Multi-view Models for Political Ideology Detection of News Articles

Abstract

A news article's title, content and link structure often reveal its political ideology. However, most existing works on automatic political ideology detection only leverage textual cues. Drawing inspiration from recent advances in neural inference, we propose a novel attention based multi-view model to leverage cues from all of the above views to identify the ideology evinced by a news article. Our model draws on advances in representation learning in natural language processing and network science to capture cues from both textual content and the network structure of news articles. We empirically evaluate our model against a battery of baselines and show that our model outperforms state of the art by 10 percentage points F1 score.

It Takes Nine to Smell a Rat: Neural Multi-Task Learning for Check-Worthiness Prediction

“…The proliferation of false information has attracted a lot of research interest recently. This includes challenging the truthiness of news (Brill, 2001;Hardalov et al, 2016;Potthast et al, 2018), of news sources (Baly et al, 2018, and of social media posts (Canini et al, 2011;Castillo et al, 2011;Zubiaga et al, 2016), as well as studying credibility, influence, bias, and propaganda (Ba et al, 2016;Chen et al, 2013;Mihaylov et al, 2015;Kulkarni et al, 2018;Baly et al, 2018;Mihaylov et al, 2018;Barrón-Cedeño et al, 2019;Zhang et al, 2019).…”

Team QCRI-MIT at SemEval-2019 Task 4: Propaganda Analysis Meets Hyperpartisan News Detection

“…The analysis of bias and disinformation has attracted significant attention, especially after the 2016 US presidential election (Brill, 2001;Finberg et al, 2002;Castillo et al, 2011;Baly et al, 2018a;Kulkarni et al, 2018;Mihaylov et al, 2018;Baly et al, 2019). Most approaches have focused on predicting credibility, bias or stance.…”

Team Jack Ryder at SemEval-2019 Task 4: Using BERT Representations for Detecting Hyperpartisan News

“…Similarly, Potthast et al (2018) classified the bias in a target article as (i) left vs. right vs. mainstream, or as (ii) hyper-partisan vs. mainstream. Left-vs-right bias classification at the article level was also explored by Kulkarni et al (2018), who modeled both text and URL structure. Some work targeted bias at the phrase or the sentence level (Iyyer et al, 2014), for political speeches (Sim et al, 2013) or legislative documents (Gerrish and Blei, 2011), or targeting users in Twitter (Preoţiuc-Pietro et al, 2017).…”