Critical Discourse Analysis of Selected Hate Comments on X Social Media Platform in the 2023 Presidential Election Nigeria

Hate comments on social media significantly impacted Nigeria’s political discourse, particularly during the 2023 presidential election, fueling socio-political divisions and electoral tensions. While Critical Discourse Analysis (CDA) has been widely studied in other contexts, the ideological and linguistic mechanisms underlying hate comments in these elections remain underexplored. This study applies CDA to analyse selected hate comments from the election period. Using a descriptive qualitative design, 30 hate comments from X (formerly Twitter) were purposively selected based on their linguistic patterns, thematic structures, and engagement levels. Van Dijk’s socio-cognitive model, Fairclough’s three-dimensional framework, and Halliday’s Transitivity theory were applied to identify ideological patterns and discourse structures in these comments. Findings revealed that hate comments strategically employed derogatory labeling, neologisms, and metaphors to delegitimise political opponents, reinforce biases, and deepen socio-political divisions. Transitivity analysis highlighted how language shaped ideological narratives of radicalism, exclusion, and power struggles. Socio-cultural factors such as religion, ethnicity, and historical grievances further fueled these trends, impacting voter behavior, institutional trust, and electoral participation. The paper concluded that hate comments reinforced an ‘Us versus Them’ binary, exacerbating political and ethnic tensions. It recommended government-led policy interventions to regulate online hate speech and digital literacy initiatives to educate social media users on the consequences of hate-driven discourse.

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Publication Details

Journal
ABUAD Journal of the Humanities-AGIDIGBO
Published
2025-06-30
DOI
https://doi.org/10.53982/agidigbo.2025.1301.18-j
Primary Topic
Hate Speech and Cyberbullying Detection
Type
article
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article

Critical Discourse Analysis of Selected Hate Comments on X Social Media Platform in the 2023 Presidential Election Nigeria

Kehinde Ikuelogbon, Olubunmi. A Idowu, Adetunji Micheal ALOBA
ABUAD Journal of the Humanities-AGIDIGBO
Hate Speech and Cyberbullying Detection
article

Critical Discourse Analysis of Selected Hate Comments on X Social Media Platform in the 2023 Presidential Election Nigeria

Kehinde Ikuelogbon, Olubunmi. A Idowu, Adetunji Micheal ALOBA
article en

Abstract

Hate comments on social media significantly impacted Nigeria’s political discourse, particularly during the 2023 presidential election, fueling socio-political divisions and electoral tensions. While Critical Discourse Analysis (CDA) has been widely studied in other contexts, the ideological and linguistic mechanisms underlying hate comments in these elections remain underexplored. This study applies CDA to analyse selected hate comments from the election period. Using a descriptive qualitative design, 30 hate comments from X (formerly Twitter) were purposively selected based on their linguistic patterns, thematic structures, and engagement levels. Van Dijk’s socio-cognitive model, Fairclough’s three-dimensional framework, and Halliday’s Transitivity theory were applied to identify ideological patterns and discourse structures in these comments. Findings revealed that hate comments strategically employed derogatory labeling, neologisms, and metaphors to delegitimise political opponents, reinforce biases, and deepen socio-political divisions. Transitivity analysis highlighted how language shaped ideological narratives of radicalism, exclusion, and power struggles. Socio-cultural factors such as religion, ethnicity, and historical grievances further fueled these trends, impacting voter behavior, institutional trust, and electoral participation. The paper concluded that hate comments reinforced an ‘Us versus Them’ binary, exacerbating political and ethnic tensions. It recommended government-led policy interventions to regulate online hate speech and digital literacy initiatives to educate social media users on the consequences of hate-driven discourse.

ABUAD Journal of the Humanities-AGIDIGBOVol. 13(1)
Babcock University (NG)
Openalex Percentile: Top 85%
Hate Speech and Cyberbullying Detection
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