Throughout the early part of this century, the world has come to rely increasingly on computer-mediated communication (CMC). Particularly, texting surpassed face-to-face and phone conversation as the primary mode of communication in the UK in 2012 (Noah 2012), and US teenagers reported texting as their primary mode of communicating in 2018 (Common Sense Media 2018). Media vary in their capacity to carry information and communication cues. Application tools such as email, instant messaging on social media, and text messages are considered the leanest media compared to voice-only or full audio-visual media (George et al. 2018). To compensate for the lack of non-verbal cues in CMC, communicators have devised graphic-based emoticons (or emojis), which represent facial expressions, attitudes, and body gestures (Duan et al. 2018; Tossell et al. 2012). Human communication, regardless of the medium over which it occurs, is not always honest. In fact, studies have shown that people tend to lie regularly across multiple media (DePaulo et al. 1996; Hancock et al. 2004). Nevertheless, research on the use of emojis in deceptive communication is scant, and little is known about how perpetrators strategize the use of emojis and how effective they are in thwarting detection (McHaney and George 2021). To address this important issue, we designed an experimental study. We plan on recruiting undergraduate and graduate students from two large US universities. The participants are asked to choose two paintings they like the least and the two paintings they like the most from a selection of 20 paintings and write down what in particular they like or dislike about each of the four paintings. This helps in establishing the ground truth. Next, the participants are asked to use a text-based app to interact with an art student (a confederate) who might have painted some of the paintings. For each painting, the participants are asked about their thoughts, including the paintings they said they did not like but were allegedly painted by the art student interviewing them. We expect most participants to lie in the latter case to avoid the awkward situation. All the communications will be saved and analyzed for the frequency and intensity of the emojis used under various conditions. This research extends the literature on deception detection in the CMC context (George et al. 2018). We expect our study to bring important implications for both research and practice.