“Don’t, never no”: Negotiating meaning in ESL among hearing/speaking-impaired netizens
DOI:
https://doi.org/10.12928/eltej.v5i1.5131Keywords:
Deaf-mute, Paralinguistic Signals, Negotiation of Meaning, Lexico-Semantic Features, English as a Second Language, Computer-Mediated CommunicationAbstract
Negotiating meaning can be difficult for the deaf-mute people when being in the hearing and speaking world. Social media offers a platform where the deaf and the mute can engage in meaningful conversations among themselves and between people with hearing and speaking abilities. This paper determined the paralinguistic signals that the deaf-mute students employed in their Facebook posts. Using descriptive-qualitative research design, the study analyzed the lexico-semantic features of their language and how both paralinguistic and linguistic aspects contribute to the negotiation of conceptual meaning. The results revealed that paralinguistic signals are found in emojis, punctuation mark repeats, onomatopoeic spelling, accent stylization, intensification, hashtag and combinations. These signals function to give emphasis or intensify intonation. An emoji is the predominant paralinguistic signal used to compensate the lack of words to express feelings. In addition, distinct lexico-semantic features observed in the data include the incorrect position of words, incorrect lexical choice, redundancy, and insertion of prepositions or the lack thereof. These features do not carry a specific function in negotiating meaning because understanding the semantic content of a message is possible either with or without comprehension of the syntax. Semantic comprehension is not expected to help in the acquisition of the syntactic system because it may be accomplished through the recognition of isolated lexical items and interpretation of non-linguistic cues. Finally, paralinguistic signals and computer-mediated communication for the deaf-mute across generation and race can be considered for future directions of the study and appropriate technological tools may be designed to automate errors found in the social media posts of the deaf-mute.
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