Believing Journalists, AI, or Fake News: The Role of Trust in Media

Abstract

An increasing amount of news is generated automatically by artificial intelligence (AI). While the technology has advantages for content production, e.g., regarding efficiency in aggregating information, it is also viewed critically due to little transparency in obtaining results and possible biases. As news media are dependent on trust and credibility, introducing AI to facilitate mass communication with consumers seems to be a risky endeavor. We expand research on consumer perception of AI-based news by comparing machinewritten and human-written texts to fake news and by examining the role of trust that consumers exhibit when evaluating news. Through an experiment with 263 participants, we find that consumers judge AI-based texts similar to true journalistic content when it comes to credibility, but similar to fake news regarding readability. Furthermore, our results indicate that consumers with low trust in media are less averse to AIbased texts than consumers with high trust in media.

Consumer Trust in AI–Human News Collaborative Continuum: Preferences and Influencing Factors by News Production Phases

“…There is extensive research on trust in brands/organizations in general (e.g., Delgado-Ballester and Luis Munuera-Alemán 2005; Kramer and Tyler 1996) and trust in AI in particular (Glikson and Woolley 2020). In recent years, combining research on trust in AI with trust in media companies using the technology in the production process has received increasing interest (e.g., Hofeditz et al 2021;Kolo et al 2022;Lee et al 2020). Nevertheless, most studies have focused on the internal perception of AI integration into the production process (Kim and Kim 2020) or on consumers' ability to differentiate between content written by humans and AI (Kolo et al 2022).…”

“…In recent years, combining research on trust in AI with trust in media companies using the technology in the production process has received increasing interest (e.g., Hofeditz et al 2021;Kolo et al 2022;Lee et al 2020). Nevertheless, most studies have focused on the internal perception of AI integration into the production process (Kim and Kim 2020) or on consumers' ability to differentiate between content written by humans and AI (Kolo et al 2022). Additional research has focused on the perception of brands using AI, not on the product specifically, leaving a research gap on the impact of including AI in various aspects of content production, especially those types of content that have significant social consequences, such as news.…”

Biometrics in online media: an anti-crisis paradigm shift

“…Recently-published systematic reviews explored specific types of online misinformation, particularly fake news, their characteristics and impact (Di Domenico et al, 2020;Pennycook, Rand, 2021). Detection methods for identifying and countering misinformation, including both human-generated and AI-generated content (Shin, Chan-Olmsted, 2023;Kolo et al, 2022), have also been extensively examined in the literature (Zhang, Ghorbani, 2020). The fusion of traditional and digital media, the permeability of information across various platforms, and the pervasive influence of technology have tested the media by transforming its environment in unprecedented ways.…”