Alternatively, we can profile entire news outlets and look for those that are likely to publish fake or biased content.
no code implementations • • Yifan Zhang, Giovanni Da San Martino, Alberto Barrón-Cedeño, Salvatore Romeo, Jisun An, Haewoon Kwak, Todor Staykovski, Israa Jaradat, Georgi Karadzhov, Ramy Baly, Kareem Darwish, James Glass, Preslav Nakov
We introduce Tanbih, a news aggregator with intelligent analysis tools to help readers understanding what's behind a news story.
For subtask A, all systems improved over the majority class baseline.
Sentiment analysis is a highly subjective and challenging task.
In this paper, we describe our submission to SemEval-2019 Task 4 on Hyperpartisan News Detection.
In the context of fake news, bias, and propaganda, we study two important but relatively under-explored problems: (i) trustworthiness estimation (on a 3-point scale) and (ii) political ideology detection (left/right bias on a 7-point scale) of entire news outlets, as opposed to evaluating individual articles.
A reasonable approach for fact checking a claim involves retrieving potentially relevant documents from different sources (e. g., news websites, social media, etc.
We present a novel end-to-end memory network for stance detection, which jointly (i) predicts whether a document agrees, disagrees, discusses or is unrelated with respect to a given target claim, and also (ii) extracts snippets of evidence for that prediction.
Ranked #5 on Fake News Detection on FNC-1
While sentiment analysis in English has achieved significant progress, it remains a challenging task in Arabic given the rich morphology of the language.
Opinion mining in Arabic is a challenging task given the rich morphology of the language.
Evaluation of machine translation (MT) into morphologically rich languages (MRL) has not been well studied despite posing many challenges.