'If you build they will come': Automatic Identification of News-Stakeholders to detect Party Preference in News Coverage

TL;DR

This paper presents a method for automatically identifying stakeholders in news articles to detect inherent bias, using contextual information, external knowledge, and clustering, tested on Indian government policy coverage.

Contribution

It introduces a novel approach combining contextual cues and external knowledge for stakeholder extraction in multi-topic news, with a clustering algorithm for stakeholder grouping.

Findings

Effective stakeholder identification in multi-topic news scenarios

Model generalizes well to different news topics

Improves bias detection in news coverage

Abstract

The coverage of different stakeholders mentioned in the news articles significantly impacts the slant or polarity detection of the concerned news publishers. For instance, the pro-government media outlets would give more coverage to the government stakeholders to increase their accessibility to the news audiences. In contrast, the anti-government news agencies would focus more on the views of the opponent stakeholders to inform the readers about the shortcomings of government policies. In this paper, we address the problem of stakeholder extraction from news articles and thereby determine the inherent bias present in news reporting. Identifying potential stakeholders in multi-topic news scenarios is challenging because each news topic has different stakeholders. The research presented in this paper utilizes both contextual information and external knowledge to identify the topic-specific stakeholders from news articles. We also apply a sequential incremental clustering algorithm to group the entities with similar stakeholder types. We carried out all our experiments on news articles on four Indian government policies published by numerous national and international news agencies. We also further generalize our system, and the experimental results show that the proposed model can be extended to other news topics.