Rolling-Type Collaborative Training for False Comment Identification: Enhancing Accuracy through Multi-Characteristic Fusion

Direct to consumer (D2C) business models, characterized by firms that design, source, and sell products directly to consumers bypassing traditional retail channels, are a relatively new phenomenon. One aspect we know little about is under what conditions consumers are willing to utilize a D2C channel, specifically, the degree to which the logistic service quality and information quality of user experience matter in choice of channel. Moreover, as these firms directly compete with Amazon’s delivery capability, we need to ask to what extent the so called “Amazon effect” impacts customer expectations. This study addresses these questions by conducting a scenario-based experiment which evaluates the effect of logistics service quality (LSQ) and online user experience (OUX) on customer satisfaction (CS), as moderated by price relative to Amazon (PRA). We find that LSQ has the largest positive effect on customer satisfaction, followed by OUX. Price moderates negatively (lessens) the effect of LSQ on customer satisfaction. Paradoxically, price also moderates the effect of OUX on CS, but counter-intuitively, strengthens it. The findings are reviewed in light of the so-called “Amazon effect”, the tendency of consumers to set their expectations for logistics at the levels of performance consistently provided by Amazon.com. Potential implications of the findings for the further evolution and growth of the D2C industry are presented.

The advent of the Internet and social media has revolutionized the way we communicate and access information, significantly changing daily interactions and our engagement with the world. However, this change has also brought about a worrying trend – the rapid spread of misinformation and misconceptions. These online platforms have become powerful of spreading misinformation, promoting mistrust and promoting harmful ideas.This paper explores the mechanisms behind the spread of false information and conspiracy theories on social media. It examines the inherent design elements of these platforms that make them highly vulnerable to the propagation of falsehoods. Algorithms that prioritize engagement and virality, combined with the ease of sharing information, create an environment where misinformation can spread rapidly without adequate editorial oversight.Furthermore, this paper highlights the challenges faced by social media platforms in dealing with misleading content. The sheer volume of posts, the delicate balance between free speech and content regulation, and the complexity of identifying lies before they spread widely present significant hurdles.Case studies on phenomena such as the anti-vaccination movement and the QAnon conspiracy theory illustrate the harmful effects of spreading misinformation online. Furthermore, the paper outlines the broader impact of misinformation on individuals, society, and democracy. It explores how the spread of misinformation erodes trust in institutions, promotes public confusion and potentially influences political processes and decisions, posing a threat to democratic structures

There are difficulties for small and medium enterprises (SMEs) to use the online platform, specifically e- commerce. Most of the problems are related to user experience because SME owners are very diverse in terms of digital literacy. For example, snack SMEs managed by elderly households are having difficulties operating e-commerce due to a lack of understanding of the user interface. There are many research used User-Centered Design (UCD) as a framework to develop the user experience for e-commerce. It is generally divided into several sections such as defining the scope, analyzing the problem including personas, and usability problems to find user problems and needs. This study uses concise UCD to model the user experience in SME e-commerce. The way to collect the SME’s information is combined in one process, namely the interview, starting with personal questions, habits, and about the business. This approach helps in determining difficult and complex decisions based on the user's point of view. The result shows the low-fidelity wireframe stage that could be the framework to develop the e-commerce user experience for SMEs.

Purpose Basic capturing of emotion on user experience of web applications and browsing is important in many ways. Quite often, online user experience is studied via tangible measures such as task completion time, surveys and comprehensive tests from which data attributes are generated. Prediction of users’ emotion and behaviour in some of these cases depends mostly on task completion time and number of clicks per given time interval. However, such approaches are generally subjective and rely heavily on distributional assumptions making the results prone to recording errors. This paper aims to propose a novel method – a window dynamic control system – that addresses the foregoing issues. Design/methodology/approach Primary data were obtained from laboratory experiments during which 44 volunteers had their synchronized physiological readings – skin conductance response, skin temperature, eye movement behaviour and users activity attributes taken by biosensors. The window-based dynamic control system (PHYCOB I) is integrated to the biosensor which collects secondary data attributes from these synchronized physiological readings and uses them for two purposes: for detection of both optimal emotional responses and users’ stress levels. The method’s novelty derives from its ability to integrate physiological readings and eye movement records to identify hidden correlates on a webpage. Findings The results from the analyses show that the control system detects basic emotions and outperforms other conventional models in terms of both accuracy and reliability, when subjected to model comparison – that is, the average recoverable natural structures for the three models with respect to accuracy and reliability are more consistent within the window-based control system environment than with the conventional methods. Research limitations/implications Graphical simulation and an example scenario are only provided for the control’s system design. Originality/value The novelty of the proposed model is its strained resistance to overfitting and its ability to automatically assess user emotion while dealing with specific web contents. The procedure can be used to predict which contents of webpages cause stress-induced emotions to users.

Several research studies have recently explored user experience, emphasizing its expanding importance. Due to technology's rapid advancement and new platforms, online user experience management and improvement are becoming more critical. User experience is crucial in the metaverse as it helps build a devoted user following. In the metaverse, gamification, sociability, and personalization can all improve user experience. However, only some studies have integrated and synthesized existing works using the user experience perspective to make metaverse user experience research more consolidated, relevant, and impactful. Content analysis was used on the selected articles to combine the outcomes into five themes: Metaverse as a New Transformational Technology and User Experience, Metaverse Interactivity and User Experience, Metaverse Immersiveness and User Experience, Integrative Flow in Metaverse and User Experience and Metaverse Environment Interface and User Experience. Researchers, practitioners, and policymakers will find this study helpful for those interested in user experience and the metaverse. In order to guide future empirical research in the field, the study concludes with a framework consolidating the findings. Keywords: Metaverse; User Experience; Immersiveness; Environment interface; Transformational technology; Interactivity

Escalating proliferation of inorganic accounts, commonly known as bots, within the digital ecosystem represents an ongoing and multifaceted challenge to online security, trustworthiness, and user experience. These bots, often employed for the dissemination of malicious propaganda and manipulation of public opinion, wield significant influence in social media spheres with far-reaching implications for electoral processes, political campaigns and international conflicts. Swift and accurate identification of inorganic accounts is of paramount importance in mitigating their detrimental effects. This research paper focuses on the identification of such accounts and explores various effective methods for their detection through machine learning techniques. In response to the pervasive presence of bots in the contemporary digital landscape, this study extracts temporal and semantic features from tweet behaviors and proposes a bot detection algorithm utilizing fundamental machine learning approaches, including Support Vector Machines (SVM) and k-means clustering. Furthermore, the research ranks the importance of these extracted features for each detection technique and also provides uncertainty quantification using a distribution free method, called the conformal prediction, thereby contributing to the development of effective strategies for combating the prevalence of inorganic accounts in social media platforms.

Cross-modal fine-grained alignment and fusion network for multimodal aspect-based sentiment analysis

Selecting a birth control method is a complex healthcare decision. While birth control methods provide important benefits, they can also cause unpredictable side effects and be stigmatized, leading many people to seek additional information online, where they can privately find reviews, advice, hypotheses, and experiences of other birth control users. However, the relationships between their healthcare concerns, sensemaking activities, and online settings are not well understood. We gather texts about birth control shared on Twitter and Reddit—popular communities with different affordances, moderation, and audiences—to study where and how birth control is discussed online. Using a combination of topic modeling and hand annotation, we identify and characterize the dominant sensemaking practices across these platforms, and we create lexica to draw comparisons across birth control methods and side effects. We use these to measure variations from survey reports of side effect experiences, highlighting topics that social media users discuss more than expected online. Our findings characterize how online platforms are used to make sense of difficult healthcare choices, including analyzing risks, calculating timing and dosages, hypothesizing about causes of side effects, and storytelling about painful experiences. We contribute both to understanding unmet needs of birth control users and to exploring context-specific patterns in social media discussions.

This study investigates the influence of customer experience on brand loyalty through perceived value by comparing Apple and Samsung users. Data collection was conducted via online platforms through self-administered questionnaire. A total of 252 Apple and 265 Samsung brand respondents were subsequently used for data analysis. Partial least square structural equation modelling was used to analyse the data. The results revealed that perceived value was determined by sense experience and relate experience for Apple users while perceived value was determined by feel experience, act experience and relate experience for Samsung users. Perceived value was the predictor of brand loyalty for both Apple and Samsung users. This study contributes to the literature of customer loyalty by confirming the impact of five customer experience dimensions on the perceived value. Further suggestions and recommendations were made regarding critical customer experience that contributed to perceived value and subsequently to brand loyalty.KeywordsSense experienceFeel experienceThink experienceAct experienceRelate experienceBrand loyalty

For sentiment analysis of user opinions on online platforms such as X (formerly known as Twitter), dictionary-based approaches and machine learning methods are generally used. Recent studies emphasize that hybridizing these approaches improves model performance. In this study, we propose a hybrid classification model for sentiment analysis of texts on food ordering. In addition, we suggest a feature selection method based on aggregating words for the high-dimensionality problem of text classification. The main problems in that domain are low number of words with distinctive features, complexity of interpretation of food ordering field, domain dependency of text classification. The use of classification algorithms and a domain lexicon-based approach will contribute to overcoming these difficulties. For this purpose, two domain-specific lexicons are developed using data from online users' opinions, one for sentiment analysis and the other for product-service systems classification, referred to as basic lexicons. Basic lexicons have been transformed into new lexicons with fewer words, referred to as boosted lexicons, by grouping the words in basic lexicons and representing the groups with a single word in boosted lexicons. 144 models of combinations of six classification algorithms, three term weighting methods, and the lexicons are created in a hybrid approach for sentiment analysis. The study used two datasets of 21 039 and 14 389 tweets obtained from X between January 1 and December 31, 2020. The models were trained, tested on the first dataset, and the best models were selected. The second dataset is analyzed with the selected models, we present proposals for the industry.

AimRide-sharing is an innovative on-demand transport service that aims to promote sustainable transport, reduce car utilization, increase vehicle occupancy and public transport ridership. By reviewing ride-sharing studies around the world, this paper aims to map major aspects of ride-sharing, including online platforms, user factors and barriers that affect ride-sharing services, and extract useful insights regarding their successful implementation.MethodA systematic literature review is conducted on scientific publications in English language. Articles are eligible if they report a study on user factors affecting ride-sharing use and/or barriers preventing ride-sharing implementation; ride-sharing online platforms in these articles are also recorded and are further explored through their official websites. A database is built that organizes articles per author, year and location, summarizes online platform attributes, and groups user factors associated with the likelihood to ride-share.FindingsThe review shows that the term “ride-sharing” is used in the literature for both profit and non-profit ride-sharing services. In total, twenty-nine ride-sharing online platforms are recorded and analyzed according to specific characteristics. Sixteen user factors related to the likelihood to ride-share are recorded and grouped into sociodemographic, location and system factors. While location and system factors are found to follow a pattern among studies, mixed findings are recorded on the relationship between sociodemographic factors and ride-sharing. Factors that may hinder the development of ride-sharing systems are grouped into economic, technological, business, behavioral and regulatory barriers.ConclusionOpportunities exist to improve the quality of existing ride-sharing services and plan successful new ones. Future research efforts should focus towards studying ride-sharing users' trip purpose (i.e., work, university, shopping, etc.), investigating factors associated to ride-sharing before and after implementation of the service, and perform cross-case studies between cities and countries of the same continent to compare findings.

This study investigates the diverse formats and categories of publications available for digital translations of the Holy Quran. Given the Quran's significance in Islam, its translation into various languages is vital for non-Arabic-speaking Muslims and scholars worldwide. The research delves into traditional printed translations, such as pocket sized editions, comprehensive annotated versions, and parallel text formats, comparing them to digital translations available in e-books, mobile applications, and online platforms. The study also examines the unique challenges posed by translating the Quran, including the preservation of its linguistic and theological nuances in different languages and mediums. By analyzing these aspects, the paper aims to shed light on the evolving methods of disseminating Quranic translations, emphasizing the balance between accessibility and the faithful representation of the sacred text. In this modern era, we cannot deny that the development of electronics is rapidly increase and has an impact on social life, even in this modern era we can get everything easily, including the model and type of digital Qur'an translation publications in the 5.0 era, this phenomenon raises several questions regarding the validity of the translation in the digital Qur'an model, is it in accordance with the provisions of the Indonesian LPMQ (Lajnah Pentashihan Mushaf al-Qur’an)? How does the birth of the digital Qur'an form of expression affect the study of the Qur'an in Indonesia? This article argues that these Qur'ans express interactivity in religion. The study of the model and type of Qur'an translation publications in this digital era aims to test the validity of the Qur'an translation.

With the growth and widespread popularity of artificial intelligence (AI) and online media, online platforms have accumulated massive and diverse resources and data. Currently, multimodal data in social networks has become the mainstream form, and compared to traditional unimodal data, it contains richer and more comprehensive emotional features. Sentiment analysis (SA), also called viewpoint mining, aims to recognize and understand users' emotional tendencies, attitude evaluations, and subjective views expressed towards specific entities through computational means. It is an important technical means to improve the quality of network services and user experience. Conducting in-depth research on social platform data can not only optimize platform services, but also assist enterprises in precise marketing, conducting public opinion monitoring, and achieving personalized recommendations.

With the rapid advancement of technology, Artificial Intelligence (AI) has become a transformative force in online commerce, reshaping the relationship between consumers and digital platforms. This study investigates the impact of AI on online sales performance, emphasizing consumer and business perceptions in Kosovo. A quantitative methodology was applied, using a structured Likert-scale questionnaire distributed to 200 randomly selected respondents, including both active online shoppers and digital business representatives. Statistical analyses revealed a significant positive relationship between the use of AI tools and the effectiveness of online sales. Key findings indicate that AI contributes substantially to offer personalization, customer interaction, and loyalty development. Users who experience higher levels of personalization report greater satisfaction and trust in online platforms. The study concludes that AI has a direct and measurable impact on improving online sales performance by enhancing both user experience and business strategy. These insights provide a theoretical and practical foundation for the further development of digital platforms seeking to maximize efficiency and customer engagement.

Robotic era is near, has never been before. In this new era, robotic education must have quality and accessibility for everyone. In this study, a disruptive technology for future of distance education based on virtual robotic laboratory (U-Plat) is presented. The created robotics education platform allows users to access a virtual robotics laboratory and training programs using internet interfaces. This environment allows them to control simulated robots, analyze and evaluate the results of experiments performed. U-Plat gives researchers, students and people working in the field of robotics the chance to learn Robot Operating System (ROS) and increase their knowledge with robotics training from beginner to advanced level. Also, U-Plat gives researchers opportunity to try their codes in online platform. Compared to traditional learning methods, U-Plat can be seen as an easier and more effective learning/teaching platform for ROS exercises, as it is an open source system. The user experience has been tried to be provided in all training tools with a very simplified and easily understandable interface. All designed training tools are provided through a web interface supported by advanced cloud systems and Docker container infrastructure. As a result, U-Plat is a novel robotic learning platform that can lead future laboratory research and distance robotic education.