthwO,M	T	15/16	2,704	−0.37	0.43	0.05	[−0.03, 0.13]	20.46	31.58
(b) Elaboration of accounts
02 Content word diversitywO	T	7/9	1,076	0.27	1.00	0.48	[0.34, 0.61]	8.13	26.22
03 Type-token ratioM	T	22	3,589	−1.40	1.09	0.14	[0.07, 0.21]	171.95	87.79
04 Six-letter words	T	10	1,617	−0.28	0.25	−0.05	[0.14, −0.05]	5.19	0.00
05 Average word lengthwO	T	7/8	954	−0.42	0.69	0.11	[−0.03, 0.25]	6.85	12.41
(c) Complexity of accounts
06 Verb quantity	T	12	2,356	−1.21	0.44	−0.03	[0.11, −0.60]	78.91	86.06
09 Causation	T	17	2,773	−0.68	0.25	−0.07	[−0.14, 0.01]	18.61	14.03
10 Exclusive wordswO,M	T	18/20	2,783	−0.24	0.81	0.24	[0.17, 0.31]	25.87	25.66
(d) Errors in production
11 Writing errorswO	D	8/10	990	−0.65	0.43	−0.01	[−0.15, 0.12]	13.36	47.61
Research Question 2: Are liars less certain than truth-tellers?
12 Tentative wordswO	D	19/20	3,145	−1.27	0.36	0.13	[0.06, 0.20]	20.13	10.56
13 Modal verbs	D	25	3,889	−0.42	0.80	0.00	[−0.07, 0.06]	32.73	26.62
14 Certainty	T	18	2,823	−0.25	0.94	−0.06	[−0.14, 0.01]	25.15	32.40
Research Question 3a: Do liars use more negations and negative emotion words?
17 NegationsM	D	20	3,659	−0.98	0.53	−0.15	[−0.22, −0.09]	155.53	87.78
18 Negative emotionsa,wO,M	D	21/24	2,593	−0.39	0.87	−0.07	[−0.15, 0.01]	21.03	4.88
18.1 Negative emotions onlyM	D	24	3,641	−1.90	0.87	−0.18	[−0.24, −0.12]	214.57	89.28
18.2 Anger	D	12	2,452	−1.32	0.38	−0.27	[−0.35, −0.19]	165.03	93.34
18.3 AnxietywO	D	11/12	1,952	−0.30	0.44	0.07	[−0.02, 0.02]	13.55	26.19
18.4 Sadness	D	12	2,452	−0.34	0.25	0.04	[−0.04, 0.12]	13.01	15.46
Research Question 3b: Do liars use fewer positive emotion words?
19 Positive emotions and feelingsa,wO	T	20/21	2,703	−0.84	0.37	−0.05	[−0.12, 0.03]	29.59	35.79
19.1 Positive emotions onlywO,M	T	20/21	2,703	−0.84	0.35	−0.07	[−0.15, 0.00]	27.98	32.08
19.2 Positive feelings only	T	9	1,422	−0.47	0.40	0.07	[−0.03, 0.18]	14.88	46.25
Research Question 3c: Do liars express more or less unspecified emotion words?
15 EmotionswO,M	?	21/25	2,941	−0.63	0.48	−0.11	[−0.19, −0.04]	28.92	30.85
16 Pleasantness and unpleasantness	?	6	806	−0.35	0.30	−0.10	[−0.25, 0.06]	8.43	40.68
Research Question 4: Do liars distance themselves more from events?
(a) Personal pronouns
21 First-person singularM	T	22	3,761	−1.00	0.61	−0.06	[−0.13, 0.00]	274.38	92.35
22 First-person pluralwO,M	T	22/25	3,224	−0.72	0.39	0.06	[−0.01, 0.13]	27.98	24.93
23 Total first-personwO,M	T	22/23	2,541	−0.39	0.57	0.14	[0.06, 0.22]	32.26	34.91
24 Total second-personwO,M	D	21/23	3,072	−0.61	0.33	−0.10	[−0.17, −0.02]	28.64	30.16
25 Total third-personwO,M	D	26/29	3,848	−0.41	0.55	−0.10	[−0.16, −0.04]	37.20	32.79
20 Total pronounswO,M	—	18/19	2,460	−0.36	0.65	0.06	[−0.02, 0.14]	19.32	12.02
(b) Passive voice and generalizing terms
26 Passive voice verbs	D	11	1,221	−0.47	0.49	0.06	[−0.06, 0.18]	9.52	0.00
27 Generalizing terms	D	4	93	−1.63	0.44	−0.37	[−0.79, 0.05]	15.78	80.99
(c) Past and present tense
47 Past tense	D	16	3,047	−0.53	0.41	0.06	[−0.01, 0.14]	22.67	33.83
48 Present tensewO,M	T	16/17	2,607	−0.51	0.60	0.01	[−0.07, 0.09]	19.38	22.61
Research Question 5: Do liars use fewer (sensory and contextual) details?
(a) Sensory and perceptual details
28 Sensory–perceptual processesa,wO,M	T	25/27	3,957	−0.70	0.70	0.05	[−0.01, 0.12]	36.00	33.33
28.1 Sensory–perceptual processes onlyM	T	27	4,177	−0.90	0.70	0.06	[0.00, 0.13]	89.26	70.87
28.2 SeeingwO	T	9/11	1,740	−0.17	0.34	0.03	[−0.06, 0.13]	13.34	40.03
28.3 FeelingwO	T	11/12	2,304	−0.49	0.27	−0.03	[−0.11, 0.05]	15.00	33.31
28.4 Hearing	T	11	2,344	−0.41	0.48	0.17	[0.09, 0.25]	14.15	29.35
(b) Contextual embedding
29 TimewO	T	23/24	3,296	−1.25	0.53	0.03	[−0.04, 0.10]	28.95	24.00
30 SpacewO,M	T	22/24	3,199	−0.36	0.58	−0.04	[−0.13, 0.03]	31.74	33.74
31 Space and Time	T	5	634	−0.25	0.61	−0.04	[−0.19, 0.12]	10.48	61.84
(c) Descriptive words
32 Prepositions	T	14	2,479	−0.55	0.48	0.02	[−0.06, 0.10]	16.54	21.38
33 Numbers	T	12	2,452	−0.28	0.23	0.05	[−0.03, 0.13]	9.37	0.00
34 Quantifier	T	4	1,198	0.06	0.22	0.14	[0.02, 0.25]	1.22	0.00
35 Modifier	T	11	1,361	−1.04	0.43	−0.08	[−0.20, 0.03]	77.46	87.09
36 Motion verbswO,M	T	16/17	2,359	−0.72	0.13	−0.09	[−0.17, −0.01]	16.84	10.92
Research Question 6: Do liars refer less often to cognitive processes?
37 Cognitive processeswO	T	18/19	2,915	−0.25	0.36	0.09	[0.01, 0.16]	19.66	13.54
38 InsightM	T	15	2,539	−0.41	0.59	0.13	[0.05, 0.21]	35.65	60.73

Note. Pred. DOE = predicted direction of effect; k = number of hypothesis tests, where the second value behind the slash indicates the number of hypothesis tests with outliers; N = total number of accounts; g_u = effect size Hedges’s g_u, positive g_us indicate higher frequencies in true accounts, negative g_us indicate higher frequencies in deceptive accounts; CI = 95% confidence interval; Q = homogeneity test statistic; I² = descriptive measure of heterogeneity; ^M = Moderator analyses conducted; T = occurs more often in true accounts; ^wO = without outlier: Results after removal of outliers detected using Hedges and Olkin’s (1985) procedure; D = occurs more often in deceptive accounts; values in bold indicate significance (p < .05).

a

Indicates that the specific linguistic cue is an umbrella term.

Weighted average effect size

For each of the 79 linguistic cues, individual meta-analyses under the fixed-effects model (Lipsey & Wilson, 2001; Shadish & Haddock, 2009; Sporer & Cohn, 2011) were calculated. Average effect sizes were weighted by the inverse of the variance to give more weight to studies with larger samples. For six studies, the total number of accounts was extremely large. To avoid unjustified extraordinary large weights, we adjusted the number of total accounts for these studies (see “Results” section).

Homogeneity of effect sizes

We report both the homogeneity test statistic Q (Lipsey & Wilson, 2001) and the descriptive homogeneity statistic I² (Higgins & Thompson, 2002; Shadish & Haddock, 2009). In rare cases where I² resulted in a negative value, it was set to 0. In case of heterogeneity, outlier and moderator analyses were conducted.

Outlier analysis

To test for the presence of outliers, we applied the two methods recommended by Hedges and Olkin (1985, chap. 12, and programmed by the fourth author). The number of outliers did not exceed 15% of the total number of effect sizes to avoid an artificial restriction of the variance between effect sizes. If outliers were detected, we calculated each meta-analysis with and without the outliers as sensitivity analyses (Greenhouse & Iyengar, 2009). Due to space limitations, we only report results without outliers in Table 1 (results with and without outliers are displayed in Appendix H in supplemental online materials).

Moderator analyses

We used categorical variables as potential moderators with Hedges’s analogue to ANOVA (Hedges, 1982; Lipsey & Wilson, 2001). Moderator analyses were only conducted if the homogeneity statistic was significant and if an individual meta-analysis of a specific linguistic cue contained enough hypothesis tests to avoid empty cell sizes and to increase power. Moderator analyses were only conducted without outliers to prevent biased results. To clarify potential confounds between moderator variables, we calculated their intercorrelations as well as all two-way and three-way cross-tabulations for each variable combination, to avoid empty or low frequency cells. As a consequence, only moderator analyses for k ≥ 13 hypothesis tests are reported.

Computer software for calculations

For computing individual effect sizes, weights, and confidence intervals (CIs), formulae were programmed in Microsoft Office Excel (2011) spreadsheets by the first and fourth author. Calculations of meta-analyses and outlier analyses were conducted using Excel spreadsheets programmed by the fourth author and cross-validated using Lipsey and Wilson’s (2001) SPSS macros (Wilson, 2002). Moderator analyses were also conducted using these macros.

Computer program

More than half of the studies (58.1%) used LIWC (2001 or 2007), 23.3% used other general programs, and 18.6% applied a program specifically developed to detect deception. Three studies, where the type of program was not specifically described or labeled (e.g., “automated analysis method,” “natural language processing tool,” “message analyzing software”), were categorized under other general programs.

Senders

There were a total of 3,780 senders (k = 43) with an average of 87.91 (SD = 19.60, Mdn = 53) senders per study, ranging from 8 to 800. Information about senders’ gender was provided in 30 studies, with more male than female participants in total (N_male = 1,254, N_female = 895), and on average per study (M_male = 41.80, SD_male = 9.22; M_female = 29.83, SD_female = 5.76). Exact information about senders’ age was reported in only 29.5% of the studies. Across all age groups, senders’ mean age was 19.33 years (SD = 8.45), ranging from 4 to 58 years. The mean age of N = 1,015 adults only was 24.17 (SD = 4.11) with a range of 17 to 58 years, whereas the mean age of N = 218 children (k = 4) was 8.45 years (SD = 1.57), ranging from 4 to 14 years.

Accounts

There were a total of 11,680 (N_truth = 5,650, N_lie = 6,030) accounts originally. However, six studies contained an extremely large number of accounts, ranging from N = 608 (Schafer, 2007, Experiment 1) to N = 3,162 (Derrick et al., 2012), with a mean of 1295.17 accounts (SD = 948.98). In the other 38 studies, the mean was M = 102.87 (SD = 73.17), ranging from N = 13 (Ali & Levine, 2008, confessions) to N = 322 (J. E. Cooper, 2008). Therefore, we decided to adjust the number of total accounts for these six studies to N = 500 (n_truth = 250, n_lie = 250) to avoid extraordinary high weights. Consequently, the final average number of accounts per study was M = 157.02 (SD = 153.66, Mdn = 103), with M = 82.02 (SD = 80.71) for truths and M = 75.00 (SD = 76.68) for lies. All accounts were provided in English except for four studies (two Spanish, one Dutch, one Arabic).

Preparation

Only eight studies provided information about how long senders had time to prepare their accounts. In four of these, senders had no opportunity, for the other four studies, senders had on average 1.31 min (SD = 0.71; range = 1-5 min) to prepare.

Theoretical background

Twelve studies referred to Newman et al.’s (2003) explanations (“LIWC approach”) to predict the outcome of specific linguistic cues, 3 used interpersonal deception theory (IDT; Buller & Burgoon, 1996), 2 RM (Sporer, 2004), and 12 a combination of IDT and RM. Twelve additional studies referred to other theoretical backgrounds, for example, media richness theory (Daft & Lengel, 1986) or verbal immediacy (Mehrabian & Wiener, 1966), and three studies did not mention any theory at all. A priori selections of linguistic cues were made for 37 studies while 7 reported only significant findings.

(a) Are liars’ accounts shorter in terms of number of words (cue 01), number of sentences (cue 07), and average sentence length (cue 08)?

As expected, liars used fewer words than truth-tellers (word quantity, 0.24 [0.19, 0.29]), with g_us ranging from −1.25 to 1.43, but no shorter sentences than truth-tellers (average sentence length, 0.05 [−0.03, 0.13]). Contrary to our prediction, liars used more sentences than truth-tellers (−0.33 [−0.44, −0.21]), although the distribution of effect sizes was also quite heterogeneous. The effect size for sentence quantity was derived from a small subset of nine studies compared with 42 studies serving data for word quantity. Therefore, word quantity is a more precise estimate for statement length.

Note that DePaulo et al. (2003) did not examine number of words per se but only response length defined as “length or duration” (cue 01, d = −0.03, k = 49, ns), or as talking time (cue 02, d = −0.35, k = 4, p < .05). Sporer and Schwandt (2006) found no reliable associations for number of words (d = −0.018, k = 8) nor for message duration (d = −0.078, k = 23). These differences in findings may be due to the stimulus accounts used. More recent studies analyzing verbal content cues to deception sometimes do (e.g., Ansarra et al., 2011) and sometimes do not find (e.g., Leal, Vrij, Warmelink, Vernham, & Fisher, 2013) differences between liars’ and truth-tellers’ length of accounts operationalized by the number of words.

(b) Are deceptive accounts less elaborated in terms of content word diversity (cue 02), type-token ratio (cue 03), or word length cues (cues 04, 05)?

Indeed, liars used fewer diverse content words (0.48 [0.34, 0.61]) and distinct words (type-token ratio: 0.14 [0.07, 0.21]) than truth-tellers. These findings could be attributed to liars’ increased cognitive load and reduced working memory capacity (relative to truth-tellers), which in turn is associated with a limitation of creative word production in speaking or writing. These findings also favor a cognitive over a social-psychological explanation, as it is unlikely that liars strategically use fewer diverse content and distinct words. However, the prediction that liars would provide shorter words was not supported (see Table 1). Presumably, the number of distinct words and word diversity indices are more sensitive to cognitive load and working memory capacity than word length.

(c) Are deceptive accounts less complex than true accounts, as indicated by fewer verbs (cue 06), causation (cue 09), and exclusive words (cue 10)?

Liars indeed used fewer exclusive words like but, except, or without, than truth-tellers (0.24 [0.17, 0.31]). Using few exclusive words results in simpler stories (Newman et al., 2003). Liars may resort to telling simple stories because their cognitive system is more taxed than that of truth-tellers. Our predictions that liars would use fewer words assigning a cause to his or her behavior (causation), or use fewer verbs than truth-tellers, were not confirmed (Table 1).

(d) Do liars commit more writing errors (cue 11) than truth-tellers?

No support was found for this hypothesis with or without two outliers (Lee, Welker, & Odom, 2009; Zhou & Zhang, 2004). This can be reconciled with DePaulo’s self-presentational perspective (DePaulo, 1992; DePaulo et al., 2003). Liars might be more self-aware and deliberate than truth-tellers; hence, they may edit their typing errors. Derrick et al. (2012) showed that liars were significantly more likely to edit their words on the keyboard (e.g., in using the backspace and delete button) than truth-tellers (−0.12 [−0.19, −0.05]). Whether their edits were aimed at correcting explicit typing errors was not investigated and should be examined more closely. In 6 of the 10 studies exploring writing errors, participants typed their stories on a computer keyboard; unfortunately, they did not measure editing behavior (with the exception of Derrick et al., 2012).

(a) To the extent that liars defend themselves or deny something they have done, do they use more negation terms such as no, never, or not (cue 17)?

This prediction was supported, with a significant negative effect of −0.15 [−0.22, −0.09] based on 20 studies (but large heterogeneity). Our results contradict Hancock, Curry, Goorha, and Woodworth’s (2008) view, who considered negations as a form of distinction marker (in addition to exclusive terms) expected to occur less frequently in deceptive accounts, presumably to avoid contradictions by being less specific than truth-tellers.

Our findings concur with those of DePaulo et al. (2003), who found a significant effect for negative statements and complaints (cue 52, d = 0.21, k = 9, p < .05) showing that liars use slightly more negative utterances than truth-tellers.

(b) Do liars use more negative emotion words in general (cues 18, 18.1), as well as more specific negative emotion words, such as anger (cue 18.2), anxiety (cue 18.3), or sadness (cue 18.4), than truth-tellers?

Contrary to the prediction that people might feel negative emotions while lying (Ekman, 2001; Zuckerman et al., 1981), liars did not use more negative emotion words (cue 18, −0.07 [−0.15, 0.01]). However, the sub-cue negative emotions only revealed a small but reliable negative effect (−0.18 [−0.24, −0.12]). ⁶ The difference between these results can be explained with their different operationalization. Whereas the superordinate category negative emotions (cue 18) contained all types of negative emotions (including anger, anxiety, and sadness), cue 18.1 encompassed only a reduced set of negative emotion words (e.g., hate, worthless, enemy).

A more differentiated picture of various negative emotions under investigation emerged when we look at the more specific type of emotion words used. Liars used more anger terms than truth-tellers (cue 18.2, −0.27 [−0.35, −0.19]), although no significant differences were found for anxiety (cue 18.3) or sadness (cue 18.4, see Table 1). Newman et al.’s (2003) assertion that “anxiety words are more predictive than overall negative emotion” (p. 672) was not supported. Rather, the present findings indicate that there are differences in words expressing feelings and/or different negative emotions while lying. Liars might not feel anxious or sad but rather feel angry, and this might be manifested in words like worthless or annoyed.

(a) Do liars use fewer first-person pronouns (cues 21, 22, 23) and more second-person (cue 24) and third-person pronouns (cue 25) than truth-tellers?

Although no significant differences were found for first-person singular, or first-person plural references (see Table 1), the weighted average effect size for total first-person pronouns was significant in the expected direction; that is, liars used fewer total first-person pronouns than truth-tellers (0.14 [0.06, 0.22], when the extreme negative effect size found by Brunet, 2009, both conditions: −1.63 [−1.98, −1.29] was excluded).

On the other side of the coin, we predicted second- and third-person pronouns to occur more often in liars’ than truth-tellers’ accounts. Our meta-analyses supported this prediction, with a negative g_u = −0.10 (Table 1). The results indicated that liars in general tried to redirect the focus of attention to other people by using more references to their interaction partner(s) (you), or to (a) third person(s) (he, she, they) than truth-tellers.

Overall pronoun use

As researchers seem to be interested in the use of any type of pronouns (total pronouns, cue 20), we aggregated all of the pronoun effect sizes. The resulting effect size was not significant (0.06 [−0.02, 0.14]).

(b) Do deceptive accounts contain more passive voice verbs (cue 26) and generalizing terms (cue 27) than truthful accounts?

Although effect sizes for passive voice verbs varied considerably (see Table 1), all were nonsignificant. This is probably due to small sample sizes or a generally low frequency of occurrence (floor effect). Generalizing terms had a medium negative effect size (−0.37 [−0.79, 0.05]) that was nevertheless not significant because of the small number of studies and large heterogeneity. Similarly, DePaulo et al. (2003) did not find a significant effect for generalizing terms (cue 21, d = 0.10, k = 5, ns).

(c) Do lies include more past tense verbs (cue 47) and fewer present tense verbs (cue 48) than true accounts?

Significant differences were neither found for past tense verbs nor for present tense verbs (Table 1). A potential reason why the data did not support our predictions could be the way the dependent variable was operationalized. It is important to note that Pillemer et al.’s (1998) hypothesis stated that verb tense shifts occur more often in critical parts of experienced (i.e., true) autobiographical events. Here, we did not consider verb tense shifts, but absolute number of present and past tense verbs. Future research could construct a more suitable linguistic cue than counting the number of verbs only.

(a) Do liars use fewer sensory and perceptual details than truth-tellers?

They did, according to our findings for sensory–perceptual processes only (cue 28.1), although the average effect size was very small (0.06 [0.00, 0.13]). For the variable sensory–perceptual processes overall (cue 28), the effect size was not significant (0.05 [−0.01, 0.12], after two outliers were excluded).

Some support came from the more specialized cue hearing (cue 28.4, 0.17 [0.09, 0.25]), showing that liars used fewer words expressing their acoustic impressions (like listen, sound, or speak) than truth-tellers. Indeed, in case of entirely fabricated lies (compared to partially fabricated lies or lies of omission), persons may not experience any audio(visual) impressions at all and do not seem to deliberately include these words in their lies. However, the cues seeing (cue 28.2) and feeling (cue 28.3) yielded nonsignificant results (see Table 1). Although DePaulo et al. (2003) also found no significant effects for sensory information (cue 05: d = −0.17, k = 4, ns) there have been many new RM studies we are currently synthesizing in an updated large scale meta-analysis.

(b) Are liars’ accounts less contextually embedded than those of truth-tellers, as indicated by fewer time and space words?

No significant effects emerged for time (cue 29), space (cue 30), or the combination of spatial and temporal details (cue 31). Our results for temporal and spatial details are in line with DePaulo et al.’s (2003) nonsignificant finding for contextual embedding (cue 76, d = −0.21, k = 6, ns), though it should be noted that contextual embedding goes beyond temporal and spatial details in that the event has to be connected to everyday occurrences, habits, relationships, and so forth (e.g., Steller & Köhnken, 1989). Again, many newer CBCA and RM studies found support for this assumption (see Masip et al., 2005; Vrij, 2005, 2008a) but linguistic analyses by computers do not seem to capture them.

(c) Relative to truth-tellers, do liars use fewer descriptive words, such as prepositions (cue 32), numbers (cue 33), quantifiers (cue 34), modifiers (adverbs and adjectives, cue 35), and motion verbs (cue 36)?

The only significant effect size was obtained for quantifiers (0.14 [0.02, 0.25]) indicating a slightly lower use of words such as all, bit, few, less, among liars. However, this finding was synthesized from four studies only, so we should not make strong conclusions for this cue in general.

Interestingly, liars produced more motion verbs (such as walk, go, or move) than truth-tellers (−0.09 [−0.17, −0.01]) after removing the only significant positive effect size (Liu et al., 2012; 0.38 [0.21, 0.56]), which was found to be an outlier. This finding is contrary to our prediction but is in line with the cognitive load approach (Research Question 1) and Newman et al.’s (2003) assumption that, when constructing a lie, “simple, concrete actions are easier to string together than false evaluations” (p. 667). Therefore, liars, who are cognitively taxed by the act of lying, “should use more motion verbs and fewer exclusive words” (Newman et al., 2003, p. 667).

Event type and personal involvement

We hypothesized that larger effect sizes would be found for Research Questions 1, 2, 3a, 3c, and 4 if the event was personally relevant to the participant (“First-person experience,” k = 21) than in the “Attitude/liking” paradigm (k = 7) or the “Miscellaneous” paradigms (k = 14; see Table 2). First, concerning cognitive load (Research Question 1), event type affected average sentence length only. Liars used shorter sentences than truth-tellers when articulating attitudes (0.17), but not under the other two paradigms. Second, regarding negative emotions and negations (Research Question 3a), liars used more negative emotion words than truth-tellers only if they had to tell a personally relevant story (−0.37, −0.57), and expressed more negations only in miscellaneous paradigms (−0.59). Thus, although liars might experience and express more negative emotions when the topic is personally relevant, they do not necessarily use more negations. Third, as expected, liars also expressed more unspecified emotions (Research Question 3c; −0.45) when talking about a personal experience than when having performed other tasks. Fourth, concerning distancing (Research Question 4), liars used fewer first-person plural pronouns primarily when describing a video (0.38), but fewer total first-person pronouns when talking about attitudes (0.31). This unexpected finding suggests that it may be especially hard for liars to refer to themselves while articulating a false attitude; however, liars may still use self-references while telling a personal event because it is common (in the English language) to refer to oneself as the actor. Also, it would be hard to avoid self-references when telling a story with oneself as the acting person, even when lying.

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Table 2.

Effect Sizes of Linguistic Cues to Deception When Studies Used Different Type of Events and Personal Involvement.

Linguistic cue (RQ)	k	Overall gu [CI]	k 1	Attitude/liking paradigm	k 3	First-person experience	k 2	Miscellaneous paradigms
08 Average sentence lengthwO (RQ1)	15	0.04 [−0.04, 0.12]	2	0.17 [0.05, 0.29]	8	−0.07 [−0.20, 0.06]	5	−0.07 [−0.17, 0.13]
17 Negations (RQ3a)	20	−0.15 [−0.22, −0.08]	7	0.08 [−0.02, 0.18]	7	−0.08 [−0.20, 0.05]	6	−0.59 [−0.71, −0.47]
18 Negative emotions a (RQ3a)	24	−0.13 [−0.19, −0.06]	7	0.03 [−0.06, 0.13]	10	−0.37 [−0.48, −0.25]	7	−0.10 [−0.24, 0.03]
18.1 Negative emotions only (RQ3a)	24	−0.17 [−0.24, −0.11]	7	0.06 [−0.04, 0.15]	10	−0.57 [−0.69, −0.45]	7	−0.11 [−0.25, 0.03]
15 Emotions (RQ3c)	24	−0.21 [−0.27, −0.14]	7	−0.08 [−0.17, 0.02]	12	−0.45 [−0.56, −0.34]	5	−0.02 [−0.19, 0.15]
22 First-person plural (RQ4)	24	0.08 [0.01, 0.14]	7	0.09 [−0.01, 0.19]	12	−0.08 [−0.18, 0.03]	5	0.38 [0.23, 0.53]
23 Total first-personwO (RQ4)	22	0.14 [0.06, 0.22]	6	0.31 [0.19, 0.42]	11	−0.11 [−0.27, 0.05]	5	0.10 [−0.05, 0.26]
24 Total second-person (RQ4)	22	−0.04 [−0.01, 0.03]	7	−0.18 [−0.27, −0.08]	10	0.09 [ − 0.02, 0.20]	5	0.09 [ − 0.09, 0.26]
25 Total third-person (RQ4)	28	− 0.11 [− 0.17, − 0.05]	7	− 0.12 [− 0.21, − 0.02]	13	−0.22 [−0.32, −0.12]	8	0.06 [ − 0.07, 0.18]

Note. RQ = research question; k = number of hypothesis tests; g_u = ES Hedges’s g_u, positive g_us indicate higher frequencies in true accounts, negative g_us indicate higher frequencies in deceptive accounts; ES = effect size; CI = 95% confidence interval; ^wO = without outlier; bold ES indicate significant difference from zero; ES in italics correspond to the largest difference between liars and truth-tellers (in the predicted direction according to the RQ) for a specific cue across the moderator variable levels; this indicates under what level of the moderator variable our hypotheses were most strongly supported.

a

Indicates that the specific linguistic cue is an umbrella term.

Liars used more total second-person pronouns only when talking about attitudes (−0.18), and more total third-person pronouns in all kinds of events except the miscellaneous paradigms. In general, thus, the predicted differences for Distancing (Research Question 4) between liars and truth-tellers appear enhanced in the attitude/liking paradigm—compared with the other two paradigms.

Emotional valence

We predicted effects for cues under Research Questions 1 to 4 to be larger for negative (k = 18) than for neutral events or themes (k = 17; see Table 3). ⁸ Indeed, liars used fewer words (Research Question 1—Cognitive Load; 0.54) only when the event was negative. In terms of negative emotions (Research Question 3a), liars also used considerably more negations (−0.42) and negative emotions (−0.39, −0.65) than truth-tellers, most notably when the event was negative. This supported the notion that telling a negatively toned lie might be accompanied by negative emotions.

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Table 3.

Effect Sizes of Linguistic Cues to Deception When the Emotional Valence of the Event Was Neutral Versus Negative.

Linguistic cue (RQ)	k	Overall gu [CI]	k 1	Neutral	k 2	Negative
01 Word quantity (RQ1)	33	0.25 [0.19, 0.31]	17	0.04 [ − 0.03, 0.12]	16	0.54 [0.45, 0.62]
03 Type-token ratio (RQ1)	20	0.26 [0.18, 0.33]	11	0.32 [0.24, 0.41]	9	0.04 [ − 0.12, 0.19]
10 Exclusive words (RQ1)	14	0.38 [0.29, 0.46]	8	0.47 [0.36, 0.59]	6	0.26 [0.11, 0.38]
17 Negations (RQ3a)	14	−0.30 [−0.38, −0.21]	8	−0.13 [−0.26, 0.01]	6	−0.42 [−0.53, −0.31]
18 Negative emotions (RQ3a)	17	−0.26 [−0.35, −0.18]	10	−0.16 [−0.28, −0.05]	7	−0.39 [−0.52, −0.26]
18.1 Negative emotions onlywO (RQ3a)	17	−0.41 [−0.50, −0.32]	10	−0.22 [−0.34, −0.10]	7	−0.65 [−0.79, −0.52]
15 Emotions (RQ3c)	18	−0.50 [−0.58, −0.42]	8	−0.54 [−0.65, −0.43]	10	−0.45 [−0.57, −0.33]
21 First-person singularwO (RQ4)	15	− 0.04 [ − 0.12, 0.05]	9	−0.25 [−0.36, −0.13]	6	0.27 [0.14, 0.40]
23 Total first-personwO (RQ4)	17	0.10 [0.01, 0.20]	8	0.22 [0.10, 0.34]	9	− 0.13 [ − 0.30, 0.05]
24 Total second-personwO (RQ4)	15	−0.20 [−0.28, −0.12]	9	−0.40 [−0.50, −0.29]	6	0.09 [ − 0.05, 0.22]

Note. RQ = research question; k = number of hypothesis tests; g_u = ES Hedges’s g_u, positive g_us indicate higher frequencies in true accounts, negative g_us indicate higher frequencies in deceptive accounts; ES = effect size; CI = 95% confidence interval; ^wO = without Outlier; bold ES indicate significant difference from zero; ES in italics correspond to the largest difference between liars and truth-tellers (in the predicted direction according to the RQ) for a specific cue across the moderator variable levels; this indicates under what level of the moderator variable our hypotheses were most strongly supported.

However, contrary to our predictions regarding the cognitive load cues (Research Question 1), differences between lies and truths for type-token ratio (0.32) and exclusive words (0.47) were larger when telling a neutral event rather than a negative event. Perhaps truth-tellers reporting a negative event are as emotionally involved as liars. This may imply using less elaborate language (compared with neutral events), which would explain the lack of difference in type-token ratio between liars and truth-tellers. Finally, no difference in the use of unspecified emotions (Research Question 3c) was found between neutral and negative topics: Liars used more emotion words overall for both (−0.54, −0.45).

Regarding distancing (Research Question 4), somewhat contradictory findings occurred for self-references. When telling neutral events, liars used more first-person singular pronouns (−0.25) but fewer total first-person pronouns (0.22) than truth-tellers. Also, when telling negative events, liars used fewer first-person singular pronouns than truth-tellers (0.27) but about the same amount of total first-person pronouns (−0.13). These findings clearly show that (a) differences exist between liars and truth-tellers in terms of referring solely to oneself or to oneself in addition to one’s group, and (b) these differences depend on the valence of the event. If we think about examples of wrongdoing as typical negative events, it perfectly makes sense to distribute responsibility to “we” (or “you and me,” “they and me”) than to take it on one’s own shoulders (“I”). Finally, liars expressed more total second-person pronouns only when the event was neutral (−0.40).

Intensity of interaction

We predicted that the higher the interaction level, the larger the effect sizes would be (Table 4). Indeed, effect sizes for word count (Research Question 1—Cognitive Load; 0.69), negative emotions (Research Question 3a; −0.48, −0.79), unspecified emotions (Research Question 3c; −0.63), and first-person singular pronouns (Research Question 4—Distancing; 0.34) were largest in person to person interactions. Note also that for computer-mediated communication the direction of effect (−0.41) reversed compared with other conditions. Furthermore, in the interview condition (which was considered as the second intense interaction category), effect sizes for word count, exclusive words, and negative emotions only, were in the expected direction. Interestingly, when no interaction took place, liars used significantly more first-person singular pronouns (−0.22) and total third-person pronouns than truth-tellers (−0.31).

Together, this evidence suggests that some verbal differences between liars and truth-tellers manifest themselves most when a bidirectional interaction between two persons—not only a one-way interview—took place.

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Table 4.

Effect Sizes of Linguistic Cues to Deception When Studies Applied Different Type of Interaction Levels (Between Sender and Receiver).

Linguistic cue (RQ)	k	Overall gu [CI]	k 1	No interaction	k 2	Computer-mediated communication	k 3	Interview	k 4	Person-to-person interaction
01 Word quantity (RQ1)	37	0.22 [0.17, 0.28]	13	0.14 [0.07, 0.21]	5	−0.41 [−0.65, −0.18]	16	0.35 [0.23, 0.46]	3	0.69 [0.53, 0.85]
10 Exclusive words (RQ1)	19	0.30 [0.23, 0.36]	9	0.37 [0.29, 0.45]	2	− 0.02 [ − 0.31, 0.26]	6	0.25 [0.04, 0.46]	2	0.17 [0.02, 0.33]
18 Negative emotions a (RQ3a)	22	−0.14 [−0.21, −0.07]	8	0.03 [ − 0.07, 0.12]	3	− 0.18 [ − 0.46, 0.10]	7	− 0.16 [ − 0.34, 0.01]	4	−0.48 [−0.63, −0.34]
18.1 Negative emotions only (RQ3a)	22	−0.20 [−0.27, −0.13]	8	0.05 [−0.05, 0.14]	3	−0.18 [−0.46, 0.10]	7	−0.18 [−0.36, −0.01]	4	−0.79 [−0.94, −0.64]
15 Emotions (RQ3c)	24	−0.34 [−0.41, −0.28]	11	−0.36 [−0.44, −0.28]	0		11	−0.04 [−0.19, 0.11]	2	−0.63 [−0.79, −0.47]
21 First-person singularwO (RQ4)	21	−0.06 [−0.12, 0.01]	9	−0.22 [−0.30, −0.13]	2	−0.04 [−0.33, 0.24]	7	0.01 [−0.16, 0.19]	3	0.34 [0.20, 0.49]
25 Total third-person (RQ4)	27	−0.21 [−0.27, −0.15]	10	−0.31 [−0.40, −0.23]	2	−0.21 [−0.52, 0.09]	12	−0.04 [−0.18, 0.09]	3	−0.09 [−0.23, 0.06]

Note. RQ = research question; k = number of hypothesis tests; g_u = ES Hedges’s g_u, positive g_us indicate higher frequencies in true accounts, negative g_us indicate higher frequencies in deceptive accounts; ES = effect size; CI = 95% confidence interval; ^wO = without outlier; bold ES indicate significant difference from zero; ES in italics correspond to the largest difference between liars and truth-tellers (in the predicted direction according to the RQ) for a specific cue across the moderator variable levels; this indicates under what level of the moderator variable our hypotheses were most strongly supported.

a

Indicates that the specific linguistic cue is an umbrella term.

Motivation

In support for our hypotheses, larger effects occurred for not-motivated liars, who used fewer words (Research Question 1—Cognitive Load) than truth-tellers (0.47), compared with moderately (0.19) or highly motivated liars (0.18; see Table 5). Also, liars used fewer temporal details (Research Question 5 regarding details) only when no motivation was induced (0.20). These findings support the notion that highly motivated liars are more successful than unmotivated liars in controlling their verbal behavior (at least in terms of number of words and temporal details). Note that liars seem to be less able to control their paraverbal behavior under high motivation (e.g., for pitch, response latency; Sporer & Schwandt, 2006) nor their visual nonverbal behavior (e.g., for eye contact; DePaulo et al., 2003).

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Table 5.

Effect Sizes of Linguistic Cues to Deception When Studies Induced Different Levels of Motivation.

Linguistic cue (RQ)	k	Overall gu [CI]	k 1	No motivation	k 2	Low to medium motivation	k 3	High motivation
01 Word quantity (RQ1)	37	0.27 [0.21, 0.32]	11	0.47 [0.36, 0.57]	22	0.19 [0.12, 0.26]	4	0.18 [0.04, 0.31]
03 Type-token ratio (RQ1)	19	0.00 [−0.08, 0.08]	4	−0.17 [−0.39, 0.05]	12	−0.12 [−0.21, −0.02]	3	0.67 [0.47, 0.87]
08 Average sentence lengthwO (RQ1)	13	0.08 [−0.01, 0.16]	5	0.09 [−0.09, 0.28]	6	0.15 [0.04, 0.26]	2	−0.21 [−0.42, 0.01]
18 Negative emotions a (RQ3a)	21	−0.14 [−0.21, −0.07]	6	−0.19 [−0.36, −0.03]	13	−0.01 [−0.10, 0.07]	2	−0.56 [−0.74, −0.39]
18.1 Negative emotions only (RQ3a)	21	−0.20 [−0.27, −0.13]	6	−0.20 [−0.37, −0.03]	13	0.00 [−0.09, 0.09]	2	−1.03 [−1.21, −0.86]
15 Emotions (RQ3c)	23	−0.22 [−0.29, −0.15]	4	−0.20 [−0.42, 0.01]	15	−0.10 [−0.19, −0.02]	4	−0.53 [−0.66, −0.39]
28 Sensory–perceptual processes a (RQ5)	25	0.08 [0.02, 0.15]	7	−0.22 [−0.39, −0.06]	15	0.12 [0.03, 0.20]	3	0.21 [0.07, 0.35]
28.1 Sensory–perceptual processes only (RQ5)	25	0.08 [0.02, 0.15]	7	−0.29 [−0.45, −0.13]	15	0.12 [0.03, 0.20]	3	0.25 [0.12, 0.38]
29 TimewO (RQ5)	21	0.03 [−0.05, 0.10]	4	0.20 [0.01, 0.40]	15	−0.02 [−0.10, 0.07]	2	0.09 [−0.12, 0.30]

Note. RQ = research question; k = number of hypothesis tests; g_u = ES Hedges’s g_u, positive g_us indicate higher frequencies in true accounts, negative g_us indicate higher frequencies in deceptive accounts; ES = effect size; CI = 95% confidence interval; ^wO = without outlier; bold ES indicate significant difference from zero; ES in italics correspond to the largest difference between liars and truth-tellers (in the predicted direction according to the RQ) for a specific cue across the moderator variable levels; this indicates under what level of the moderator variable our hypotheses were most strongly supported.

a

Indicates that the specific linguistic cue is an umbrella term.

Other linguistic cues under various research questions showed findings contrary to our hypothesis (see Table 5): (a) Only highly motivated liars used fewer different words (type-token ratio: 0.67), (b) only moderately motivated liars built slightly shorter sentences (average sentence length: 0.15) than truth-tellers, (c) liars expressed more negative emotional words than truth-tellers only when they were highly (−0.56, −1.03) or not motivated (−0.20, −0.19), (d) liars expressed more unspecified emotions (−0.53) than truth-tellers when highly motivated, and (e) both highly (0.21, 0.25) and moderately motivated (both 0.12) liars reported fewer sensory–perceptual processes than truth-tellers, whereas not motivated liars tended to refer more often to these processes than truth-tellers (−0.22, −0.29).

Taken together, these results show a mixed picture. Our prediction that highly motivated liars would control their verbal behavior better than less motivated liars was confirmed for only two cues. However, our findings should not be over-interpreted because the number of studies with highly motivated participants was very small—calling for more research with highly motivated liars.

Production mode

Moderator analyses showed mixed findings (see Table 6). Liars used fewer words (Research Question 1—Cognitive Load) than truth-tellers under all production modes, though effects were larger for handwritten texts (0.33) and for transcripts from spoken accounts (0.26) than for typed texts (0.10). It seems that storytellers may use the opportunity to edit their accounts when typing, thus reducing the number of errors. More direct evidence for this point comes from a study by Derrick et al. (2012) who developed a specialized computer applet that clandestinely recorded edits and revisions during real time synchronous communication between a computer interviewer and senders. They found that when deceiving, people were significantly more likely to take longer and perform a greater number of edits to their responses (more frequently using the delete and backspace keys) than when telling the truth. To the extent that deceptive individuals are more likely to engage in such editing, differences in writing errors between true and false statements may be obscured. This might explain why the effect for number of typed words is smaller than for number of handwritten or spoken words.

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Table 6.

Effect Sizes of Linguistic Cues to Deception When Studies Used Different Modes of Producing an Account.

Linguistic cue (RQ)	k	Overall gu [CI]	k 1	Handwritten	k 2	Typed	k 3	Spoken
01 Word quantity (RQ1)	38	0.19 [0.13, 0.24]	6	0.33 [0.21, 0.44]	14	0.10 [0.03, 0.17]	18	0.26 [0.15, 0.36]
17 Negations (RQ3a)	19	−0.19 [−0.26, −0.12]	5	−0.60 [−0.72, −0.46]	5	0.06 [ − 0.05, 0.17]	9	−0.14 [−0.28, −0.01]
18.1 Negative emotions only (RQ3a)	23	− 0.04 [ − 0.11, 0.03]	3	−0.28 [−0.51, −0.05]	8	0.07 [ − 0.03, 0.16]	12	−0.14 [−0.25, −0.02]
15 Emotions (RQ3c)	21	−0.29 [−0.36, −0.22]	4	−0.25 [−0.44, −0.07]	6	−0.44 [−0.54, −0.35]	11	− 0.04 [ − 0.16, 0.08]
28 Sensory–perceptual processes a (RQ5)	24	0.06 [ − 0.01, 0.13]	3	0.33 [0.11, 0.54]	8	0.01 [ − 0.08, 0.11]	13	0.06 [ − 0.06, 0.17]
28.1 Sensory–perceptual processes only (RQ5)	24	0.05 [ − 0.01, 0.12]	3	0.34 [0.12, 0.56]	8	0.00 [ − 0.09, 0.10]	13	0.05 [ − 0.07, 0.16]
30 Space (RQ5)	22	0.04 [ − 0.03, 0.11]	3	0.03 [ − 0.19, 0.24]	6	0.13 [0.02, 0.23]	13	− 0.06 [ − 0.17, 0.05]
36 Motion verbswO (RQ5)	16	−0.09 [−0.17, −0.01]	2	−0.28 [−0.57, 0.00]	4	0.00 [ − 0.11, 0.11]	10	−0.16 [−0.29, −0.04]

Note. RQ = research question; k = number of hypothesis tests; g_u = ES Hedges’s g_u, positive g_us indicate higher frequencies in true accounts, negative g_us indicate higher frequencies in deceptive accounts; ES = effect size; CI = 95% confidence interval; ^wO = without outlier; bold ES indicate significant difference from zero; ES in italics correspond to the largest difference between liars and truth-tellers (in the predicted direction according to the RQ) for a specific cue across the moderator variable levels; this indicates under what level of the moderator variable our hypotheses were most strongly supported.

a

Indicates that the specific linguistic cue is an umbrella term.

In line with our hypothesis concerning details (Research Question 5), liars expressed fewer sensory–perceptual words than truth-tellers only when writing their accounts by hand (0.33, 0.34), whereas liars used fewer spatial details than truth-tellers only in typed accounts (0.13). Contrary to our hypothesis (but in line with Newman et al.’s, 2003, assumption), liars used more motion verbs than truth-tellers when handwriting (−0.28) or speaking (−0.16), but not when typing them (0.00).

Our hypothesis concerning negative emotions (Research Question 3a) was not supported: Liars expressed more negations and negative emotions than truth-tellers when handwriting (−0.60, −0.28, respectively) rather than when speaking or typing. A potential reason for the larger effect in the handwriting condition could be that a writer might take more time to re-experience a negative emotion linked to the process of lying (see Ekman, 1988). Also, the special advantage to edit typed words could be a reason why the difference between liars and truth-tellers disappeared under this condition. Interestingly, regarding unspecified emotions (Research Question 3c), liars’ spoken messages—compared with truth-tellers’—showed no differences (−0.04) in unspecified emotion words but more when typing (−0.44) or handwriting (−0.25).

In conclusion, the question of how the mode of production affects the language of lying is not sufficiently answered. Again, other moderators such as interaction type or motivation may be confounded in these analyses. The pattern of findings that typed accounts showed smallest effects also converges with the finding that computer-mediated communication showed smallest effects (Table 4 above). Future studies should investigate interaction intensity and production mode in more detail, perhaps controlling for language proficiency and typing skill.

Computer program

The hypothesis that effects would be larger if statements were analyzed with programs specifically designed to detect deception (k = 8) rather than with LIWC (k = 26) or general programs (k = 11) was only confirmed for first-person plural pronouns (−0.31; see Table 7). Specific programs were more sensitive than LIWC or other general programs to differences in first-person plural pronouns, finding more of these words among liars than among truth-tellers. Contrary to our hypothesis, four linguistic cues were found to have larger effects if LIWC or other general programs were used than with more specific programs. The direction of the effect for word quantity was even reversed if specific lie detection software was used. A parsimonious explanation may be that these specific programs were developed and used for different types of accounts. It also demonstrates that the validity of linguistic cues to deception depends on the kind of program used. However, this conclusion is limited by the fact that we had to exclude quite a few studies using specialized software as these did not contain sufficient information to calculate effect sizes. Journal editors and grant agencies should emphasize completeness of data reporting including effect sizes (APA Publications and Communications Board Working Group on Journal Article Reporting Standards, 2008).

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Table 7.

Effect Sizes of Linguistic Cues to Deception When Studies Used LIWC, a General Program or a Specific Program.

Linguistic cue	k	Overall gu [CI]	k 1	LIWC	k 2	General program	k 3	Specific program
01 Word quantity	41	0.25 [0.20, 0.30]	23	0.28 [0.21, 0.34]	10	0.53 [0.43, 0.63]	8	−0.19 [−0.30, −0.07]
15 Emotions	25	−0.25 [−0.41, −0.28]	19	−0.39 [−0.45, −0.32]	—	—	6	− 0.14 [ − 0.29, 0.02]
17 Negations	20	−0.15 [−0.22, −0.08]	17	0.05 [ − 0.03, 0.12]	3	−0.82 [−0.96, −0.69]	—	—
20 Total pronounswO	18	0.29 [0.20, 0.38]	13	0.38 [0.28, 0.49]	2	0.06 [ − 0.17, 0.28]	3	− 0.13 [ − 0.44, 0.17]
			k 1	LIWC + General program			k 2	Specific program
22 First-person plural	25	− 0.04 [ − 0.10, 0.02]	18	0.01 [ − 0.05, 0.08]			7	−0.31 [−0.46, −0.15]

Note. RQ = research question; k = number of hypothesis tests; g_u = ES Hedges’s g_u, positive g_us indicate higher frequencies in true accounts, negative g_us indicate higher frequencies in deceptive accounts; ES = effect size; CI = 95% confidence interval; LIWC = Linguistic Inquiry and Word Count; ^wO = without outlier; bold ES indicate significant difference from zero; ES in italics correspond to the largest difference between liars and truth-tellers (in the predicted direction according to the RQ) for a specific cue across the moderator variable levels; this indicates under what level of the moderator variable our hypotheses were most strongly supported.

Publication bias

The correlation between sample sizes (number of accounts) and the absolute value of all effect sizes (excluding extremely large samples to avoid skewed distributions) was r(904) = −.11, p < .001. This negative correlation could be due to a publication bias, that is, a tendency for significant findings to be more likely to be published than unpublished (Levine et al., 2009). However, our moderator analyses showed that for 7 of 12 cues, for which there was a significant difference between published and unpublished studies, effects were actually greater in unpublished studies (see Appendix E in supplemental online materials). Thus, publication bias is unlikely to be a threat to the validity of our conclusions.