We lifted 3 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| mdepak/fake-news-propagation | canonical | 1 of 1 |
| avik-samanta99/Fake-News-Detection | reimplementation | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| get_classifier_by_name | Ran | avik-samanta99/Fake-News-Detection/basic_model.py pointer only (licence: NONE) · get_code("87cf15d67c411383") |
| get_classifier_by_name | Ran | mdepak/fake-news-propagation/basic_model.py pointer only (licence: NONE) · get_code("bad1cf63a92f5f3c") |
| get_metrics | Ran | avik-samanta99/Fake-News-Detection/basic_model.py pointer only (licence: NONE) · get_code("a5b011eedfaada32") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Consuming news from social media is becoming increasingly popular. However, social media also enables the widespread of fake news. Because of its detrimental effects brought by social media, fake news detection has attracted increasing attention. However, the performance of detecting fake news only from news content is generally limited as fake news pieces are written to mimic true news. In the real world, news pieces spread through propagation networks on social media. The news propagation networks usually involve multi-levels. In this paper, we study the challenging problem of investigating and exploiting news hierarchical propagation network on social media for fake news detection. In an attempt to understand the correlations between news propagation networks and fake news, first, we build a hierarchical propagation network from macro-level and micro-level of fake news and true news; second, we perform a comparative analysis of the propagation network features of linguistic, structural and temporal perspectives between fake and real news, which demonstrates the potential of utilizing these features to detect fake news; third, we show the effectiveness of these propagation network features for fake news detection. We further validate the effectiveness of these features from feature important analysis. Altogether, this work presents a data-driven view of hierarchical propagation network and fake news and paves the way towards a healthier online news ecosystem.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1903.09196")
get_code_for_paper("1903.09196")
have("1903.09196")
Connect an agent — have() is free.