Understanding User Perception of Automated News Generation System

Oh, Changhoon; Choi, Jinhan; Lee, Sung‐Woo; Park, So‐Hyun; Kim, Daeryong; Song, Jungwoo; Kim, Dongwhan; Lee, Joonhwan; Suh, Bongwon

doi:10.1145/3313831.3376811

Cited by 19 publications

(9 citation statements)

References 32 publications

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“…Yet, this effect is not as pronounced when consumers read only one article, as is the case in this study [27]. In addition, when they are factual and accurate, texts written by a software are perceived as such [28]. In comparison, consumers often recognize the false or misleading nature of fake news and sharing rather occurs due to motivated reasoning [29], which is of little importance in the setting of this study.…”

Section: Hypothesesmentioning

confidence: 60%

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

Kolo¹,

Mütterlein²,

Schmid³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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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.

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Section: Hypothesesmentioning

confidence: 60%

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

Kolo¹,

Mütterlein²,

Schmid³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…We have seen a growing body of work on journalism in HCI [1,12,20,52,61,65,69] at a time when online aggregation and algorithmic feeds of news stories have dramatically changed both the news consumption environment and the professional practice of mainstream journalism [12,17]. The online information environment, characterized by a rapid access to updates from across the world, disproportionately favors the "breaking news" form of viral information, often driven by online engagement [35].…”

Section: Journalism and Hcimentioning

confidence: 99%

“…For example, Diakopoulos et al [20] developed Algorithm Tips, a tool designed to support news discovery by helping journalists find newsworthy leads on algorithmic decision-making systems being used across all levels of the US government. Oh et al [52] developed NewsRobot to automatically generate news stories at a scale as major events are unfolding in real time and Wang and Diakopoulos [69] developed a tool to analyze large quantities of user-generated content to support journalists' discovery of news sources from their audience.…”

Section: Journalism and Hcimentioning

confidence: 99%

Understanding Journalists’ Workflows in News Curation

Atreja

Srinath

Jain

et al. 2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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With the increasing dominance of internet as a source of news consumption, there has been a rise in the production and popularity of email newsletters compiled by individual journalists. However, there is little research on the processes of aggregation, and how these differ between expert journalists and trained machines. In this paper, we interviewed journalists who curate newsletters from around the world. Through an in-depth understanding of journalists' workflows, our findings lay out the role of their prior experience in the value they bring into the curation process, their own use of algorithms in finding stories for their newsletter, and their internalization of their readers' interests and the context they are curating for. While identifying the role of human expertise, we highlight the importance of hybrid curation and provide design insights on how technology can support the work of these experts.

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“…EC 2: We chose system/application papers that used generative models to support their tasks. In this regard, qualitative/study papers [68,77] were excluded. EC 3: We chose papers that use or study neural network-based generative models such as GAN, VAE, LSTM, GPT-3, and DALL-E. For instance, the use of conventional generative models like Gaussian mixture models was excluded.…”

Section: Exclusion Criteriamentioning

confidence: 99%

Large-scale Text-to-Image Generation Models for Visual Artists' Creative Works

Ko¹,

Park²,

Jeon³

et al. 2022

Preprint

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Large-scale Text-to-image Generation Models (LTGMs) (e.g., DALL-E), self-supervised deep learning models trained on a huge dataset, have demonstrated the capacity for generating high-quality opendomain images from multi-modal input. Although they can even produce anthropomorphized versions of objects and animals, combine irrelevant concepts in reasonable ways, and give variation to any user-provided images, we witnessed such rapid technological advancement left many visual artists disoriented in leveraging LT-GMs more actively in their creative works. Our goal in this work is to understand how visual artists would adopt LTGMs to support their creative works. To this end, we conducted an interview study as well as a systematic literature review of 72 system/application papers for a thorough examination. A total of 28 visual artists covering 35 distinct visual art domains acknowledged LTGMs' versatile roles with high usability to support creative works in automating the creation process (i.e., automation), expanding their ideas (i.e., exploration), and facilitating or arbitrating in communication (i.e., mediation). We conclude by providing four design guidelines that future researchers can refer to in making intelligent user interfaces using LTGMs.

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Understanding User Perception of Automated News Generation System

Cited by 19 publications

References 32 publications

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

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

Understanding Journalists’ Workflows in News Curation

Large-scale Text-to-Image Generation Models for Visual Artists' Creative Works

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