
This folder contains the source code along with training and testing datasets for a Fake News Detection Dashboard. The dashboard allows users to detect disinformation using six standard benchmark datasets.The front end, developed with Streamlit, provides an interactive interface for uploading files in CSV, PDF, or DOCX formats, and visualizes results using bar charts and word clouds. Its modular architecture separates data ingestion, preprocessing, model inference, and visualization, ensuring scalability and maintainability. The dashboard has been applied to multiple datasets, including EUvsDisinfo, EUvsISOT, EUvsIGF, FA-KES, George McIntire, and ISOT, enabling large-scale predictions, cross-dataset generalizability assessment, propagation analysis, and exploration of textual patterns contributing to disinformation.
Fake News, Disinformation, NLP, EU Disinfo Lab, Dashboard, datasets, Streamlit
Fake News, Disinformation, NLP, EU Disinfo Lab, Dashboard, datasets, Streamlit
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
