Papers › Combination Of Convolution Neural Networks And Deep Neural Networks For Fake News Detection
Combination Of Convolution Neural Networks And Deep Neural Networks For Fake News Detection
ZAINAB A. JAWAD, AHMED J. OBAID
Nowadays, People prefer to follow the latest news on social media, as it is cheap, easily accessible, and quickly disseminated. However, it can spread fake or unreliable, low-quality news that intentionally contains false information. The spread of fake news can have a negative effect on people and society. Given the seriousness of such a problem, researchers did their best to identify patterns and characteristics that fake news may exhibit to design a system that can detect fake news before publishing. In this paper, we have described the Fake News Challenge stage #1 (FNC-1) dataset and given an overview of the competitive attempts to build a fake news detection system using the FNC-1 dataset. The proposed model was evaluated with the FNC-1 dataset. A competitive dataset is considered an open problem and a challenge worldwide. This system's procedure implies processing the text in the headline and body text columns with different natural language processing techniques. After that, the extracted features are reduced using the elbow truncated method, finding the similarity between each pair using the soft cosine similarity method. The new feature is entered into CNN and DNN deep learning approaches. The proposed system detects all the categories with high accuracy except the disagree category. As a result, the system achieves up to 84.6 % accuracy, classifying it as the second ranking based on other competitive studies regarding this dataset.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Fake News Detection | FNC-1 | ZAINAB A. JAWAD, AHMED J. OBAID (CNN and DNN with SCM, 2022) | Per-class Accuracy (Agree) | 88.47 | #2 of 10 | Archive leaderboard | report |
| Fake News Detection | FNC-1 | ZAINAB A. JAWAD, AHMED J. OBAID (CNN and DNN with SCM, 2022) | Per-class Accuracy (Disagree) | 96.00 | #2 of 10 | Archive leaderboard | report |
| Fake News Detection | FNC-1 | ZAINAB A. JAWAD, AHMED J. OBAID (CNN and DNN with SCM, 2022) | Per-class Accuracy (Discuss) | 87.70 | #2 of 10 | Archive leaderboard | report |
| Fake News Detection | FNC-1 | ZAINAB A. JAWAD, AHMED J. OBAID (CNN and DNN with SCM, 2022) | Per-class Accuracy (Unrelated) | 95.04 | #2 of 10 | Archive leaderboard | report |
| Fake News Detection | FNC-1 | ZAINAB A. JAWAD, AHMED J. OBAID (CNN and DNN with SCM, 2022) | Weighted Accuracy | 84.60 | #2 of 10 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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