Work

Multi-view Models for Political Ideology Detection of News Articles

Year: 2018

Type: conference-paper

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.

Source: Conference on Empirical Methods in Natural Language Processing (EMNLP)

DOI: 10.18653/v1/d18-1388

Authors: Vivek Kulkarni, Junting Ye, Steve Skiena, William Yang Wang

Institutions: University of California, Santa Barbara, Stony Brook University

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Science Foundation, Defense Advanced Research Projects Agency

Awards: BIGDATA: F: DeepWalking Graphs for Feature Extraction, ABI Innovation: Sequence Optimization for Synthetic Biology