We conducted an online experiment to study people’s perception of automated computer-written news. Using a 2 × 2 × 2 design, we varied the article topic (sports, finance; within-subjects) and both the articles’ actual and declared source (human-written, computer-written; between-subjects). Nine hundred eighty-six subjects rated two articles on credibility, readability, and journalistic expertise. Varying the declared source had small but consistent effects: subjects rated articles declared as human written always more favorably, regardless of the actual source. Varying the actual source had larger effects: subjects rated computer-written articles as more credible and higher in journalistic expertise but less readable. Across topics, subjects’ perceptions did not differ. The results provide conservative estimates for the favorability of computer-written news, which will further increase over time and endorse prior calls for establishing ethics of computer-written news.

Artificial intelligence (AI) and people’s interactions with it—through virtual agents, socialbots, and language-generation software—do not fit neatly into paradigms of communication theory that have long focused on human–human communication. To address this disconnect between communication theory and emerging technology, this article provides a starting point for articulating the differences between communicative AI and previous technologies and introduces a theoretical basis for navigating these conditions in the form of scholarship within human–machine communication (HMC). Drawing on an HMC framework, we outline a research agenda built around three key aspects of communicative AI technologies: (1) the functional dimensions through which people make sense of these devices and applications as communicators, (2) the relational dynamics through which people associate with these technologies and, in turn, relate to themselves and others, and (3) the metaphysical implications called up by blurring ontological boundaries surrounding what constitutes human, machine, and communication.

In this article, we argue that journalism studies, and particularly research focused on automated journalism, has much to learn from Human-Machine Communication (HMC), an emerging conceptual framework and empirically grounded research domain that has formed in response to the growing number of technologies-such as chatbots, sodas bots, and other communicative agents enabled by developments in artificial intelligence (AI)-that are designed to function as message sources, rather than as message channels. While the underlying, but often unquestioned, theoretical assumption in most communication research is that humans are communicators and machines are mediators, within HMC this assumption is challenged by asking what happens when a machine steps into this formerly human role. More than merely a semantic move, this theoretical reorientation opens up new questions about who or what constitutes a communicator, how social relationships are established through exchange among humans and machines, and what the resulting implications may be for self, society, and communication. In the particular case of automated journalism-in which software assumes a news-writing role that has long been considered a distinctly central, and indeed human, element of journalism-the introduction of HMC offers a generative starting point for theory development, advancing our understanding of humans, machines, and news for an oncoming era of AI technologies.

A comparison of attitudes of Filipino and Indian students in the U.S. toward the mass media supports a general hypothesis that the media are many things to many people.

Although credibility is a complicated construct involving interrelationships among messenger, message, communication channels, and recipients, the most widely used credibility indexes seek to measure only one of those attributes at a time. This study used two widely used credibility scales to simultaneously measure message and messenger credibility. An analysis showed high correlations between the two scales and among their individual variables but still distinguished differences between messenger and message.