Online Users Research Articles

Community Notes vs. Related Articles: Assessing Real-World Integrated Counter-Rumor Features in Response to Different Rumor Types on Social Media

The pervasive reach of the Internet has revolutionized information access and transmission, which has contributed to the widespread dissemination of rumors on social media. This study explored the impact of real-world integrated counter-rumor features, specifically community notes (which provide context and additional information from the online community) and related articles (which link to verified news sources that address the rumor), on online users’ intentions to believe and spread rumor tweets on social media. Additionally, we investigated how these features mitigate online users’ intentions to believe and spread different types of rumor messages, including wish and dread rumors. After conducting an experimental study with 201 online users on social media, we found that the presence of integrated counter-rumor features in rumor tweets can reduce online users’ intentions to believe and spread rumors, regardless of the specific feature used. While we observed no significant differences between the effects of community notes and related articles on overall online users’ intentions, a nuanced pattern emerged when we considered wish and dread rumors. Specifically, community notes proved more effective at reducing online users’ intentions to believe and spread wish-related rumors due to the diverse perspectives and opinions within the online community. By contrast, related articles were found to have greater efficacy at mitigating online users’ intentions to believe and spread dread rumors, as they can provide more concrete information to alleviate any associated fear or anxiety. Our findings contribute theoretical and practical insights for effectively countering the spread of rumor tweets on social media platforms.

Online Social Influence and Negative Emotions toward Snow Sports Brands: Moderation and Mediation Effects

This article draws on the stimulus–organism–response (SOR) model to understand the role of negative emotions in the anti-brand behaviors of online users who consume snow sports brands. To this end, both the online social influence and the mediating effect of symbolic incongruence (stimulus) on the generation of negative emotions (anger, stress, frustration, fear, boredom and sadness) (organism), and how these influence the formation of negative customer brand engagement (nCBE) (response), are analyzed. The study also analyses the moderating effects of “level of expertise”, this makes it possible to capture differences in behaviors based on the profile of the users in each of the proposed relationships. Questionnaire responses of 400 ski and snowboard users over 18 years of age were analyzed using a quantitative methodology. The results obtained have important theoretical and practical implications, since they confirm that online social influences have both a direct and indirect (mediating) effect on negative emotions, which positively affects the nCBE of online users of snow sports brands. Significant differences in behavior based on experience level (moderation effect) were also found. The study proposes useful practical recommendations applicable in online environments that the extreme sports industry could use to neutralize/avoid highly detrimental consequences.

Infinite Potential of AI Chatbots: Enhancing User Experiences and Driving Business Transformation in E-commerce: Case of Palestinian E-Commerce

In the much-evolving digital age landscape, it is difficult to overlook artificial intelligence's enormous role in people. This study entails an empirical examination of the impact of AI-powered chatbots on improving customer experience, customer satisfaction, and potential benefits in the digital era. This study was handled by the use of quantitative data collection methods through an online survey from 221 online users of AI-powered chatbots in Palestine. The collected data was then analyzed using descriptive statistics and structural equation modeling techniques. According to the results, it was shown that service quality significantly influenced the intention to use chatbots by customers or users thus indirectly influencing the net benefits enjoyed by users in the digital era. Similarly, the service quality provided by AI-powered chatbots was found to significantly influence the intention to use them, thus indirectly influencing the net benefits of AI-powered chatbots. Additionally, service quality was found to influence user satisfaction with chatbots, which indirectly influenced the net benefits enjoyed by users/customers in the digital era. On the other hand, user satisfaction significantly and directly influenced the net benefits of AI-powered chatbots in the digital era. In this regard, this study significantly contributed to the AI-powered chatbot's body of knowledge in the Palestinian digital context by identifying the factors that influence the use of chatbots in the digital era from the users’ point of view with the capacity to help transform the Palestinian business landscape

Discipline and Power in the Digital Age. Critical Reflections from Foucault’s Thought

In the ever-evolving landscape of the digital age, the theories posited by Michel Foucault four decades ago provide an insightful lens through which to view our contemporary technological society. This article underscores the shift from modern reference disciplines, such as biology, political economy, and linguistics, to the emergent domains of cognitive and computer sciences. By exploring the personalization of online user experiences via data collection and behavioral microtargeting, the study highlights the nuances of modern surveillance. This new era of monitoring bears a resemblance to Foucault’s concept of disciplinary power, marked by its subtle yet omnipresent control. In a world where digital oversight by governments and corporations is increasingly prominent, the relevance of Foucault’s ideas becomes significant for deciphering and traversing the intricate landscapes of power and surveillance in the digital age.

Performance Analysis of NLP-Based Machine Learning Algorithms in Cyberbullying Detection

In today's pervasive online landscape, the escalating threat of cyberbullying demands advanced detection and mitigation tools. This study utilizes Natural Language Processing (NLP) techniques to confront this imperative challenge, particularly in the dynamic realm of social media, focusing on tweets. A comprehensive NLP-based classification methods is deployed to uncover instances of cyberbullying. Nine prominent machine learning algorithms are meticulously evaluated: Logistic Regression, Decision Tree, Random Forest, Naive Bayes, K-Nearest Neighbor, Support Vector Machine, XGBoost, AdaBoost, and Gradient Boosting. Through the analysis, encompassing accuracy, precision, recall, and F1 score metrics, the study offers insights into the strengths and limitations of each approach. The findings carry profound implications for online user safeguarding and cyberbullying prevalence reduction. Notably, Random Forest and XGBoost classifiers emerge as pioneers with accuracy rates of 93.34% and 93.32%, respectively. This comparative research underscores the pivotal role of expert algorithmic choices in addressing the urgency of cyberbullying and has the potential to be a valuable resource for academics and practitioners engaged in combatting this pressing societal issue.

Not our kind of crowd! How partisan bias distorts perceptions of political bots on Twitter (now X).

Social bots, employed to manipulate public opinion, pose a novel threat to digital societies. Existing bot research has emphasized technological aspects while neglecting psychological factors shaping human-bot interactions. This research addresses this gap within the context of the US-American electorate. Two datasets provide evidence that partisanship distorts (a) online users' representation of bots, (b) their ability to identify them, and (c) their intentions to interact with them. Study 1 explores global bot perceptions on through survey data from N = 452 Twitter (now X) users. Results suggest that users tend to attribute bot-related dangers to political adversaries, rather than recognizing bots as a shared threat to political discourse. Study 2 (N = 619) evaluates the consequences of such misrepresentations for the quality of online interactions. In an online experiment, participants were asked to differentiate between human and bot profiles. Results indicate that partisan leanings explained systematic judgement errors. The same data suggest that participants aim to avoid interacting with bots. However, biased judgements may undermine this motivation in praxis. In sum, the presented findings underscore the importance of interdisciplinary strategies that consider technological and human factors to address the threats posed by bots in a rapidly evolving digital landscape.

LLM technologies and information search

With the booming of LLM technologies (e.g., ChatGPT), people’s goals and behaviors in information search have been reshaped significantly. This paper attempts to conceptually discuss how LLM technologies might revolutionize these important aspects in information search and provides a comprehensive analysis of the technological advancements and capabilities of ChatGPT, highlighting its potential to disrupt traditional search engines like Google. In addition, this paper contrasts ChatGPT’s conversational approach with Google’s link-based search model, offering a detailed examination of the implications for online search advertising and user behavior and explaining why Google is concerned about ChatGPT as well as its potential reactions.

Development of the decision-support tool ‘Harvest Mate’: economic calculations

Improvements in sugarcane-harvesting practices remain critically important for productivity, profitability and sustainability gains in the Australian industry. Developing a decision-support tool to guide economically optimum harvester settings could add AUD44mn to grower revenue. The online ‘Harvest Mate’ (HMate) tool was developed to reduce green sugarcane-harvesting losses and improve economic returns for growers. The ‘Harvest Mate’ tool includes setting-up an online user profile for initial data capture that enables users to assess the most profitable harvester settings (e.g., flow rate and extractor fan speeds) infield. HMate can estimate harvesting cost changes and guide pre-season contract rate negotiations between the contractor and grower. The economic component of HMate incorporates both variable and fixed cost inputs, allowing the overall net benefit/loss to be determined for the grower. The highest net benefit output is defined as the most economically optimal harvester setting, accounting for the cost of harvesting and revenue achieved. HMate economic estimates show a high level of predictive accuracy for the cost/ha, cost/t and net benefit obtained when compared to actual detailed harvesting group costings. Slight differences were identified in the predicted cost/h results for HMate due to improvements in labour and fuel calculations. Information entered by users allows the selection of the most profitable harvester settings for each unique situation that accounts for yield, CCS, and harvesting costs.

Identifying online user discourses triggered by destination stakeholder reactions

Identifying online user discourses triggered by destination stakeholder reactions

Dynamic elicitation and forecasting innovation requirement of smart product-service system via user-manufacturer value co-creation perspective using multi-source data

Achieving greater efficiency and lower resource consumption is a constant pursuit for successfully developing smart product-service system (PSS). Conducting dynamic requirements analysis is increasingly important in the process while the value co-creation of smart PSS stakeholders has not been sufficiently considered. This study adopts a user-manufacturer value co-creation perspective to explore smart PSS dynamic requirement elicitation and forecasting. Using multi-source data of user online reviews, historical descriptive texts and patent abstracts, this study dynamically elicits the smart PSS innovation requirements using dynamic topic model (DTM) and bidirectional encoder representation from transformers (BERT). Furthermore, the key innovation requirements of smart PSS are identified by integrating their importance and satisfaction values into a dynamic importance-performance analysis (DIPA). Then, the trend of smart PSS key innovation requirements is predicted by the grey forecasting model to determine the smart PSS improvement direction. An empirical study of smartwatch service system is conducted to validate the proposed approach. By blending both user and manufacturer requirements, this study provides a novel framework for dynamic requirement elicitation and forecasting of smart PSS based on multi-source data. It contributes to facilitating the value co-creation during smart PSS development to optimize manufacturer’s R&D resource allocation and enhance user experience.

Breaking the silence: leveraging social interaction data to identify high-risk suicide users online using network analysis and machine learning

Suicidal thought and behavior (STB) is highly stigmatized and taboo. Prone to censorship, yet pervasive online, STB risk detection may be improved through development of uniquely insightful digital markers. Focusing on Sanctioned Suicide, an online pro-choice suicide forum, this work derived 17 egocentric network features to capture dynamics of social interaction and engagement within this uniquely uncensored community. Using network data generated from over 3.2 million unique interactions of N = 192 individuals, n = 48 of which were determined to be highest risk users (HRUs), a machine learning classification model was trained, validated, and tested to predict HRU status. Model prediction dynamics were analyzed using introspection techniques to uncover patterns in feature influence and highlight social phenomena. The model achieved a test AUC = 0.73 ([0.61, 0.85], 95% CI), suggesting that network-based socio-behavioral patterns of online interaction can signal for heightened suicide risk. Transitivity, density, and in-degree centrality were among the most important features driving this performance. Moreover, predicted HRUs tended to be targets of social exchanges with lesser frequency and possessed egocentric networks with “small world” network properties. Through the implementation of an underutilized method on an unlikely data source, findings support future incorporation of network-based social interaction features in descriptive, predictive, and preventative STB research.

Evaluating the impact of the global evidence, local adaptation (GELA) project for enhancing evidence-informed guideline recommendations for newborn and young child health in three African countries: a mixed-methods protocol

BackgroundPoverty-related diseases (PRD) remain amongst the leading causes of death in children under-5 years in sub-Saharan Africa (SSA). Clinical practice guidelines (CPGs) based on the best available evidence are key to strengthening health systems and helping to enhance equitable health access for children under five. However, the CPG development process is complex and resource-intensive, with substantial scope for improving the process in SSA, which is the goal of the Global Evidence, Local Adaptation (GELA) project. The impact of research on PRD will be maximized through enhancing researchers and decision makers’ capacity to use global research to develop locally relevant CPGs in the field of newborn and child health. The project will be implemented in three SSA countries, Malawi, South Africa and Nigeria, over a 3-year period. This research protocol is for the monitoring and evaluation work package of the project. The aim of this work package is to monitor the various GELA project activities and evaluate the influence these may have on evidence-informed decision-making and guideline adaptation capacities and processes. The specific project activities we will monitor include (1) our ongoing engagement with local stakeholders, (2) their capacity needs and development, (3) their understanding and use of evidence from reviews of qualitative research and, (4) their overall views and experiences of the project.MethodsWe will use a longitudinal, mixed-methods study design, informed by an overarching project Theory of Change. A series of interconnected qualitative and quantitative data collections methods will be used, including knowledge translation tracking sheets and case studies, capacity assessment online surveys, user testing and in-depth interviews, and non-participant observations of project activities. Participants will comprise of project staff, members of the CPG panels and steering committees in Malawi, South Africa and Nigeria, as well as other local stakeholders in these three African countries.DiscussionOngoing monitoring and evaluation will help ensure the relationship between researchers and stakeholders is supported from the project start. This can facilitate achievement of common goals and enable researchers in South Africa, Malawi and Nigeria to make adjustments to project activities to maximize stakeholder engagement and research utilization. Ethical approval has been provided by South African Medical Research Council Human Research Ethics Committee (EC015-7/2022); The College of Medicine Research and Ethics Committee, Malawi (P.07/22/3687); National Health Research Ethics Committee of Nigeria (01/01/2007).

How persuasive are political cheapfakes disseminated via social media? The effects of out-of-context visual disinformation on message credibility and issue agreement

ABSTRACT Although extant research offered first insights into the effects of visual disinformation, we know markedly little about the effects of cheapfakes that rely on the deceptive re-contextualization of authentic visual materials disseminated via social media. Against this backdrop, we rely on online experiments (N = 874) and (N = 733) in which we exposed representative samples of participants to different types of (visual) disinformation in which the out-of-context presentation of visual material was central. Our findings indicate that cheapfakes based on video editing are not more credible than textual or image-only visual disinformation, while disinformation in general can lower support for a delegitimized politician. As a key contribution, we show that when cheapfakes are endorsed by other online users, they can mislead people and therewith succeed in delegitimizing targeted political actors, although richer modalities used in cheapfakes do not amplify deceptive content’s impact.

A Text-Inception-Based Natural Language Processing Model for Sentiment Analysis of Drug Experiences

The study of sentiment in Natural Language Processing (NLP) is among the most successful research areas because of the availability of millions of user opinions online since the turn of the century. The economic, political, and medical fields are just some of the many that have benefited from studies of sentiment research. While numerous studies have examined more mainstream topics like consumer electronics, movies, and restaurants, relatively few have examined health and medical concerns. Considerable insight into where to direct efforts to improve public health might be gained by a study of how people feel about healthcare as a whole and of individual drug experiences in particular. When it comes to medicine, automatic analysis of online user evaluations paves the way for sifting through massive amounts of user feedback to find information regarding medications' efficacy and side effects that might be used to enhance pharmacovigilance programs. Simple rules-based methods have given way to more complex machine learning approaches like deep learning, which is developing as a technology for many natural language processing jobs. The opensource datasets have been analyzed with models that use word embeddings and term frequency-inverse document frequency (TF-IDF). A feature-enhanced text-inception model for sentiment classification was presented to work in tandem with this approach. The model first employed a cutting-edge text-inception module to glean useful shallow features from the text. K-MaxPooling was subsequently employed to reduce the dimensionality of its shallow and deep includes as well as enhance the generalization of characteristics, and a deep feature extraction module was formed using the bidirectional gated recurrent unit (Bi-GRU) and the capsule neural network to comprehend the text's semantic data. By combining traditional methods with cutting-edge artificial intelligence techniques, this hybrid approach can revolutionize public health initiatives, decision-making, and pharmacovigilance in the healthcare industry. This model achieved an exceptional accuracy rate of 99%, underscoring its effectiveness in sentiment classification and demonstrating its potential to significantly contribute to advancing healthcare and medical research.

Does Social Media Marketing Incorporating Accreditation Information Enhance Online Users’ Positive Behavioral Intentions Toward Nonprofit Organizations?

ABSTRACT Despite the increased adoption of social media marketing and accreditation practices by NPOs, little attention has been paid to their active promotion of accreditation information on social media and its potential impact on people’s attitude or behavioral change. This study examines whether the integration of accreditation information in nonprofit organizations’ social media marketing affects online users’ attitudes and behavioral intentions toward the promoted nonprofits This study conducts an online experiment using fictitious social media pages that include identifiable information on the NPOs’ environmental causes combined with an accreditation logo on public charities’ accountability as a device to test the signaling effect. This study created a factorial design of (2 × 2) by the combination of the existence of an accreditation logo (presence/absence) and the environmental awareness level (high/low). A total of 146 completed responses from graduate and undergraduate students of a public university were used for the analysis. Results show that respondents, having a high level of environmental awareness, demonstrated more positive attitudes toward the advertised nonprofits with certificate logo. This study suggests that the implementation of social media marketing with accreditation logo should be approached with caution, as it could potentially backfire with individuals with low environmental awareness.

Exploring the presence and persistence of cartographic anchors in pan-scalar maps

ABSTRACT Within this article, we hypothesize that, in the virtual space of pan-scalar maps, i.e. interactive, multi-scale and zoomable web maps, the map components perceived by the map user during a zoom can mimic the qualities of real-world anchors or landmarks to help self-localization in map. This article explores the characterizations of these potential cartographic anchors through the analysis of overlapping digital drawings on map views, collected with an online user survey. Our results allow us to identify possible cartographic anchors, characterize them in their variability, identify similar logics despite variations in localization, and discuss origins of these perceptual comparisons.

Adoption of contact tracing app during pandemic: Users’ resistance behavior

ObjectivesThe study investigates the key issues influencing different barriers resulting in user resistance toward adopting contact tracing smartphone apps launched to track COVID-19 infections. MethodsIndian users’ experiences regarding the Aarogya Setu app for preventing the spread of COVID-19 were examined in two phases. In Phase I, online users’ comments available at the Google Play Store were qualitatively analyzed using open and axial coding. These codes were then used to create an implication matrix and hierarchical value maps to illustrate and interpret the relationships between issues, barriers, and user behavior. In Phase II, a supplementary empirical study, data was collected from users and non-users of the app through semi-structured telephone interviews and then qualitatively analyzed. ResultsBy drawing on innovation resistance theory, the current study mapped a set of adoption barriers with three types of user resistance, i.e., postponement, opposition, and rejection. Rejection emerged as the most prominent consumer resistance behavior; usage barriers, functional risk, and value barriers related to the app's usage were the key drivers of this behavior. Postponement was the second most observed consumer resistance behavior. If usage barriers, functional risk, and value barriers of the app resulted in functional barriers toward adoption of the app, then image barrier was the key reason behind the psychological barrier. ConclusionAdministrators and developers of future interventions need to be conscious of usage barriers, functional risks, and value barriers related to the app's usage through stakeholder engagement to secure broader and faster adoption of such apps to improve health information systems.

Failure to treat ventricular tachycardia or ventricular fibrillation by implantable cardioverter-defibrillators

BackgroundNormally functioning implantable cardioverter-defibrillators (ICDs) with intact lead systems occasionally fail to deliver therapy for ventricular tachycardia/fibrillation (VT/VF) or deliver it only after clinically significant delays (failure-to-treat). ObjectiveWe aimed to investigate ICD failure-to-treat VT/VF in a large patient cohort. MethodsWe searched the US Food and Drug Administration's online Manufacturer and User Facility Device Experience (MAUDE) database from 2019 to 2023 for manufacturer-verified reports in which normally functioning ICDs failed to treat VT/VF. ResultsWe identified 854 reports classified as deaths (n = 96 [11.2%]), injuries (n = 585 [68.5%]), or malfunctions (n = 173 [20.4%]) for normally functioning ICDs. The most common causes were misclassification as supraventricular tachycardia (SVT) or atrial fibrillation (AF; 54.8%), undersensing (21.1%), and failure to satisfy programmed rate/duration criteria (8.7%). Most events caused by misclassification as SVT/AF (89.5%) and failure to satisfy rate/duration criteria (70.3%) were VT; most caused by undersensing were either VF (54.4%) or not specified as VT or VF (19.4%). Undersensing caused 65.6% of deaths, although it represented only 21.1% of reports. In the United States, the number of reports increased faster than that of ICD patients. ConclusionIn the largest reported series of failure-to-treat VT/VF by normally functioning ICDs, the most common cause was misclassification of VT as SVT/AF; the most common cause of death was undersensing of VF. Although relatively few patients with normally functioning ICDs experience failure-to-treat VT/VF, the absolute number of verified MAUDE reports suggests that more work is needed to quantify the magnitude of the problem, to identify root causes, and to develop solutions.

“Dropping the F-Bomb”: Vocabulary Choice in Feedback and Novelty in Online User Community Innovation

“Dropping the F-Bomb”: Vocabulary Choice in Feedback and Novelty in Online User Community Innovation

Comparing online wrist and forearm EMG-based control using a rhythm game-inspired evaluation environment

Objective.The use of electromyogram (EMG) signals recorded from the wrist is emerging as a desirable input modality for human-machine interaction (HMI). Although forearm-based EMG has been used for decades in prosthetics, there has been comparatively little prior work evaluating the performance of wrist-based control, especially in online, user-in-the-loop studies. Furthermore, despite different motivating use cases for wrist-based control, research has mostly adopted legacy prosthesis control evaluation frameworks.Approach.Gaining inspiration from rhythm games and the Schmidt's law speed-accuracy tradeoff, this work proposes a new temporally constrained evaluation environment with a linearly increasing difficulty to compare the online usability of wrist and forearm EMG. Compared to the more commonly used Fitts' Law-style testing, the proposed environment may offer different insights for emerging use cases of EMG as it decouples the machine learning algorithm's performance from proportional control, is easily generalizable to different gesture sets, and enables the extraction of a wide set of usability metrics that describe a users ability to successfully accomplish a task at a certain time with different levels of induced stress.Main results.The results suggest that wrist EMG-based control is comparable to that of forearm EMG when using traditional prosthesis control gestures and can even be better when using fine finger gestures. Additionally, the results suggest that as the difficulty of the environment increased, the online metrics and their correlation to the offline metrics decreased, highlighting the importance of evaluating myoelectric control in real-time evaluations over a range of difficulties.Significance.This work provides valuable insights into the future design and evaluation of myoelectric control systems for emerging HMI applications.