Journalism, algorithms, and bias: an analysis of startup Knowhere News

Authors

DOI:

https://doi.org/10.5007/1984-6924.2023.94993

Keywords:

Journalism, bias, Algorithms

Abstract

The article starts from the empirical analysis of startup Knowhere News to discuss the implication of the algorithmic production of news items. We understand algorithms as knowledge machines and discuss the ideological implications of software designers in configuring the action guidelines of such tools. From the case, which has impartiality as editorial matrix, we seek to understand the meanings of impartiality proposed by the startup and, from an analysis of the journalistic product, map how the human and non-human agents quest to build impartiality effects in the news items. The study considered a set of six articles, each produced in three versions (labeled as positive, negative, and neutral), totalizing eighteen texts. The analysis was carried out considering the operating axes of natural language generation (NLG) and allowed us to recognize production patterns of the impartiality effects connected to the selection, hierarchization, omission, and lexicalization of information.

Author Biographies

Laura Strelow Storch, Federal University of Santa Maria

Professora na Universidade Federal de Santa Maria/UFSM. Mestre e Doutora em Comunicação e Informação/Universidade Federal do Rio Grande do Sul.

Bruna Meinen Feil, Federal University of Santa Maria

Graduanda na Universidade Federal de Santa Martia/UFSM.

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Published

2024-08-08

Issue

Vol. 20 No. 2 (2023): Jornalismo Artificial?

Section

Núcleo Temático

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