Non-verbal communication is a system involving an assortment of features often used together to support expression [1]. The field of examining nonverbal communication has begun with the publication of Charles Darwin book The Expression of the Emotions in Man and Animals in 1872 [2] and it is considered as the spark that influenced many researchers to study nonverbal behavior and after that, Ekman and Friesen presented a classification for five types of communicative movements which reflected communicative functions [3]. According to researchers in [4], we can classify nonverbal communication into three main categories: Kinesics, Haptics, and Proxemics. Each of these aspects is further divided into other types. Kinesics includes the use of gestures, head movements and posture, eye contact, and facial expressions, Proxemics includes any aspect that relates to space and distance and how it influences our communication, and Haptics includes any kind of communication by touch. Other researchers as [1] include in addition to the previously stated types Vocalics or paralinguistics like Tone of voice, volume, speed, and pitch and Appearance like hairstyle and clothing into their classification for nonverbal communication.

Nonverbal communication is an integral part of our daily face-to-face communication. Most of the studies suggest that nearly 60 - 70 percent of our communication accounts for the non-verbal component [5], other researchers suggest the Albert Mehrabian’s 7-38-55 percent communication rule which proposes that only 7 percent of our communication is verbal, and 93 percent is nonverbal; 38 percent vocal, and 55 percent facial [6]. From the previously stated percentages, it is apparent how humans cannot communicate ideas, feelings, and attitudes efficiently without relying on nonverbal cues, however, on Text-based computer-mediated communication as social media platforms communicators only communicate through text which poses a challenge for them to express the nonverbal cues while delivering their messages. One of the situations that influenced the researchers to study the representations of the nonverbal cues on Twitter is the misunderstanding that occurs between people while texting, for example, people could complain that one of the texters is rising his/her voice when one character is repeated inside the word or when an emoji does not fit the context although the verbal message is written correctly and that reflects the importance of the nonverbal cues in written messages and how it is more strong than the verbally written messages.

The following research tries to answer the following questions. How Arabs across different dialects compensate for the absence of nonverbal cues on Twitter? Does Twitter limit and restrict the use of nonverbal cues? How the nonverbal components as facial expressions, paralanguage, and prosodic features are expressed by the means of emojis, character repetition, punctuations, and interjections?

Few works had been done on examining nonverbal cues across text-based computer-mediated communication especially on social networking platforms like Facebook and Twitter, however, in the following years with the increasing number of users on online platforms and after the COVID-19 pandemic which directed people towards a more digitalized world, this number will increase. This section will present the previous work examined nonverbal communication on Twitter.

Park and El Mimouni study [7] is considered one of the most important studies that focused on cross-cultural nonverbal communication across Twitter, the main goal of this study is to examine how users are drawn from three different languages and cultural boundaries manage the lack of contextual cues through an analysis of Arabic, English, and Korean tweets. They examined linguistic contractions, (un)conventional shorthand, prosodic features, punctuations, Language-specific features, and emoticons and showed by examples how each of these categories are expressed across the three languages.

Tantawi and Rosson [8] investigated the paralinguistic function of emojis on Twitter, 1600 tweets were examined. The analysis conducted on the collected tweets was divided into two main aspects; the first aspect is topic analysis to find out the most common topics on Twitter and the second aspect focused on finding out the emojis function, the tweets they analyzed contained three paralinguistic features for emojis; attitude, gesture, and topic. Their results showed that emojis are primarily used to signal attitudes and emotions. They also discussed implications for the design of Twitter and other text-based communication tools.

Álvarez and Muñoz study [9] has twofold goals; the first one is to describe tweets on Twitter from a discourse contrastive point of view and to study several numbers of distinguishing features of language use that are specific to this social network in Spanish and English [9]. One of the most important findings of the study is how the character limitation that Twitter force upon their users did not cause communication failures on the contrary they were able to communicate themselves very well.

To reach the research goals the following stages have been applied: the first stage was concerned with data collection, the second stage focused on data cleaning and normalization, the third stage is the data analysis which has been done by using python scripts to get the corpus pure structure information and measurements for the nonverbal devices categories which are presented in Table 1. In

addition to that, a manual analysis was done to investigate the nonverbal devices inside the corpus for each Arabic dialect.

After data cleaning and normalization, the analysis was performed on two main levels; the first level focused on examining the emojis usage in Arabic language and across different dialects and that was analyzed with the help of custom python scripts. The second level focused on describing what devices twitter users use to compensate for the absence of nonverbal cues with 280 characters restriction for each tweet. For dialectal analysis, only dialects with more than 100 tweets were subjected to further analysis. Table 1 summarizes the nonverbal device categories examined in our study; some of these categories were derived from previous literature analysis.

Text-based computer-mediated communication social media platforms like Twitter do not limit and restrict the use of nonverbal cues, on the contrary, Twitter users find creative ways to show and express the nonverbal component whether by using emojis to represent their facial expressions, punctuation, and character repetition to reflect the paralinguistic prosodic features or even by using vocal expressions as Interjections to express their emotional state. Talking specifically about Arabs it seems that there is a little difference in using emojis, but when it comes to gestures significant differences were found. The Face with Tears of Joy came in the first place across all dialects as the most used emoji. Egyptians seem to use hand gestures more than other dialects, Emirati and Lebanese dialects use them less frequently. The number of emojis per tweet is nearly the same across dialects with an average of 3 emojis per tweet. Arabs rarely use punctuation marks, and it was apparent that emojis are replacing them. The top used punctuation mark was the comma followed by the exclamation mark. Lebanese dialect uses punctuation marks with a higher percentage than other dialects and that is reflected in their higher number of characters per tweet. Character repetition was pervasive across all dialects which reflects intensifying emotions and shows words that are pronounced louder than other words. Arabs use interjections to reflect their emotional state instead of writing a full sentence, only a vocal expression is written.

First and foremost, praise thanks to God for his countless blessing that have made this research possible, and second, I would like to express my thanks and gratitude for my professor Seham El-kareh for her efforts, her guidance, and her consistent support, and I wish her all the beautiful things in life.

The authors declare no conflicts of interest regarding the publication of this paper.

Etman, M. and Elkareh, S. (2021) Nonverbal Communication and Emojis Usage in Arabic Tweets: A Cross-Cultural Study. Social Networking, 10, 19-28. https://doi.org/10.4236/sn.2021.102002