Nonverbal and verbal cues can indicate that a person is deceiving. Although the findings pertaining to verbal deception cues appear contradictory, meta-analyses have found that deceivers are more informal and expressive than truth tellers [5, 6]. Furthermore, deceivers convey their story with a higher pitch of voice, shorter time of talking, and fewer details than truth tellers [3]. Deceivers also differ in linguistic style compared to truth tellers, by showing lower cognitive complexity, using fewer self-references and other references, and using more negative emotional words [7]. Furthermore, research showed that truth tellers have lengthier text messages than deceivers [3].

Multiple indicators of deception can be found in the body language of a deceiver, such as using fewer illustrators, more dilated pupils, and adopting a more open posture [3, 4, 8]. There is, however, no set behaviour or group of behaviours only occurring while deceiving. Therefore, these behaviours are only more or less likely to occur when deceiving and as such do not flawlessly predict deception [3, 8, 9].

The above-mentioned (non)verbal deception cues are predominantly researched in ftf interaction, whereas written communication via computers increases. Therefore, computer-mediated communication increases, by which we mean all communication that is done via some sort of device, including computers, mobile phones, laptops, and tablets and therefore including e-mails, text messages via online messengers or social media, but also video calls [10]. Most CMC interactions occur through text as opposed to for example video calls [11]. Communications mediated by computers differs from ftf communication in two important ways. First, the written CMC lacks the immediacy of distance and time of ftf communication. The asynchronous nature of most computer-mediated conversations means that more thoughts can be put into messages [1]. Second, the nature of online communication is more disembodied than ftf communication: written CMC lacks access to nonverbal visual and auditory cues which are usually present in ftf communication. The lack of nonverbal signals has been associated with difficulty interpreting the tone of the messages [12].

Deceptive cues in CMC found so far regard textual cues. As the act of deception places a demand on people’s cognitive functions, this may affect people’s language use in CMC. This is supported by the results of Toma and Hancock [13] showing that deceivers had shorter descriptive bodies of text in their online dating profiles compared to truth tellers. Research showed mixed evidence regarding the average sentence length and level of informality [5, 14, 15]. As most CMC research focused on verbal cues of deception [4, 16], it is important to gain insight into nonverbal cues of deception in CMC. Even though CMC lacks subtle nonverbal behaviours as displayed during ftf interactions, compensatory mechanisms have been developed for these behaviours: digital behaviours such as, but not exclusively, emojis [17-20].

Emojis denote graphical representations of facial and/or emotional expressions which mostly follow messages in written CMC [20-23]. Evolving since their implementation in 1982, emojis nowadays have various forms and meanings. Their use can have two main underlying motives: (1) aiding personal expressions, such as establishing emotional tone and lightening the mood and (2) reducing ambiguity of discourse [16, 19, 21, 22, 24-26]. Veytia-Bucheli et al. [27] found that people use emojis (1) in social interactions, (2) for expressing their emotions and feelings, (3) for understanding utensils, and (4) as part of their writing style. The five most commonly used emojis were the happy face (42%), the smile (22%), finger pointed up (21%) and angry and sadness emojis (each 18%) [27]. Arafah and Hasyim [28] found, furthermore, that emojis are part of the grammar of digital written communication, being used as punctuation at the end of a sentence.

The ability to recognize the emotion of the emoji depends on culture: The extent to which people are exposed to emojis in their culture influences their emotion recognition ability [29]. Generally, emoji help in understanding the emotions, attitudes, and attention that the sender intends to communicate to the reader [16, 21, 22]. Boutet et al. [30] showed that emojis help to intensify the perceived mood of the sender in a positive way with positive emojis and in a negative way with negative emojis. Additionally, they found that the processing speed and understanding of verbal messages increased when emojis were added in a congruent way [30]. Kimura-Thollander and Kumar [31] found that emojis help to establish a more close personal connection and overcome language differences and help to express peoples’ cultural identities. They are, however, not the same as emotions or gestures [19, 21, 22]. Nonverbal behavior is mostly nonintentional and not always controllable, which makes it more trustworthy in showing a sender’s intentions. Emojis, on the other hand, are intentional and added to messages in a controlled way and thus are less trustworthy [19, 22].

Research indicated that higher use of emoji results in lower detection of deceit [14]. Apparently, receivers are less skeptical when the deceiver tries to cover up their real emotions and thoughts by supporting their messages with more emojis. Briscoe and colleagues [14] argued that the use of emoji indicates that the sender wants to build a relationship and therefore the receiver is less likely to assume deception. However, the role of emojis as deception cues and therefore how they could be used in the detection of deception has not received much attention so far [32].

The current study investigates the use of emoji in deceptive messages compared to truthful messages. Leading to the research question: How does emoji frequency and type differ between deceitful and truthful messages?

Two contrasting hypotheses can be expected based on previous research. In the current study, we explore whether the emoji frequency between deceitful and truthful messages varies. On the one hand, one can expect deceptive messages to contain more emoji than truthful messages. As research indicates that deceivers tend to be intentionally more expressive to make their story believable [5, 6, 15] and want to appear honest and friendly through open and informal body language [16, 19, 21, 24, 26], deceivers can be expected to use more emojis in their expressions. Assuming that emojis are used to support a message intentionally [4], one would expect deceivers to deliberately increase the believability of their story by being more expressive [5, 15]. On the other hand, one can expect deceptive messages to contain fewer emojis than truthful messages since DePaulo and colleagues [3] found that deceivers (unintentionally) use fewer illustrators and have less embraced statements.

Even though people are more likely to deceive about positive than negative matters [2], research indicated that deceivers use more negations [13, 33], more negative emotion words [7], and more negative statements and complaints in their messages [3] compared with truth tellers. Therefore, our third hypothesis is: deceptive messages contain more negative emojis than truthful messages.

To explore the use of emojis in deceptive and truthful messages, we chose an observational procedure. Participants were asked to fill in an online questionnaire and add screenshots of six conversations they had in the past via WhatsApp messenger. These six conversations should include three deceptive and three truthful answers to the questions participants were asked. The frequency and type (valence and intensity) of emoji were assessed for both deceptive and truthful messages. As our main aim of the study is to explore whether a single nonverbal emoji cue can predict deception, we choose to investigate the frequency and type of emoji in deceptive versus non-deceptive messages, regardless of the specific verbal content. In this study, we focused on the use of emojis in social networking rather than email as emojis are more often used in social networking [34]. We chose to focus on WhatsApp messages as WhatsApp is one of the most used CMC [35]. As such, WhatsApp may well provide a communication channel just as amenable to deception attempts as ftf communication. Additionally, WhatsApp has a gallery of emojis and GIF’s that can be shared with others to indicate nonverbal cues and could therefore be used as cues of deception [4, 36].

A within-participant design was used in which the frequency and type (positive vs negative) of emojis were compared between deceitful and non-deceitful messages. The dependent variable was the frequency of emojis per participant. The analysis of the third hypothesis was done per valence (positive versus negative).

The study was an online questionnaire that was published online via Qualtrics. Participants were asked to answer multiple questions about their demographics, the conversations they had with WhatsApp, and were asked to include screenshots from said conversations.

The study took participants five to ten minutes during five or six different moments in a week or about 50 to 60 minutes once. Participants read the informed consent and, after agreeing, they continued reading the instructions. In the instructions, the participants were asked to fill in the questionnaire and upload three deceptive and three nondeceptive messages of WhatsApp conversations. They were informed that they were able to access the questionnaire multiple times during a period of seven days, therefore, they could choose to collect the six messages during that week and fill in the questionnaire at the end of the week or to access the questionnaire multiple times during that week and upload the messages on different occasions. To prevent biased results, they were not fully informed about the true goals of the study; instead, they learned that it investigated deception cues in WhatsApp.

Then they filled in eight demographic questions about age, gender, highest educational status, nationality, country of residence, whether they were born in the Netherlands or Germany, and if not, for how long they had lived in either of the countries, WhatsApp usage per week, and the English skills of the participants.

Participants were then asked to disclose, during the participation, three WhatsApp messages in which they attempted to deceive the recipient, and three messages in which they told the truth. These could be either previously sent messages or messages sent during the week of participation and had to be messages which constituted answers to questions asked by the recipient. Disclosure of deceptive and truthful messages was done by taking screenshots, in which participants marked the part that constituted the lie or the truth and removed the name and other identifiable details of the person they were communicating with for confidentiality reasons.

As a backup, in case the screenshot was not uploaded or marked correctly, participants were also asked to fill in the exact text of the messages. Moreover, they were asked to indicate whether their message was truthful or deceptive. At the end of each form, the participants were able to upload their screenshot¹, which was used for the analyses.

Whenever the participants came back to the Qualtrics-link, they were able to continue where they stopped. After the last session, participants were thanked for their participation and were given the opportunity to be debriefed.

To narrow the research down, only emojis on WhatsApp were included, reflecting facial expressions (N = 60). These emojis were sorted into one of the four main categories of valence (neutral, positive, negative, or surprising) and six subcategories of intensity (weak and strong). This resulted in seven categories (neutral, positive-weak, positive-strong, negative-weak, negative-strong, surprising-weak, & surprising-strong) since no distinction was made between weak and strong intensity in the neutral valence. The valence surprising was added because a surprising state can either be positive or negative but is not a neutral expression [37-39].

The coding of the emojis was done because there was no suitable categorization found in previous studies. Rodrigues et al. [40] for example categorized emojis and emoticons on seven dimensions (aesthetic appeal, familiarity, visual complexity, clarity, valence, arousal, and meaningfulness). Their valence was measured by asking ‘To what extent do you consider this stimulus refers to something positive/pleasant or negative/unpleasant’. In our study, however, we wanted to distinguish between strong and weak emojis as well as neutral and surprising emojis and only include facial expressions.

The emoji were independently sorted into different categories by four individual raters. These scorings were then compared and emoji that were sorted in the same category by all four individuals (N = 14) and by three of the four individuals (N = 31) were sorted into the final table. Finally, emojis that only two individuals rated similarly were checked for differences (N = 15). For these 15 emojis, there were two individuals that sorted the emoji in the same valence and intensity, while a third individual chose the same valence but disagreed on the intensity. The fourth individual disagreed with both valence and intensity; therefore, it was chosen to sort the emoji in the valence of the three individuals and the intensity of the two raters agreeing with each other. No discussion occurred to confine disagreements. In addition to pre-classified emoji, participants also used 6 textual portrayals of facial expressions in the form of icons, which were additionally coded by three raters with complete agreement. Emoji that were used in the conversations that are not included in these categories were sorted as ‘others’. See Appendix A for the division into the categories of emoji.

To explore differences of the frequency of emoji used in deceptive versus truthful messages, a Wilcoxon Signed Rank Test was conducted with the content (deceit versus truth) as independent variable and the number of emoji as the dependent variable. The analysis showed that participants did not use different numbers of emoji in deceptive messages [Median = 0.67, M = 0.68] compared with truthful messages [Median = 0.67, M = 0.68], two-sided Monte-Carlo p = .79, Z = -0.27, 95% CI [0.782, 0.798], d = .039, ƞ² < .001.

To investigate the hypothesis that more negative than positive emojis are used in deceit compared to truth, two Wilcoxon Signed Rank Tests were conducted. The first test was conducted within the positive valence of emoji with the content (deceit versus truth) as the independent variable and frequency of emoji as the dependent variable. This test indicated that participants used significantly more positive emojis in truthful messages [Median = 0.17; M = 0.19] compared to deceptive messages [Median = 0.17; M = 0.12], two-sided Monte-Carlo p < .01, Z = - 3.48, 95% CI [0.000, 0.001], d = .518, ƞ² = .063.

The second test was conducted within the negative valence of emoji with the content (deceit versus truth) as the independent variable and frequency of emoji as the dependent variable. It showed that participants used significantly more negative emojis while deceiving [Median < 0.01; M = 0.08] compared to telling the truth [Median < 0.01; M = 0.04], two-sided Monte-Carlo p < .01, Z = - 4.22, 95% CI [0.000, 0.000], d = .640, ƞ² = .093.

Moreover, four Wilcoxon Signed Rank Tests were conducted to investigate whether these effects hold for different intensities (strong versus weak) of the emoji (Table 1) for the mean number of emoji per message). The tests were done within the valence (positive versus negative) and intensity (strong versus weak) of emojis, with content (lie versus truth) as the independent variable and frequency of emojis as the dependent variable.

First, participants used significantly more positive strong emoji in truth [Median < 0.01, M = 0.13] compared to lies [Median < 0.01, M = 0.07], two-sided Monte-Carlo p = .01, Z = -2.52, 95% CI [0.009, 0.013], d = .37, ƞ² = .033. Second, participants also used significantly more positive weak emoji while telling the truth [Median < 0.01, M = 0.24] compared with telling a lie [Median < 0.01, M = 0.17], two-sided Monte-Carlo p = .01, Z = -2.45, 95% CI [0.010, 0.014], d = .36, ƞ² = .031. Third, participants did not differ in the number of negative strong emoji in deceptive [Median < 0.01, M = 0.04] than truthful messages [Median < 0.01, M = 0.02], two-sided Monte-Carlo p = .50, Z = 0.68, 95% CI [0.528, 0.547], d = .10, ƞ² = .002. Finally, participants used significantly more negative weak emoji while telling a lie [Median < 0.01, M = 0.13] compared to telling the truth [Median < 0.01, M = 0.05], two-sided Monte-Carlo p < .01, Z = 4.61, 95% CI [0.000, 0.000], d = .71, ƞ² = .111 .These four tests indicated that participants used significantly more positive strong and positive weak emoji in truths compared with lies and used more negative weak emojis in lies compared with truths.

As stated earlier research showed that truth tellers have lengthier text messages than deceivers [3], it is conceivable that the number of emoji is affected by the length of text. We did not include this in the hypotheses but after exploring the data we wanted to check for the potential differences in message length. Therefore, an additional explorative analysis was conducted. A Wilcoxon Signed Rank test was conducted to investigate whether deceivers and truth tellers differed in the number of words used in their messages. The test indicated that participants did not use more words in deceptive [Median = 9.00, M = 10.24] compared with truthful messages [Median = 8.17, M = 9.64], two-sided Monte-Carlo p = .10, Z = -1.67, 95% CI [0.094, 0.106], d = .243, ƞ² = .015².

The practical implication of the knowledge about how to identify lies on social media can on the one hand help when communicating with friends and family via WhatsApp, for identifying when somebody lies. Especially now in pandemic times, when most communication is not face-to-face. On the other hand, the analysis of deception detection cues in social media can also help law enforcement identify fraudsters online.