The Relationship between the Use of Emoticons and Virtual Engagement on Facebook among the Expatriates in the UAE

This study explores the practice of utilizing emoticons on Facebook and the different levels of virtual engagement according to gender among the expatriates in United Arab Emirates. The primary responses from 236 expatriates were collected using survey questionnaire. The data collected included demographic variables that constitute gender, age and nationality, frequency, social engagement level and motives of use of emoticons. The variables were analyzed using frequencies, correlations and t- tests. The results of the analysis show that both males and females used emoticons on Facebook; however, emoticons were used more frequently in personal communication than on official chats. It was further found that the main motives for the utilization of emoticons were to express emotions and to enhance the meaning of chat

The Relationship between the Use of Emoticons and Virtual Engagement on Facebook among the Expatriates in the UAE

This study explores the practice of utilizing emoticons on Facebook and the different levels of virtual engagement according to gender among the expatriates in United Arab Emirates. The primary responses from 236 expatriates were collected using survey questionnaire. The data collected included demographic variables that constitute gender, age and nationality, frequency, social engagement level and motives of use of emoticons. The variables were analyzed using frequencies, correlations and t- tests. The results of the analysis show that both males and females used emoticons on Facebook; however, emoticons were used more frequently in personal communication than on official chats. It was further found that the main motives for the utilization of emoticons were to express emotions and to enhance the meaning of chat

USING AND UNDERSTANDING OF SEMIOTIC ELEMENTS IN ONLINE MEDIA

Symbolic activity widely disseminated through online communication has become a media communication trend in recently. The signs that are used to replace language are adapted in such a way that they serve as models of meaning, which symbolically link perception to cognition. Through the use of these signs in communication, we manage to distribute our knowledge in different meaningful situations, which have an effect on the common use of language.This paper aims to describe and analyze the development of symbolic meanings (combination of written text with different visual forms) in media communication, through the expression and content they convey in communication. Understanding text and language richness in visual meaning systems (emoji and language) will be evidenced from social networks (i.e: Facebook) and online media, using observational and descriptive methods. The collected data show that the images in the text play the role of a meaning system interacting with the language, manifesting thoughts and perceptions through the emotional attitude of the speaker. At the end of this paper, it will be shown that images play a special role in media communication, because they convey connotative meanings as forms of expressions and emotions, which are combined with the common use of languag

Effect of Text Processing Steps on Twitter Sentiment Classification using Word Embedding

Processing of raw text is the crucial first step in text classification and sentiment analysis. However, text processing steps are often performed using off-the-shelf routines and pre-built word dictionaries without optimizing for domain, application, and context. This paper investigates the effect of seven text processing scenarios on a particular text domain (Twitter) and application (sentiment classification). Skip gram-based word embeddings are developed to include Twitter colloquial words, emojis, and hashtag keywords that are often removed for being unavailable in conventional literature corpora. Our experiments reveal negative effects on sentiment classification of two common text processing steps: 1) stop word removal and 2) averaging of word vectors to represent individual tweets. New effective steps for 1) including non-ASCII emoji characters, 2) measuring word importance from word embedding, 3) aggregating word vectors into a tweet embedding, and 4) developing linearly separable feature space have been proposed to optimize the sentiment classification pipeline. The best combination of text processing steps yields the highest average area under the curve (AUC) of 88.4 (+/-0.4) in classifying 14,640 tweets with three sentiment labels. Word selection from context-driven word embedding reveals that only the ten most important words in Tweets cumulatively yield over 98% of the maximum accuracy. Results demonstrate a means for data-driven selection of important words in tweet classification as opposed to using pre-built word dictionaries. The proposed tweet embedding is robust to and alleviates the need for several text processing steps.Comment: 14 pages, 3 figures, 7 table

Anaphoricity in emoji: An experimental investigation of face and non-face emoji

Emoji are widely used, but have received relatively little attention in psycholinguistic research. Upon encountering a message consisting of both text and emoji, readers presumably construct some link between emoji and text. Based on a psycholinguistic study on text-emoji relations, we argue for (at least) two types of emoji-text dependencies, related to referential dependencies known to exist in the linguistic domain, namely (i) the dependency between an expressive (e.g. wow, damn, f*king) and the individual whose opinion it expresses, and (ii) the dependency between a pronoun (or other pro-form) and its antecedent. We extend the discussion of these dependencies to emoji, and provide experimental data that face emoji resemble expressives in that they tend to be interpreted as expressing the opinion of a salient experiencer, while action emoji are interpreted based on principles of discourse coherence (e.g. discourse relations like explanation), similar to what coherence-based accounts of pronoun resolution predict

The role of emojis and emoticons in social engineering

The purpose of this paper is to place the use of emojis and emoticons in communication, e.g., on social networks and with SMSs, to the field of social engineering. With a systematic literature survey in academic databases Web of Science and Scopus, and opportunistic search, 40 scientific papers about emojis and emoticons in social engineering were identified. The use of emojis and emoticons in communication is a form of direct communication as the sender directly influences the recipient. Emojis and emoticons are a universal tool that is understood by all communication technology users regardless of their demographics. Most of the analyzed studies employ a quantitative approach and span different research areas, such as marketing, psychology, sociology, etc. This paper represents one of the first systematic literature reviews that deal with emojis and emoticons in the context of social engineering. The findings of this paper may serve as a basis for further, especially qualitative research

Outdoor sports brands’ strategies for building Instagram brand community

Over 56% of the world’s population now live with social media (“Digital in 2019,” n.d.). Most direct-to-consumer brands are now using social media as a market tool to communicate with consumers, and the outdoor industry is no exception. Instagram, as the second most popular social networking medium globally, is a popular place to share photos and videos within the online brand community. Most outdoor brands maintain Instagram accounts as a part of their online brand community to interact with followers. This research examines 957 Instagram posts from three leading outdoor sports brands, namely, Arc’teryx, Patagonia, and Salomon via content analysis. The purpose of this study is to investigate post orientations and sports types across the three target brands, and gain insights into their Instagram practices by examining visual elements, textual attributes, and technical factors. Results suggest that outdoor brands with different followers took diverse strategies to build either a transactional or relationship Instagram brand community. Findings from this study offer important implications for researchers as well as practitioners in the domain of social media brand management