Name
Abstract | ||
|---|---|---|
There is an increasing amount of false claims in news, social media, and other web sources. While prior work on truth discovery has focused on the case of checking factual statements, this paper addresses the novel task of assessing the credibility of arbitrary claims made in natural-language text - in an open-domain setting without any assumptions about the structure of the claim, or the community where it is made. Our solution is based on automatically finding sources in news and social media, and feeding these into a distantly supervised classifier for assessing the credibility of a claim (i.e., true or fake). For inference, our method leverages the joint interaction between the language of articles about the claim and the reliability of the underlying web sources. Experiments with claims from the popular website snopes.com and from reported cases of Wikipedia hoaxes demonstrate the viability of our methods and their superior accuracy over various baselines. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2983323.2983661 | CIKM |
Keywords | Field | DocType |
Credibility Analysis, Rumor and Hoax Detection, Text Mining | Data mining,World Wide Web,Social media,False accusation,Credibility,Information retrieval,Computer science,Inference,Baseline (configuration management),Classifier (linguistics) | Conference |
Citations | PageRank | References |
28 | 1.22 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kashyap Popat | 1 | 72 | 5.70 |
| Subhabrata Mukherjee | 2 | 236 | 21.95 |
| Jannik Strötgen | 3 | 492 | 38.20 |
| Gerhard Weikum | 4 | 12710 | 2146.01 |