arXiv subjects

Hongrui Jin

Publications and source records attributed to Hongrui Jin.

6 recordsLinked to original sources

arXiv2025

Vibe Coding, Interface Flattening

Large language models are reshaping programming by enabling 'vibe coding': the development of softwares through natural-language interaction with model-driven toolchains. This article argues that vibe coding is best understood as interface flattening, a reconfiguration in which previously distinct modalities (GUI, CLI, and API) appear to converge into a single conversational surface, even as the underlying chain of translation from intention to machinic effect lengthens and thickens. Drawing on Friedrich Kittler's materialist media theory and Alexander Galloway's account of interfaces as sites of protocol control, the paper situates programming as a historically localised interface arrangement rather than an essential relation to computation. Through a materialist reconstruction of the contemporary vibe-coding stack, it shows how remote compute infrastructures, latency and connectivity, structured outputs, function/tool calling, and interoperability standards such as the Model Context Protocol relocate control and meaning-making power to model and protocol providers. The apparent democratisation of technical capability therefore depends on new dependencies and new literacies. By foregrounding the tension between experiential flattening and infrastructural thickening, I demonstrate how LLM-mediated development redistributes symbolic labour/power, obscures responsibility, and privatises competencies previously dispersed across programming communities, contributing a critical lens on the political economy of AI-mediated human-computer interaction.

cs.HC↗

arXiv2025

Towards Non-linear Cultural Production and systems of machinic agency: in the case of TikTok value generation

The rise of TikTok has brought forth novel ways to create and consume media content, accelerated by technologies such as hyper-individualised algorithms and easy-to-use video production tools. Despite its popularity, scholars and politicians alike have raised many concerns on the legitimacy and ethics of TikTok regarding its services, and its collected data. However, much of these discussions take the premise of user-generated content for granted, attributing them to human expression without critically evaluating how the making of on-platform content production have changed. With a grounded theory approach, in conjunction with a platform-aware walkthrough that pays special attention to the material and immaterial premises of platform value generation, my findings suggest that the intensification of datafication have proliferated from consumption behaviours to the process of content production, whereas content production no longer solely produce media content. As platforms become the active recruiter, mobiliser and co-producer of media production, I argue that it is no longer feasible to distinguish human and machine contribution in the ways they are consumed to facilitate platform valorisation. I propose that the technical arrangements of TikTok, in relation to its users has fostered a non-linear mode of platform cultural production capable of generating economic value through a system of machinic agency that incorporates human and machines in an indistinguishable manner. As content, the premises of platform valorisation has become an inseparable effort of human-machines, I urge that the relationship between technology and humans be reassessed as a system of machinic agency that mutually shapes our mediated reality, rather than singular, differentiable actors that contribute to platforms.

cs.CY↗

arXiv2024

Re-calibrating methodologies in social media research: Challenge the visual, work with Speech

This article methodologically reflects on how social media scholars can effectively engage with speech-based data in their analyses. While contemporary media studies have embraced textual, visual, and relational data, the aural dimension remained comparatively under-explored. Building on the notion of secondary orality and rejection towards purely visual culture, the paper argues that considering voice and speech at scale enriches our understanding of multimodal digital content. The paper presents the TikTok Subtitles Toolkit that offers accessible speech processing readily compatible with existing workflows. In doing so, it opens new avenues for large-scale inquiries that blend quantitative insights with qualitative precision. Two illustrative cases highlight both opportunities and limitations of speech research: while genres like #storytime on TikTok benefit from the exploration of spoken narratives, nonverbal or music-driven content may not yield significant insights using speech data. The article encourages researchers to integrate aural exploration thoughtfully to complement existing methods, rather than replacing them. I conclude that the expansion of our methodological repertoire enables richer interpretations of platformised content, and our capacity to unpack digital cultures as they become increasingly multimodal.

cs.SI↗

arXiv2022

From Information to Affirmation: An Investigation on the Echo Chamber Effect from YouTube Comments under Technology Product Reviews

Social media may create echo chambers that reaffirm users' beliefs and opinions through repeated exposure of similar notions. Whilst the formation and effect of echo chambers have been intensively examined in thread-based platforms such as Twitter, Facebook and Reddit, we shift our focus on product review discussions on YouTube. This paper examines YouTube comments (n=2500) through a combined approach of quantitative content analysis (QCA) and sentiment analysis (SA) under selected selected YouTube videos (n=10). We conclude this paper by highlighting the formation of echo chamber effect in relation to comment argumentation and sentiments.

cs.SI↗

arXiv2022

Who Tracks Who? A Surveillance Capitalist Examination of Commercial Bluetooth Tracking Networks

Object and person tracking networks powered by Bluetooth and mobile devices have become increasingly popular for purposes of public safety and individual concerns. This essay examines popular commercial tracking networks and their campaigns from Apple, Samsung and Tile with reference to surveillance capitalism and digital privacy, discovering the hidden assets commodified through said networks, and their potential of turning users into unregulated digital labour while leaving individual privacy at risk.

cs.SI↗

arXiv2022

Seeing the Unseen: Errors and Bias in Visual Datasets

From face recognition in smartphones to automatic routing on self-driving cars, machine vision algorithms lie in the core of these features. These systems solve image based tasks by identifying and understanding objects, subsequently making decisions from these information. However, errors in datasets are usually induced or even magnified in algorithms, at times resulting in issues such as recognising black people as gorillas and misrepresenting ethnicities in search results. This paper tracks the errors in datasets and their impacts, revealing that a flawed dataset could be a result of limited categories, incomprehensive sourcing and poor classification.

cs.CV↗