Linguistic Features and Bi-LSTM for Identification of Fake News

Stance Detection for Fake News Identification

The latest election cycle generated sobering examples of the threat that fake news poses to democracy. Primarily disseminated by hyper-partisan media outlets, fake news proved capable of becoming viral sensations that can dominate social media and influence elections. To address this problem, we begin with stance detection, which is a first step towards identifying fake news. The goal of this p...

On the Benefit of Combining Neural, Statistical and External Features for Fake News Identification

Identifying the veracity of a news article is an interesting problem while automating this process can be a challenging task. Detection of a news article as fake is still an open question as it is contingent on many factors which the current state-of-the-art models fail to incorporate. In this paper, we explore a subtask to fake news identification, and that is stance detection. Given a news ar...

Incremental Parsing with Minimal Features Using Bi-Directional LSTM

Recently, neural network approaches for parsing have largely automated the combination of individual features, but still rely on (often a larger number of) atomic features created from human linguistic intuition, and potentially omitting important global context. To further reduce feature engineering to the bare minimum, we use bi-directional LSTM sentence representations to model a parser stat...

Linguistic Features and Newsworthiness: an Analysis of News style

English. In this paper, we present a preliminary study on the style of headlines in order to evaluate the correlation between linguistic features and newsworthiness. Our hypothesis is that each particular linguistic form or stylistic variation can be motivated by the purpose of encoding a certain newsworthiness value. To discover the correlations between newsworthiness and linguistic features, ...

The Economics of "Fake News"