'If you build they will come': Automatic Identification of
News-Stakeholders to detect Party Preference in News Coverage
- URL: http://arxiv.org/abs/2212.08864v1
- Date: Sat, 17 Dec 2022 13:08:39 GMT
- Title: 'If you build they will come': Automatic Identification of
News-Stakeholders to detect Party Preference in News Coverage
- Authors: Alapan Kuila and Sudeshna Sarkar
- Abstract summary: The research presented in this paper utilizes both contextual information and external knowledge to identify the topic-specific stakeholders from news articles.
We carried out all our experiments on news articles on four Indian government policies published by numerous national and international news agencies.
- Score: 1.0787390511207684
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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