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- Model Performance
- Overall Performance Comparison
- Effectiveness of Weak Supervision
- Ablation Study
- The Impact of Concept Drift
- The Effectiveness of Early Fake News Detection
We show the learning curve for training loss and validation loss during model training in Fig. 7. In our model, the validation loss is quite close to the training loss. The validation loss is slightly higher than training loss, but overall, both values are converging (when plotting loss over time). Overall, it shows a good fit for model learning. W...
We show the best results of all baselines and our FND-NS model using all the evaluation metrics in Table 5. The results are based on data from both datasets, i.e. social contexts from Fakeddit on the NELA-GT-19 news. The input and hyperparameter optimization settings for each baseline model are given above (Sect. 5.5). The best scores are shown in ...
In this experiment, we test the effectiveness of the weak supervision module on the validation data for the accuracy measure. We show different settings for weak supervision. These settings are: 1. M1: Weak supervision on both datasets, NELA-GT-19 and Fakeddit with original labels + user credibility label + crowd response label; 2. M2: Weak supervi...
In the ablation study, we remove a key component from our model one a time and investigate its impact on the performance. The list of reduced variants of our model are listed below: 1. FND-NS: The original model with news and social contexts component; 2. FND-N: FND-NS with news component—removing social contexts component; 3. FND-N(h-): FND-N with...
The concept drift occurs when the interpretation of the data changes over time, even when the data may not have changed . Concept drift is an issue that may lead to the predictions of trained classifiers becoming less accurate as time passes. For example, the news that is classified as real may become fake after some time. The news profiles and use...
In this experiment, we compare the performance of our model and baselines on early fake news detection. We follow the methodology of Liu and Wu to define the propagation path for a news story, shown in Eq. (19): where \(x_{j}\) is the observation sample and \(\mathcal{T}\) is the detection deadline. The idea is that any observation data after the ...
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Final Year Fake News Detection using Machine learning Project with Report, PPT, Code, Research Paper, Documents and Video Explanation.
Detection of fake images shared over social platforms is extremely critical to mitigating its spread. Fake images are often associated with textual data. Hence, a multi-modal framework is employed utilizing visual and textual feature learning.
30,900 Free images of Fake News. Free fake news images to use in your next project. Browse amazing images uploaded by the Pixabay community.
