Name
Abstract | ||
|---|---|---|
Prior work on algorithmic truth assessment on unreliable content, has mostly pursued binary classifiers - factual vs. fake - and disregarded the finer shades of untruth. On the other hand, manual analysis of questionable content has proposed a more fine-grained classification: distinguishing between hoaxes, irony and propaganda, or the six-way rating by the PolitiFact community. In this paper, we present a principled approach to capture these finer shades in automatically assessing and classifying news articles and claims. We systematically explore a variety of signals from both news and social media, and give an analysis of the underlying features.
|
Year | DOI | Venue |
|---|---|---|
2018 | 10.5555/3382225.3382355 | ASONAM '18: International Conference on Advances in Social Networks Analysis and Mining
Barcelona
Spain
August, 2018 |
Keywords | Field | DocType |
fake news, unreliable content, social media, fine-grained classification | Irony,Social media,Computer science,Fake news,Artificial intelligence,Natural language processing,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-5386-6051-5 | 0 | 0.34 |
References | Authors | |
1 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liqiang Wang | 1 | 0 | 0.34 |
| Yafang Wang | 2 | 134 | 13.56 |
| Gerard de Melo | 3 | 723 | 53.54 |
| Gerhard Weikum | 4 | 12710 | 2146.01 |