Linguistic Implications of Political Tweets


  •  Abdulaziz Alshahrani    

Abstract

Among the social media, Twitter is widely used by political leaders and the public to express opinions about various political issues. These tweets may influence the course of major political events like elections, Brexit and popularity of certain politicians. This common observation led to the research question of this paper: What are the political implications of Twitter postings? To answer this research question, an exploratory qualitative review of tweets was undertaken. Google Scholar was searched using the topic itself as the search term twice with two different time frames till 2019. The search yielded 41 papers. The papers were listed with brief description. A table categorising the methods used in the papers was useful to derive some conclusions. Generally, Twitter can be used for both positive and negative purposes and it can impact either or both the leader and the people who read them. Certain factors are involved in determining the nature of post and its outcomes. Many theories and methods had been used in the papers. Manual, machine learning and automatic analytical tools have been tested and used widely. None of the methods is perfectly suitable for all types of Twitter analysis. General content textual descriptions, characteristics of the texts, symbols, hidden meanings and presentation methods have been used in the tweets examined by the authors. The potential of negative tweets and hate speeches is quite clear. Absence of internal standard definition of these types of posts stands in the way of effective prevention. Some recommendations have been listed based on the findings of this review. A few limitations of this review have also been listed.



This work is licensed under a Creative Commons Attribution 4.0 License.
  • ISSN(Print): 1923-869X
  • ISSN(Online): 1923-8703
  • Started: 2011
  • Frequency: bimonthly

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Google-based Impact Factor (2021): 1.43

h-index (July 2022): 45

i10-index (July 2022): 283

h5-index (2017-2021): 25

h5-median (2017-2021): 37

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