AI agency vs. human agency: understanding human–AI interactions on TikTok and their implications for user engagement

Kang, Hyunjin; Lou, Chen

doi:10.1093/jcmc/zmac014

Cited by 57 publications

(40 citation statements)

References 66 publications

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“…At the same time, the emphasis on certain content and effects was likely influenced by the platform’s recommendation algorithms. This pattern points to the shared agency between content creators and algorithms (Kang and Lou, 2022).…”

Section: Resultsmentioning

confidence: 92%

“…lip-synching) and add their personal touches with text and images, they co-construct collective voices on the platform to encompass a broader range of topics in relation to environmental and climate action. This interaction represents a human-algorithm shared agency (Kang and Lou, 2022). Content production is driven by individual creators, as well as the underlying algorithms and engagement from other users to prioritize certain content types (Klug et al , 2021).…”

Section: Discussionmentioning

confidence: 99%

“…An alternative explanation for the data practices is that in reproducing trending content, content creators are participating in "performative activism"superficially partaking in trending social movements to increase online presence (Kalina, 2020). One might argue that the creators' agency is undermined as they try to tailor video content to the platform's algorithms.…”

Section: Proportion Of Video Features By Data Practicesmentioning

confidence: 99%

“…While self-presentation is key to participation in social media, algorithms driven by artificial intelligence (AI) alter the ways that people interact with, search for, and generate content (Lewis and Moorkens, 2020). On social media, there exists shared agency for algorithms and users to co-construct platform interactions (Kang and Lou, 2022). The TikTok’s algorithm selects videos to recommend to users based on automated analysis of video descriptions, hashtags, audio, and visual objects (Matsakis, 2020).…”

Section: Theoretical Frameworkmentioning

confidence: 99%

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Meaning making and relatedness: exploring critical data literacies on social media

Nguyen

Parameswaran

2023

ILS

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Purpose The goal of this study is to explore how content creators engage in critical data literacies on TikTok, a social media site that encourages the creation and dissemination of user-created, short-form videos. Critical data literacies encompass the ability to reason with, critique, control, and repurpose data for creative uses. Existing work on critical data literacies on social media has focused on understanding of personal data, critique of data use, and strategies to protect privacy. This work focuses on how TikTok content creators repurpose data to construct their own narratives. Design/methodology/approach Through hashtag search, the authors created a corpus of 410 TikTok videos focused on discussing environmental and climate action, and qualitatively coded the videos for data literacies practices and video features (audio, footage, background images) that may support these practices. Findings Content creators engaged in multiple practices to attach meanings to data and situate environmental and climate action discourse in lived experiences. While there were instances of no data practices, we found cases where creators compiled different data sources, situated data in personal and local contexts, and positioned their experiences as data points to supplement or counter other statistics. Creators further leveraged the platform’s technical features, particularly the ability to add original audio and background images, to add narratives to the collective discourse. Originality/value This study presents a unique focus on examining critical data literacies on social media. Findings highlight how content creators repurpose data and integrate personal experiences. They illustrate platform features to support data practices and inform the design of learning environments.

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Section: Resultsmentioning

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Proportion Of Video Features By Data Practicesmentioning

confidence: 99%

Section: Theoretical Frameworkmentioning

confidence: 99%

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Meaning making and relatedness: exploring critical data literacies on social media

Nguyen

Parameswaran

2023

ILS

View full text Add to dashboard Cite

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“…Agency involves people retaining control to make their own choices. A focus on human agency is particularly important in the context of AI-influenced communication (Floridi, 2023; Kang & Lou, 2022). A rich tradition of human-in-the-loop research explores the ways in which people and AI can interact to achieve better outcomes while ensuring there is always human input and control (Zanzotto, 2019).…”

Section: Discussion and Recommendationsmentioning

confidence: 99%

The Challenges and Opportunities of AI-Assisted Writing: Developing AI Literacy for the AI Age

Cardon,

Fleischmann,

Aritz

et al. 2023

Business and Professional Communication Quarterly

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Generative AI may significantly disrupt the teaching and practice of business communication. This study of 343 communication instructors revealed a collective view that AI-assisted writing will be widely adopted in the workplace and will require significant changes to instruction. Key perceived challenges include less critical thinking and authenticity in writing. Key perceived benefits include more efficiency and better idea generation in writing. Students will need to develop AI literacy—composed of application, authenticity, accountability, and agency—to succeed in the workplace. Recommendations are provided for instructors and administrators to ensure the benefits of AI-assisted writing can outweigh the challenges.

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I'm the same, I'm the same, I'm trying to change: Investigating the role of human information behavior in view change

McKay,

Makri,

Gutierrez‐Lopez

et al. 2024

Asso for Info Science & Tech

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Information is powerful; it can influence peoples' views and, in turn, their behavior. Much recent research and discussion on the role information plays in view change has focused on filter bubbles, echo chambers and misinformation and how they might influence what people think and how they act. However, no prior work has focused specifically on understanding the human information behavior (HIB) that drives and facilitates view change. We report findings from interviews with 18 people who recently changed views on issues they considered important. We found a tight symbiotic relationship between HIB and view change; passive information encountering sparked change, often spurring follow‐up active seeking and verification which progressed the change to a “point of no return,” supported making the change and reinforced the decision to change. When shared, information that contributed to the change sometimes sparked changes in others (as did expressing or debating the change), serving as an information encounter that perpetuated a cycle of HIB and view change. This understanding of the integral role of HIB in view change can inform policy and systems design to promote view change autonomy and a broader research agenda of understanding HIB to support democratic principles and values.

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AI agency vs. human agency: understanding human–AI interactions on TikTok and their implications for user engagement

Cited by 57 publications

References 66 publications

Meaning making and relatedness: exploring critical data literacies on social media

Meaning making and relatedness: exploring critical data literacies on social media

The Challenges and Opportunities of AI-Assisted Writing: Developing AI Literacy for the AI Age

I'm the same, I'm the same, I'm trying to change: Investigating the role of human information behavior in view change

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