TEXTUAL ANALYSIS OF SELECTED X'S (FORMERLY TWITTER) 2023 POST- ELECTION "ETHNIC HATE MESSAGES AGAINST THE IGBO PEOPLE OF

Nigeria’s post-election atmosphere has always been inundated with one form of crisis or the other. The 2023 generalelection was not an exception to this historical fact. This study aimed to determine the degree of post-election ethnichate messages against the Igbo people of Nigeria. The study is anchored on the social responsibility theory. Theresearchers employed qualitative textual analysis as the research methodology, where posts and comments from X(formerly Twitter were textually analysed. Findings from the study revealed that hate messages are targeted againstthe Igbo people of Nigeria. From the hate messages analysed, Igbos were framed as betrayers, political intruders,subservient, people deserving of mass killing, and those never to begiven the opportunity of leadership in Nigeria.Also, the researchers found that hate messages against Igbos continuously strain the unity of Nigeria as each electionseason impacts the unity of the country negatively. Further findings indicated that hate messages against the Igbosemanate from both prominent Nigerians and ordinary citizens. The researchers concluded that Hate Messages aredangerous and can set Nigeria ablaze if left unchecked and recommend that: X (formerly Twitter) and Nigeriagovernment should activate punitive measures against posters of hate messages to deter those who may want to takethat path.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-11-29
DOI
https://doi.org/10.5281/zenodo.14246269
Primary Topic
Hate Speech and Cyberbullying Detection
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

TEXTUAL ANALYSIS OF SELECTED X'S (FORMERLY TWITTER) 2023 POST- ELECTION "ETHNIC HATE MESSAGES AGAINST THE IGBO PEOPLE OF

Zenodo (CERN European Organization for Nuclear Research)
Hate Speech and Cyberbullying Detection
article

TEXTUAL ANALYSIS OF SELECTED X'S (FORMERLY TWITTER) 2023 POST- ELECTION "ETHNIC HATE MESSAGES AGAINST THE IGBO PEOPLE OF

article en

Abstract

Nigeria’s post-election atmosphere has always been inundated with one form of crisis or the other. The 2023 generalelection was not an exception to this historical fact. This study aimed to determine the degree of post-election ethnichate messages against the Igbo people of Nigeria. The study is anchored on the social responsibility theory. Theresearchers employed qualitative textual analysis as the research methodology, where posts and comments from X(formerly Twitter were textually analysed. Findings from the study revealed that hate messages are targeted againstthe Igbo people of Nigeria. From the hate messages analysed, Igbos were framed as betrayers, political intruders,subservient, people deserving of mass killing, and those never to begiven the opportunity of leadership in Nigeria.Also, the researchers found that hate messages against Igbos continuously strain the unity of Nigeria as each electionseason impacts the unity of the country negatively. Further findings indicated that hate messages against the Igbosemanate from both prominent Nigerians and ordinary citizens. The researchers concluded that Hate Messages aredangerous and can set Nigeria ablaze if left unchecked and recommend that: X (formerly Twitter) and Nigeriagovernment should activate punitive measures against posters of hate messages to deter those who may want to takethat path.

Zenodo (CERN European Organization for Nuclear Research)
Peace, Justice and strong institutions
Openalex Percentile: Top 65%
Hate Speech and Cyberbullying Detection
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.