Running Head : DETECTING DECEPTION IN

Text messaging has become wildly popular in the United States, and it has some unique characteristics that may impact interpersonal communication such as deception. Here we report how deception detection operates in text messaging, specifically exploring the roles of context, relationship closeness, message content, and one’s liar type on deception detection. We addressed these questions using a novel data collection method which relied on the archives of undergraduate students’ sent text messages. We find that people are overall accurate at identifying text messages, primarily because most texts are truthful. For those text messages that are deceptive, our participants performed poorly in identifying them, correctly identifying less than 20% of lies. Beyond this central contribution, this study also demonstrates that the other factors described above are important for assessing text messages, which adds an important dimension to the deception detection research. DETECTING DECEPTION IN TEXT MESSAGING 3 Detecting Deception in Text Messaging: The Role of Context, Relationships, the Lie and the Liar Is it possible to detect deception in text messaging, perhaps the most quickly adopted communication medium in human history (Smith, 2011)? The extensive research on deception detection, spanning over fifty years, is not encouraging: people tend to detect lies at a rate roughly 54% of the time, when at chance they have 50-50 odds of being correct (Bond & DePaulo, 2006). Given that text messages lack the usual nonverbal cues and are, by design, quite short (160 characters), it seems unlikely that deception detection accuracy in text messaging should be any different than what prior work has found, if not worse. There are several important factors for text messaging, however, that differentiate it from the typical kind of messages used in standard deception detection paradigms. In the standard paradigm participants are asked to judge a series of stimuli, such as a videotaped recoding of a person denying stealing a wallet (e.g., Burgoon, Blair, Qin, & Nunamaker, 2003), on whether the speaker is being truthful or deceptive. Usually the participant has little to no context other than the stimulus, and must make their decision based only on the cues and information available in the stimulus. Our interest, however, is in text messages that have been exchanged between communication partners who know each other and are part of an ongoing interaction situated within their social life. Recent work suggests that this kind of context can play an important role in deception detection (Levine & McCornack, 1992; Vrij, 1994). For example, in one study the availability of context drastically increased a participant’s ability to detect deception (Levine & McCornack, 1992). Can the contextually embedded nature of text messages between communication partners play a similar role in improving deception detection for this novel DETECTING DECEPTION IN TEXT MESSAGING 4 medium? If this is the case, we should expect higher deception detection accuracy for text messages that are received as part of an ongoing interaction than for text messages that are from a partner but not part of an ongoing interaction (e.g., a message sent from the partner to another person) Furthermore, even lower rates of deception detection should be expected for text messages produced by a random stranger. Other factors emerging in deception research that may affect text messaging detection accuracy include the nature of the relationship to the partner and the actual content or nature of the lie. For example, there is an ongoing debate in the literature about whether it is more difficult to detect deception from an intimate than from a stranger (e.g., Miller, Mongeau, & Sleight, 1986). There is also recognition that lies can be about very different content, such as one’s feelings versus one’s stated achievements, and that lies can be enacted with quite different methods, from an outright lie (e.g., “I got an A on the exam” when in fact the speaker received a lower grade) to much more subtle forms (e.g., “I don’t know how I did on the exam” when the speaker knew they received a low grade) (DePaulo et al, 1996; Hancock, 2004). Are some kinds of content more difficult to detect than others, and are subtle lies more difficult to detect than more blatant ones? Finally, a recent phenomenon highlighted in deception research is the prolific liar, which refers to people that lie at substantially higher rates (e.g., two standard deviations) in their everyday communication than most people (Serota, Levine, & Boster, 2010). While these individuals make up only a small fraction of the population, does their proficiency in lying affect their ability to detect the lies of others? Their frequent use of lying may improve their detection accuracy on the one hand, or their enhanced experience might bias their perceptions of how often others lie and interfere with their ability to perceive when others are being truthful. DETECTING DECEPTION IN TEXT MESSAGING 5 In the present study we seek to address these questions using a novel method of analyzing real texts exchanged between communication partners. We asked pairs of participants to produce recent messages they had sent one another via text messaging, and to retrospectively identify which of these texts was deceptive. We then asked their partner to identify any messages they suspected to be deceptive. This procedure allowed us to calculate detection accuracy for messages exchanged between the partners as part of their everyday communication (high context messages), which we compared to their ability to detect deception in text messages sent by their partner to other individuals (low context messages) or to text messages sent by random others (no context messages). The procedure also allowed us to examine the role of the partners’ relationship on their ability to detect one another’s lies, and to examine how the content and type of the lie affect detection accuracy. Lastly, we identified a small subset of participants as prolific liars that told two standard deviations more lies on average than other participants and assessed their deception detection accuracy relative to more typical liars. Given the small number of prolific liars, this last analysis was limited to a descriptive assessment.