User Attention Research Articles

Digital transformation’s impact on media concentration and news diversity: a network analysis of cross-platform news usage in Germany

ABSTRACT The digital transformation of news media has led to significant changes in their distribution and use. Digital platforms as well as media and technology companies play a critical role in the distribution and consumption of information, which has a significant impact on media market concentration and diversity. This study applies market concentration measurements and a network analysis to investigate the static market structure as well as dynamics of cross-platform news media use in Germany. It assesses competition intensity by considering absolute market shares of media companies and brands as well as usage dynamics between different offerings. Our findings reveal that larger media companies tend to absorb and retain user attention, which decreases diversity. Smaller media companies contribute to a broader spectrum of opinions by directing and forwarding users’ attention to other media offerings. The study highlights the importance of considering not only the size but also the degree of centrality and permeability of news offers and how that impacts on core cornerstones of media market diversity. As a pilot for a longitudinal study, it provides a model that can regularly generate empirical evidence for monitoring media concentration. This provides a systematic, empirical basis for media economic and media policy decision-making.

ArtStory Beats: Highlighting interactions between visual arts and music with storytelling

In this article, we present the use-case of ArtStory Beats, a mobile application that aims to reflect the creative dialogue between visual arts and music, offering stories about the vibrant interactions that take place within the artistic communities over time. The app use is quite simple: it scans physical copies of visual artworks and provides audio-enhanced stories. However, the audio is not intended to be background or supplementary informative material. It is a music song that somehow relates to the visual artwork, and the stories tell you how. First, we describe our content creation process along with the design of the mobile application. Then we present the results from a qualitative user study with 15 participants, investigating the role and effects of music over several dimensions: overall experience, user satisfaction, and most notably, cultural engagement. The outcomes of the study reveal the potential of the approach to foster curiosity, capture the user's attention and support a deeper, reflective connection of the user with the combined artworks. Finally, we discuss the potential use of ArtStory Beats in different contexts, as a remote but also onsite experience, elaborating on related challenges and future directions.

Four-class ASME BCI: investigation of the feasibility and comparison of two strategies for multiclassing

IntroductionThe ASME (stands for Auditory Stream segregation Multiclass ERP) paradigm is proposed and used for an auditory brain-computer interface (BCI). In this paradigm, a sequence of sounds that are perceived as multiple auditory streams are presented simultaneously, and each stream is an oddball sequence. The users are requested to focus selectively on deviant stimuli in one of the streams, and the target of the user attention is detected by decoding event-related potentials (ERPs). To achieve multiclass ASME BCI, the number of streams must be increased. However, increasing the number of streams is not easy because of a person's limited audible frequency range. One method to achieve multiclass ASME with a limited number of streams is to increase the target stimuli in a single stream.MethodsTwo approaches for the ASME paradigm, ASME-4stream (four streams with a single target stimulus in each stream) and ASME-2stream (two streams with two target stimuli in each stream) were investigated. Fifteen healthy subjects with no neurological disorders participated in this study. An electroencephalogram was acquired, and ERPs were analyzed. The binary classification and BCI simulation (detecting the target class of the trial out of four) were conducted with the help of linear discriminant analysis, and its performance was evaluated offline. Its usability and workload were also evaluated using a questionnaire.ResultsDiscriminative ERPs were elicited in both paradigms. The average accuracies of the BCI simulations were 0.83 (ASME-4stream) and 0.86 (ASME-2stream). In the ASME-2stream paradigm, the latency and the amplitude of P300 were shorter and larger, the average binary classification accuracy was higher, and the average weighted workload was smaller.DiscussionBoth four-class ASME paradigms achieved a sufficiently high accuracy (over 80%). The shorter latency and larger amplitude of P300 and the smaller workload indicated that subjects could perform the task confidently and had high usability in ASME-2stream compared to ASME-4stream paradigm. A paradigm with multiple target stimuli in a single stream could create a multiclass ASME BCI with limited streams while maintaining task difficulty. These findings expand the potential for an ASME BCI multiclass extension, offering practical auditory BCI choices for users.

Behind the clicks: Can Amazon allocate user attention as it pleases?

We investigate Amazon's ability to direct user clicks to more visually prominent search results, even as quality declines with the increasing prevalence of sponsored advertising products. We analyze product results from over 2,000 Amazon Marketplace search queries to estimate how a top three most clicked product's features (price and quality) and screen placement influence a user's clicks. Our econometric results show that the position of a product search result (“position bias”), adjusted for its relative prominence on the screen, strongly shapes whether a user clicks on it. Within the top five search results, where typically four are advertisements, users exhibit decreased sensitivity to a product's relevance or pricing. This allows Amazon's sponsored ads to leverage product prominence as a mechanism for rent extraction from users and producers. Regulatory frameworks might limit platforms from exploiting consumers' satisficing behaviour online, including via moderating excessive advertising in algorithmic search results.

An Improved and Effective Approach for User Attention in the IoT Through the Hybrid Lightweight Security Framework (HLSF) with Data Mining

An Improved and Effective Approach for User Attention in the IoT Through the Hybrid Lightweight Security Framework (HLSF) with Data Mining

Research on Traffic Optimization and Growth Strategy under Precision Marketing -- A Case Study of Social Media Xiaohongshu

With the development and progress of social media, digital marketing has ushered in a new era. With over 200 million monthly active users, Xiaohongshu has shown brands the potential of precision marketing and traffic optimization on social media. Major brands have joined Xiaohongshu to improve their communication matrix. Therefore, this article adopts the literature analysis method to analyze the marketing status and reasons and problems for the existence of traffic on the Xiaohongshu platform, and explores strategies for optimizing traffic on the Xiaohongshu platform based on the 4C template. In the era of digital marketing, the Xiaohongshu brand needs to reposition itself accurately, promote personalized marketing, use technological methods to create creative content, attract user attention, and use diverse interactive methods to establish emotional resonance with users. In order to improve the drawbacks of Xiaohongshu’s traffic growth, enhance its traffic utilization rate, improve Xiaohongshu’s user reputation, and provide more accurate theoretical basis and development direction for social media precision marketing, ushering in long-term and healthy development.

Good Practices for Designing a UI/UX Motorcycle Display: A Systematic Literature Review

Within the scope of the Mobilizing Agenda for the Development of Intelligent Green Mobility Products and Systems (A-MoVeR), specifically in the second PPS2 defined the presentation of a “new electric motorcycle, with high autonomy, aimed at promoting comfortable, efficient and green urban mobility"". In this context, we intend to develop user interfaces (UI) for an electric motorcycle that meet the end-user’s expectations by promoting optimal user experience and security. To achieve this goal, this paper provides a preliminary literature analysis, with a compilation of literature related to major aspects for developing an optimized User Interface (UI) and consequently increasing User eXperience (UX), specifying accessibility, adaptability, appeal, and conciseness of motorcycle interfaces in an attempt to determine its constructive qualities. Therefore, it was analyzed studies regarding filtering of displayed information; the controlling of a user’s focus and emotions through means of efficient visual representations; the differences in various types of input methods regarding user attention; and, the relevance of dynamic UI as a solution to a variety of problems related to UI/UX design. Therefore, a systematic literature review was performed, which resulted in the finding of various advantageous practices and ideas that are relevant to the design of a motorcycle’s UI/UX.

Virtual reality-enabled high-performance emotion estimation with the most significant channel pairs

Human-computer interface (HCI) and electroencephalogram (EEG) signals are widely used in user experience (UX) interface designs to provide immersive interactions with the user. In the context of UX, EEG signals can be used within a metaverse system to assess user engagement, attention, emotional responses, or mental workload. By analyzing EEG signals, system designers can tailor the virtual environment, content, or interactions in real time to optimize UX, improve immersion, and personalize interactions. However, in this case, in addition to the signals' processing cost and classification accuracy, cybersickness in Virtual Reality (VR) systems needs to be resolved. At this point, channel selection methods can perform better for HCI and UX applications by reducing noisy and redundant information in generally unrelated EEG channels. For this purpose, a new method for EEG channel selection based on phase-locking value (PLV) analysis is proposed. We hypothesized that there are interactions between EEG channels in terms of PLV in repeated tasks in different trials of the emotion estimation experiment. Subsequently, frequency-based features were extracted. The features were classified by dividing them into bags using the Multiple-Instance Learning (MIL) variant. This study provides higher classification performance using fewer EEG channels for emotion prediction. The performance rate obtained in binary classification with the Random Forests (RF) algorithm is at a promising level of 99%. The proposed method achieved an accuracy of 99.38% for valence using all channels on the new dataset (VREMO) and 98.13% with channel selection. The benchmark dataset (DEAP) achieved accuracies of 98.16% using all channels and 98.13% with selected channels.

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Digitalization has significantly influenced the challenges facing the media industry. The very process of media transformations "is no longer a technical need, but is already a social reflection and need, a need for communication, a need for solving political and technological problems in the society of this time" (Bebić & Volarević, 2016). Therefore, the attention of users is increasingly difficult to capture, thanks to the diversity of media publications, and in this sense traditional media often fail to respond to specific audience demands (Trattner, et al., 2022).Today, users are exposed to a large amount of information on a daily basis, their content is increasing, so media content cannot be imposed on users, but they choose only what they are interested in for consumption. However, traditional electronic media have understood the importance of the new multidimensional environment, therefore they are using a multiplatform through the Internet that use digital technology both to place the content and to personalize the offers against the user (Ćitić, 2020).

MRAM: Multi-scale Regional Attribute-weighting via Meta-learning for Personalized Image Aesthetics Assessment

Image aesthetics assessment (IAA) is inherently subjective, with generic image aesthetics assessment (GIAA) often overlooking individual user preferences. Personalized aesthetic assessment (PIAA) addresses this by accounting for user-specific inconsistencies. However, the limited annotated data from individual users and the scarcity of user-jointly annotated image data pose challenges. To cope with these problems, we propose a multi-scale regional attribute-weighting via meta-learning for PIAA, namely MRAM, which aims to simulate how the brain computes aesthetic values from visual stimuli. The human brain is to predict the subjective aesthetic value of the individual to the image by combining the features of the visual scene segmentation through the weighting method. MRAM introduces a multi-scale attention module to capture visual scene regions segmented at different scales, adjusting feature selection based on user attention. Additionally, a regional attribute-weighting module (RAM) is proposed to weigh these features using aesthetic attribute representations. In the meta-training phase, MRAM learns aesthetic prior knowledge from a diverse set of PIAA tasks. Transitioning to the personalization phase, MRAM is fine-tuned with only a small number of specific user samples, enabling it to rapidly adapt to the unique aesthetic preferences of individuals. Experimental results demonstrate the superior performance of the proposed MRAM compared to state-of-the-art PIAA methods.

Adaptive QP algorithm for depth range prediction and encoding output in virtual reality video encoding process.

In order to reduce the encoding complexity and stream size, improve the encoding performance and further improve the compression performance, the depth prediction partition encoding is studied in this paper. In terms of pattern selection strategy, optimization analysis is carried out based on fast strategic decision-making methods to ensure the comprehensiveness of data processing. In the design of adaptive strategies, different adaptive quantization parameter adjustment strategies are adopted for the equatorial and polar regions by considering the different levels of user attention in 360 degree virtual reality videos. The purpose is to achieve the optimal balance between distortion and stream size, thereby managing the output stream size while maintaining video quality. The results showed that this strategy achieved a maximum reduction of 2.92% in bit rate and an average reduction of 1.76%. The average coding time could be saved by 39.28%, and the average reconstruction quality was 0.043, with almost no quality loss detected by the audience. At the same time, the model demonstrated excellent performance in sequences of 4K, 6K, and 8K. The proposed deep partitioning adaptive strategy has significant improvements in video encoding quality and efficiency, which can improve encoding efficiency while ensuring video quality.

Improving Online Culinary Experience with Attractive F&B Website Design

In the era of digital technology development, this research explains the process of designing websites using Html, Css, Javascript, and APIs. An attractive website has the potential to enhance the reputation of an individual or business and attract the attention of users. The waterfall development method was chosen to build the website. Performance evaluation is done using Gitsofspeed as a tool for measuring site speed and efficiency. The hope is to successfully address existing sales issues and increase positive impact, while stimulating consumer desire. Using technological advancements, an attractive website is able to provide information about food and beverages in a way that appeals to users

Exploring linguistic features and user engagement in Chinese online mental health counseling

With the popularity of online mental health platforms, more individuals are seeking help and receiving social support by openly discussing their problems. Therefore, it's crucial to gain a deeper understanding of which problem disclosures and social support on these platforms can attract more user attention and engagement. Previous research has primarily focused on social media forums. Our work concentrates on the professional mental health platform, intending to understand the linguistic features present in posts that promote user engagement and interaction. We employ text mining and deep learning techniques to analyze posts consisting of 22,250 questions from help-seekers and 78,328 answers providing social support extracted from the Chinese online mental health counseling platform. Initially, we analyze the high-frequency words and topics of the questions and answers to gain insights into the primary focal points and the range of topics covered in these posts. The results indicate that work-related issues are the most concerning and troublesome for help-seekers, and the topics that users follow are approximately 8 types, including growth, family, in-love, marriage, emotions, human-relations, behavioral-therapy and career. Subsequently, we analyze the language usage in question-and-answer posts with different engagement from three aspects: vocabulary categories, linguistic style matching, and language modeling, aiming to identify which linguistic features can attract more user attention and engagement. The results reveal that high-engagement answer posts exhibit a higher degree of linguistic style matching with the corresponding questions, and the use of vocabulary categories also influences the attention and engagement of the posts. By exploring the linguistic features and patterns displayed in posts with different levels of engagement on the professional online mental health platform, this study offers deep insights into user behavior and the factors that impact counseling effectiveness on the platform and provides valuable knowledge for understanding effective user interactions and engagement.

Adaptive Knowledge Contrastive Learning with Dynamic Attention for Recommender Systems

Knowledge graphs equipped with graph network networks (GNNs) have led to a successful step forward in alleviating cold start problems in recommender systems. However, the performance highly depends on precious high-quality knowledge graphs and supervised labels. This paper argues that existing knowledge-graph-based recommendation methods still suffer from insufficiently exploiting sparse information and the mismatch between personalized interests and general knowledge. This paper proposes a model named Adaptive Knowledge Contrastive Learning with Dynamic Attention (AKCL-DA) to address the above challenges. Specifically, instead of building contrastive views by randomly discarding information, in this study, an adaptive data augmentation method was designed to leverage sparse information effectively. Furthermore, a personalized dynamic attention network was proposed to capture knowledge-aware personalized behaviors by dynamically adjusting user attention, therefore alleviating the mismatch between personalized behavior and general knowledge. Extensive experiments on Yelp2018, LastFM, and MovieLens datasets show that AKCL-DA achieves a strong performance, improving the NDCG by 4.82%, 13.66%, and 4.41% compared to state-of-the-art models, respectively.

Can Stylized Products Generated by AI Better Attract User Attention? Using Eye-Tracking Technology for Research

The emergence of AIGC has significantly improved design efficiency, enriched creativity, and promoted innovation in the design industry. However, whether the content generated from its own database meets the preferences of target users still needs to be determined through further testing. This study investigates the appeal of AI-generated stylized products to users, utilizing 12 images as stimuli in conjunction with eye-tracking technology. The stimulus is composed of top-selling gender-based stylized Bluetooth earphones from the Taobao shopping platform and the gender-based stylized earphones generated by the AIGC software GPT4.0, categorized into three experimental groups. An eye-tracking experiment was conducted in which 44 participants (22 males and 22 females, mean age = 21.75, SD = 2.45, range 18–27 years) observed three stimuli groups. The eye movements of the participants were measured while viewing product images. The results indicated that variations in stimuli category and gender caused differences in fixation durations and counts. When presenting a mix of the two types of earphones, the AIGC-generated earphones and earphones from the Taobao shopping platform, the two gender groups both showed a significant effect in fixation duration with F (2, 284) = 3.942, p = 0.020 < 0.05, and η = 0.164 for the female group and F (2, 302) = 8.824, p < 0.001, and η = 0.235 for the male group. They all had a longer fixation duration for the AI-generated earphones. When presenting exclusively the two types of AI-generated gender-based stylized earphones, there was also a significant effect in fixation duration with F (2, 579) = 4.866, p = 0.008 < 0.05, and η = 0.129. The earphones generated for females had a longer fixation duration. Analyzing this dataset from a gender perspective, there was no significant effect when the male participants observed the earphones, with F (2, 304) = 1.312 and p = 0.271, but there was a significant difference in fixation duration when the female participants observed the earphones (F (2, 272) = 4.666, p = 0.010 < 0.05, and η = 0.182). The female participants had a longer fixation duration towards the earphones that the AI generated for females.

Digital Reconstruction: A Critical Examination of the History and Adaptation of Ku Klux Klan Websites.

In response to the data revolution, academic research and media attention have increasingly focused on the technological adaptation and innovation displayed by the far right. The greatest attention is paid to social media and how groups and organizations are utilizing technological advancement and growth in virtual networks to increase recruitment and advance radicalization on a global scale. As with most social and political endeavors, certain technologies are in vogue and thus draw the attention of users and regulators and service providers. This creates a technological blind spot within which extremist groups frequently operate older and less well regarded technologies without the oversight that one might expect. This article examines the less well-studied traditional and official websites of the Ku Klux Klan, the most established and iconic of American far-right organizations. By incorporating non-participant observation of online spaces and thematic analysis, this research analyzes the evolution of 26 websites, from their emergence in the early 1990s to the present day. We examine the ways in which traditional printed communications and other ephemera have progressed with advances in technology, focusing on the following central elements of Klan political activism and community formation: Klan identity, organizational history, aims and objectives; technology and outreach, including online merchandise and event organization; and the constructions of whiteness and racism. The results add value and insight to comparable work by offering a unique historical insight into the ways in which the Klan have developed and made use of Web 1.0, Web 2.0, and Web3 technologies.

A Survey on Progressive Visualization.

Currently, growing data sources and long-running algorithms impede user attention and interaction with visual analytics applications. Progressive visualization (PV) and visual analytics (PVA) alleviate this problem by allowing immediate feedback and interaction with large datasets and complex computations, avoiding waiting for complete results by using partial results improving with time. Yet, creating a progressive visualization requires more effort than a regular visualization but also opens up new possibilities, such as steering the computations towards more relevant parts of the data, thus saving computational resources. However, there is currently no comprehensive overview of the design space for progressive visualization systems. We surveyed the related work of PV and derived a new taxonomy for progressive visualizations by systematically categorizing all PV publications that included visualizations with progressive features. Progressive visualizations can be categorized by well-known visualization taxonomies, but we also found that progressive visualizations can be distinguished by the way they manage their data processing, data domain, and visual update. Furthermore, we identified key properties such as uncertainty, steering, visual stability, and real-time processing that are significantly different with progressive applications. We also collected evaluation methodologies reported by the publications and conclude with statistical findings, research gaps, and open challenges.

Navigating the Unique Challenges of Automated Insulin Delivery Systems to Facilitate Effective Uptake, Onboarding, and Continued Use.

Advances in diabetes technologies have enabled automated insulin delivery (AID) systems, which have demonstrated benefits to glycemia, psychosocial outcomes, and quality of life for people with type 1 diabetes (T1D). Despite the many demonstrated benefits, AID systems come with their own unique challenges: continued user attention and effort, barriers to equitable access, personal costs vs benefits, and integration of the system into daily life. The purpose of this narrative review is to identify challenges and opportunities for supporting uptake and onboarding of AID systems to ultimately support sustained AID use. Setting realistic expectations, providing comprehensive training, developing willingness to adopt new treatments and workflows, upskilling of diabetes team members, and increasing flexibility of care to tailor care to individual needs, preferences, lifestyle, and personal goals will be most effective in facilitating effective, widespread, person-centered implementation of AID systems.

VAEnvGen: A Real-Time Virtual Agent Environment Generation System Based on Large Language Models

Environment plays an important role in non-verbal communication for human-virtual agent interaction. Existing research explores the influence of an agent’s appearance and attributes to enhance human-virtual agent communication. However, there is no common practice for dynamically adjusting the surrounding environments of the virtual agent. In this paper, we introduce a real-time virtual agent environment generation system (VAEnvGen), which contributes to the field by enhancing users’ content perception and improving task performance through dynamic environment adjustment. The system dynamically analyzes both the appropriate communication environment and filters the key information according to the current context. Leveraging Large Language Models, it generates a pseudo-3D background space to create an engaging atmosphere and a dynamic foreground content space for vivid key information display, thereby significantly enhancing content perception. For widespread adoption and flexibility, VAEnvGen is developed as a web application. We further evaluate the impact of VAEnvGen on content perception, user attention, and subjective satisfaction through a mixed-design user study with 50 participants. Quantitative and qualitative results reveal significant improvements in content perception, task completion time, and user satisfaction when using VAEnvGen. The system effectively redistributes user attention from subtitles and the virtual agent itself to the dynamically generated background and key foreground information, leading to a more immersive and less fatiguing user experience.

A Quality of Experience and Visual Attention Evaluation for 360° Videos with Non-spatial and Spatial Audio

This article presents the results of an empirical study that aimed to investigate the influence of various types of audio (spatial and non-spatial) on the user quality of experience (QoE) of and visual attention in 360° videos. The study compared the head pose, eye gaze, pupil dilations, heart rate, and subjective responses of 73 users who watched ten 360° videos with different sound configurations. The configurations evaluated were no sound; non-spatial (stereo) audio; and two spatial sound conditions (first- and third-order ambisonics). The videos covered various categories and presented both indoor and outdoor scenarios. The subjective responses were analyzed using an ANOVA (Analysis of Variance) to assess mean differences between sound conditions. Data visualization was also employed to enhance the interpretability of the results. The findings reveal diverse viewing patterns, physiological responses, and subjective experiences among users watching 360° videos with different sound conditions. Spatial audio, in particular third-order ambisonics, garnered heightened attention. This is evident in increased pupil dilation and heart rate. Furthermore, the presence of spatial audio led to more diverse head poses when sound sources were distributed across the scene. These findings have important implications for the development of effective techniques for optimizing processing, encoding, distributing, and rendering content in virtual reality (VR) and 360° videos with spatialized audio. These insights are also relevant in the creative realms of content design and enhancement. They provide valuable guidance on how spatial audio influences user attention, physiological responses, and overall subjective experiences. Understanding these dynamics can assist content creators and designers in crafting immersive experiences that leverage spatialized audio to captivate users, enhance engagement, and optimize the overall quality of VR and 360° video content. The dataset, scripts used for data collection, ffmpeg commands used for processing the videos, and the subjective questionnaire and its statistical analysis are publicly available.