Detecting Fake News Using AI in Liberia

Local Chapter Liberia Chapter

Coordinated by Spain ,

Project background.

In this digital age, the spread of fake news has become a significant challenge. Many years ago, such information was carried to its target audiences through word of mouth, but today the spread of misinformation has been hastened and weaponized by social media, which is one of the most convenient ways of conveying information in Liberia. From gruesome experiences, fake news carried throughout Liberia has led to atrocities and nearly brought the nation to the brink of collapse. The action of some Liberians has set in motion negative communal responses that have unleashed chaos in the past. Fake news in Liberia is commonly called “Dey Say,” a statement told by an unknown person. Dey Say is usually accepted as fact among many young Liberians and illiterates.

In 2020, violence took place in Gbarpolu County during the senatorial elections. The election was fueled with hate speech and misinformation, which spread throughout the nation via social media. The 2020 senatorial elections in Gbarpolu hang in uncertainty after a local paramount chief in Normondatonu district, called Chief McGill Wleh, confiscated the ballot boxes on the morning of the elections, leaving voters stranded. Chief Mcgill Wleh’s justification was that he believed citizens from Sierra Leone planned to cross the border and vote in the senatorial elections – information that has not been proven to be factual. The National Elections Commission (NEC) tried to organize a rerun, but that plan was aborted following more violence during which supporters of independent candidate Kanneh Botoe and locals in Normondatonu clashed in multiple acts of violence that targeted NEC staff as well.

Project plan.

  • Week 1

    Data Collection

  • Week 2

    Exploratory Data Analysis

  • Week 3


  • Week 4

    Feature Extraction

  • Week 5

    Model Development and Training

  • Week 6

    Model Evaluation

  • Week 7

    Model Integration

  • Week 8

    Model Deployment

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