Patterns Of Online Behavior Research Articles

"I'm in the Bluesky Tonight": Insights from a year worth of social data.

Pollution of online social spaces caused by rampaging d/misinformation is a growing societal concern. However, recent decisions to reduce access to social media APIs are causing a shortage of publicly available, recent, social media data, thus hindering the advancement of computational social science as a whole. We present a large, high-coverage dataset of social interactions and user-generated content from Bluesky Social to address this pressing issue. The dataset contains the complete post history of over 4M users (81% of all registered accounts), totalling 235M posts. We also make available social data covering follow, comment, repost, and quote interactions. Since Bluesky allows users to create and like feed generators (i.e., content recommendation algorithms), we also release the full output of several popular algorithms available on the platform, along with their timestamped "like" interactions. This dataset allows novel analysis of online behavior and human-machine engagement patterns. Notably, it provides ground-truth data for studying the effects of content exposure and self-selection and performing content virality and diffusion analysis.

Advancing Mental Health and Ethics Education for College Students

This study explores the implementation of the DeepPsy framework at a university in Hebei, China, assessing its impact on student mental health and ethical behavior. By integrating a 2D convolutional neural network (2D-CNN) with long short-term memory (LSTM) networks, DeepPsy analyzes complex online behavior patterns and academic performance data. The methodology involves diverse data collection techniques, providing a comprehensive understanding of students' mental health. Results indicate improved identification of at-risk students, leading to timely interventions and enhanced mental health outcomes. This study highlights the potential of advanced data analytics in fostering supportive educational environments that promote student mental health and ethical decision making.

Family communication patterns, self-efficacy, and adolescent online prosocial behavior: a moderated mediation model

As technology has been developing by leaps and bounds, concerns regarding adolescent online behavioral patterns have garnered significant attention. Nevertheless, current research exhibits limitations in both perspective and depth. Consequently, this study introduces a moderated mediation model to investigate whether the mediating effect of self-efficacy and the moderating effect of emotional regulation strategies are valid in the relationship between family communication patterns and adolescent online prosocial behavior. A questionnaire survey encompassing 1183 adolescents across 12 schools in three cities of mainland China was conducted. The findings reveal that conversation orientation contributes to the augmentation of adolescents’ self-efficacy and online prosocial behavior, whereas conformity orientation follows a reversed trend. Furthermore, self-efficacy serves as a mediator in the relationship between conversation orientation and conformity orientation, influencing adolescent online prosocial behavior in both positive and negative manners. Additionally, this study underscores the significance of emotion regulation strategies; cognitive reappraisal not only reinforces the positive effects of conversation orientation, but also mitigates the adverse effects of conformity orientation, while expressive suppression demonstrates the inverse effect. This research yields a comprehensive and insightful understanding of adolescent online prosocial behavior, furnishing a valuable theoretical foundation for future research and practice in family education.

Parental Monitoring of Early Adolescent Social Technology Use in the US: A Mixed-Method Study

Parental monitoring of digital media use evolves throughout childhood and adolescence and become increasingly important when autonomy seeking on social technologies emerges during early adolescence. We investigate a survey cohort of 248 US parents of early adolescents and their parental media monitoring behaviors (i.e., restrictive, active, deference), the family context (i.e., closeness and parent technoference), and perceptions of child’s problematic internet use. Using an explanatory mixed methods approach, the results of this study reveal that restrictive parental monitoring of adolescents’ digital media use is positively associated with child’s problematic internet use. However, active and deference monitoring are not associated with early adolescent problematic internet use and positively associated with family contexts. Familial closeness and technoference are consistent negative and positive correlates, respectively, of perceived problematic use among early adolescents. Qualitative interviews with a subset of 31 parents reveal while most parents report restrictive behaviors, multiple techniques (e.g., active, surveillant, and deference) are also leveraged when navigating their child’s online behaviors. Parents tended to converge on the same types and reasons for restrictive monitoring of media, whereas for other approaches the reasons behind their decision-making were quite divergent. The implication of this study is that parental media monitoring behaviors during early adolescence are rapidly evolving and not confined to a singular strategy, often related to prevention of or intervention on their child’s online behavior patterns. Understanding the family dynamics and parent involvement of adolescent’s digital media use remains critical in prevention of child’s problematic behaviors and promotion of positive online behaviors.

Control and Research of Computer Virus by Multimedia Technology

The rapid development of multimedia technology has brought great changes to people's lives and production. Computer viruses also spread widely through the network. In order to solve the above problems, this article intends to use multimedia technology to control computer viruses, and proposes a virus control method based on SICES model. According to the controlled SICES model and the set objective function, the influence of users' online behavior patterns on virus propagation can be explored, and an optimal control problem is proposed. Through theoretical analysis, the existence of optimal control is proved, and the optimal system is obtained. Numerical experiments show the effectiveness of the optimal control strategy. In addition, in order to optimize the objective function to make its value smaller, and to control the proportion of infected nodes at a lower level, users should choose the corresponding online behavior mode according to different network structures. For example, on the WS small world network, the network should be frequently disconnected or always kept online.

Generation Z in Chile, Colombia, México, and Panama: Interests and new digital consumption habits. Their use of Instagram and TikTok

In a reality that combines the virtual with the physical and in a context of information saturation, there are consumers who are more critical, demanding, and less loyal. In this context, it is timely to know the new habits of the Centennials, so as to understand their behaviors and consumption trends, aligning future communication strategies to their personality and interests. The Centennials or Generation Z are digital natives, concerned with technology, and complex to understand (Alonso-López; Terol-Bolinches, 2020). Generation Z –analyzed in this study– is composed of those born between 2000 and 2008, which corresponds to the youth currently between 14 and 22 years old (Vilanova; Ortega, 2017; Hernández; Andrade-del-Cid, 2020). The main objective of this research is to determine the patterns of consumption and online behavior exhibited by this generation in Mexico, Colombia, Chile, and Panama, with a focus on the characteristics of communication processes, online consumption patterns, and the cultural traits they possess. To achieve this, a mixed method is proposed consisting of a Twitter conversation analysis, survey application (n=550), in-depth interviews (n=36), and focus groups (n=2); all of which have been applied in the four selected countries. The main conclusions are that the preferred content of Centennials is humor (75%), with Instagram and TikTok being the most used networks. For they, the most important thing is to take care of their image and 40% are not interested in technological topics. Furthermore, cultural characteristics are shared among countries; however, language is the main difference between countries.

Discovery of behavioral patterns in online social commerce practice

AbstractDiscovery of behavioral patterns in online social commerce practice becomes important in this digital era. In this article, we propose a systematic approach to behavioral pattern discovery, and apply it in an emerging online social commerce venue: live streaming. We investigate behavioral patterns in gifting encouragement in live streaming to understand online social commerce practice. Our proposed approach is based on multiple triangulation, including data source triangulation (i.e., streamers, viewers, and actual behavior) and data collection method triangulation (i.e., interviews, focus groups, and observations). Through multiple triangulation, four behavioral patterns of gifting encouragement are discovered: (i) requesting a certain gift for providing a particular service, (ii) creating a raffle, (iii) eliciting competition between individuals, and (iv) eliciting competition between groups. This research reveals the special behavioral patterns in live streaming, and thus increases our knowledge of social commerce practices. This research provides a systematic approach to discover online behavioral patterns, and provides practical implications in live streaming platforms, especially in marketing and platform design.This article is categorized under: Application Areas > Business and Industry

Improving recommendation by connecting user behavior in temporal and topological dimensions

The rapid development of Internet provides many high quality data sets for understanding human online behavior patterns. Many analysis tools from complexity science such as complex networks and human dynamics have been applied to study human online activities. However, in most existing works, the topological and temporal features of human online activities are analyzed independently. In this paper, we connect these two dimensions by investigating the relations between online users inter-event time and the network distance between the items selected by users. We find that users will choose items with longer network distance when the inter-event time is long, and vice versa. This finding is then applied to improve the recommendation process where recommendation diversity should be given more weight when users return from a long inactive period. Finally, we use a trade-off between accuracy and complexity to further improve the recommendation algorithms.

Investigating the Links Between Students’ Learning Engagement and Modeling Competence in Computer-Supported Modeling-Based Activities

The purpose of this study was to understand how students engage in computer-supported modeling-based activities (CSMBAs), and the relationship between their engagement and their modeling competence. Different facets of learning engagement were measured through multiple data, including performance on modeling tasks, self-reported level of engagement, and online behavior patterns of science modeling. The research participants were 76 11th-grade students in Taiwan. The research instruments included online student worksheets, an engagement questionnaire, computer logs, and modeling competence tests. Students’ online worksheets were scored and used to group them into three performance groups—the low-level-performance group (LPG), the middle-level-performance group (MPG) and the high-level-performance group (HPG). ANOVA statistics lag sequential analysis (LSA), and ANCOVA statistics were used for the data analysis. The results showed that, first, in analyzing the engagement questionnaires, students’ negative cognitive engagement, negative behavioral engagement, and negative social engagement all played important roles in their low performance in the CSMBAs. Second, through the use of LSA, it was found that the LPG students lacked evaluative behavior, while the HPG students emphasized reflective behavior. Third, analysis of the students’ pre- and post-modeling competence tests showed that those who were in the HPG and MPG scored significantly higher than those in the LPG in two dimensions of the modeling competence post-tests. The results indicate that efforts made in completing tasks in CSMBAs can lead to better modeling competence. Implications for developing future CSMBAs and for promoting student engagement are suggested.

Google analytics of a pilot study to characterize the visitor website statistics and implicate for enrollment strategies in Medical University

BackgroundTaiwan’s colleges and universities are struggling to maintain their student enrollment rates owing to the declining fertility rate. Focusing on students in higher education programs, this study aims to analyze online behavioral patterns for university departmental websites and accordingly, suggests response strategies to increase the rate of enrollment.MethodsWe use Google Analytics to examine the websites of two departments in a medical university between February 1 and July 30, 2018. We study website patterns during the study periods for three college admission routes: STARS program, personal applications, and admission through examination and placement.ResultsMost website visitors during the three visiting date ranges for the two departments are 18–24 years. The visitor groups are mainly freshmen at the university and their parents. The homepage and Subject Credits, Course Planning, Teacher Lineup, and Certificate of Subjects were the most visited webpages. The overall number of daily page views varied by academic event.ConclusionsUniversity departments should enhance the presentation of featured courses on their webpage or distinguish course characteristics from those of competing departments in the curriculum to ensure clear market segmentation. In addition, departments should consider examining online data to identify suitable high schools that can be visited to attract potential students and to improve students’ willingness to choose their university.

A survey of Twitter research: Data model, graph structure, sentiment analysis and attacks

Twitter is the third most popular worldwide Online Social Network (OSN) after Facebook and Instagram. Compared to other OSNs, it has a simple data model and a straightforward data access API. This makes it ideal for social network studies attempting to analyze the patterns of online behavior, the structure of the social graph, the sentiment towards various entities and the nature of malicious attacks in a vivid network with hundreds of millions of users. Indeed, Twitter has been established as a major research platform, utilized in more than ten thousands research articles over the last ten years. Although there are excellent review and comparison studies for most of the research that utilizes Twitter, there are limited efforts to map this research terrain as a whole. Here we present an effort to map the current research topics in Twitter focusing on three major areas: the structure and properties of the social graph, sentiment analysis and threats such as spam, bots, fake news and hate speech. We also present Twitter’s basic data model and best practices for sampling and data access. This survey also lays the ground of computational techniques used in these areas such as Graph Sampling, Natural Language Processing and Machine Learning. Along with existing reviews and comparison studies, we also discuss the key findings and the state of the art in these methods. Overall, we hope that this survey will help researchers create a clear conceptual model of Twitter and act as a guide to expand further the topics presented.

DEVIANT ONLINE BEHAVIOR IN ADOLESCENT AND YOUTH CIRCLES: IN SEARCH OF A RISK ASSESSMENT MODEL

The authors made an attempt to describe the problem of developing the concept of risk assessment of deviant online behavior of minors and young adults in social networks. The article looks into the psychological consequences of banalizing of the internet, analysis possibilities for offline and online behavior. Approaches for risk factors assessment of deviant behavior in real life are described: the qualitative (clinical), statistical (actuarial) and structured. The article systemizes the studies of risk factors, vulnerability, and deviant patterns in the context of phenomena such as aggressive, asocial, auto-aggressive, self-mutilating, suicidal, risk-taking and victim online behavior. Approaches and models of online deviant behavior are discussed; an attempt is made to build a structured model of risk assessment of deviant patterns of online behavior in the context of the cultural historical concept. On the basis of theoretical analysis, a hypothetical set of group, interindividual and intraindividual constructs is formulated, the combination of online and offline risk factors produces a model for risk assessment of deviant behavior. The article is written as part of the research project “Developing the profiling model of online behavior of minors and young adults in social networks” which was initiated by the Moscow State University of Psychology and Education.

Using cluster analysis to explore the engagement with a flipped classroom of native and non-native English-speaking management students

Flipped classrooms are becoming increasingly popular, particularly when teaching non-native speaking students. Existing research has largely focused on examining academic performance and students’ perceptions of the learning process. This exploratory study uses log-file data to identify hidden patterns of student online behaviour in a flipped classroom environment for a cohort of students, with special attention to their command of the home language of the institution. Using cluster analysis, categories were identified regarding when and how often online flipped lessons were accessed: (i) before class, (ii) after class and before a weekly exam, and (iii) after the weekly exam but before an assignment. Gender and the average number of minutes the lessons were accessed before each time period were also considered. Findings indicated that there was sustained access to flipped materials throughout the semester for all students. In addition to accessing online lessons prior to class, students also accessed online lessons prior to weekly exams and project submission deadlines, indicating the value of such material for revision. Interestingly, two clusters of non-native English-speakers were identified where one group accessed the material more often than native students, and the second group accessed the material less frequently than the native English-speaking students.

SOCIAL ANXIETY AND SELF-REPORTED TIME SPENT ONLINE IN A SAMPLE OF ALBANIAN UNIVERSITY STUDENTS

Online activity serves different purposes, one of them being communication and social interaction. Studies have demonstrated that individuals tend to display online behavioral patterns that are similar to their social groups; also there is some evidence that individuals who have interaction difficulties in real life (e.g., social anxiety) might engage more in online behavior. The purpose of the present study was to investigate the relationship between self-reported time spent online, descriptive social norms, and social anxiety symptoms in a sample of 356 Albanian University students. Participants were recruited online. Results showed a significant predictive model for self-reported time spent online, F (7,342)=48.99, p<.001, R2=.50. Age, gender, and the four social anxiety components were not significant predictors; only descriptive norms had a significant effect, β=.70, p<.001. Results are in line with the social normative approach to internet use and have several implications which are discussed in the paper.

Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs

ABSTRACTThe emergence of massive open online courses not only changes the ecology of higher education, but also facilitates a blending learning paradigm, also known as small private online courses (SPOCs). In order to understand how college students interact with an SPOC platform, this study collects their online behaviors for a semester and adopts a lag sequential analysis approach to identify significant transitions between interactions with content, peers, and instructors. Regarding content, after entering courses, the students tend to access learning resources. Besides, the transitions between learning resources and personal performance are significantly interconnected to each other. Regarding peers, the interaction with classmates was mainly connected to the access of assignments and performance. Regarding instructors, the interaction with teachers was minor but connected to all other behaviors. In addition, the results also show that students’ online behavioral patterns in SPOCs may change over time. The implications of the findings for SPOCs research are discussed in this paper.

Evaluation of Online Log Variables that Estimate Learners’ Time Management in a Korean Online Learning Context

The purpose of this study was to identify the relationship between the psychological variables and online behavioral patterns of students, collected through a Learning Management System (LMS). Test was attempted of a structural equation model representing the relationships among Time and Study Environment Management (TSEM), one of the sub-constructs of MSLQ, influencing a set of time-related online log variables: login frequency, login regularity, and total login time. Data were collected from 188 college students in a Korean university. Employing structural equation modeling, a hypothesized model was tested for measuring the model fit. The results presented a criterion validity of online log variables to estimate their time management. The structural model including TSEM, online variable, and final score with a moderate fit indicated that learners’ time related online behavior mediates their psychological functions and their learning outcome. Based on the results, the final discussion includes the recommendations for further study and the meaningfulness in regard to the expantion of Learning Analtyics for Performance and Action (LAPA) model.

Exploring college students' generational differences in Facebook usage

In recent years the usage of social networks among students has become the subject of numerous studies. In this study we were interested in differences in patterns of online behaviour and the use of Facebook (FB) among the second and third generation of students. The results showed that 66% of the second year students had a FB account, while in the third generation all of the respondents had a FB account. One third of the second year students avoid any involvement in FB. Lack of interest was one of the main reasons the respondents indicated for not being engaged in FB, as well as their unwillingness to put personal information online. Currently, most of them are satisfied using FB, and do not plan to turn off their profile. The newer generations of students use FB mainly for overcoming boredom. Based on the results of the study, it can be concluded that the time spent online increases, as well as the number of FB users, and also the number of virtual friends increases in order to exchange information.

Development of the Learning Analytics Dashboard to Support Students' Learning Performance

The Learning Analytics Dashboard (LAD) is an application to show students' online behavior patterns in a virtual learning environment. This supporting tool works by tracking students' log-files, mining massive amounts of data to find meaning, and visualizing the results so they can be comprehended at a glance. This paper reviews previously developed applications to analyze their features. Based on the implications from the review of previous studies as well as a preliminary investigation on the need for such tools, an early version of the LAD was designed and developed. Also, in order to improve the LAD, a usability test incorporating a stimulus recall interview was conducted with 38 college students in two blended learning classes. Evaluation of this tool was performed in an experimental research setting with a control group and additional surveys were conducted asking students' about perceived usefulness, conformity, level of understanding of graphs, and their behavioral changes. The results indicated that this newly developed learning analytics tool did not significantly impact on their learning achievement. However, lessons learned from the usability and pilot tests support that visualized information impacts on students' understanding level; and the overall satisfaction with dashboard plays as a covariant that impacts on both the degree of understanding and students' perceived change of behavior. Taking in the results of the tests and students' open- ended responses, a scaffolding strategy to help them understand the meaning of the information displayed was included in each sub section of the dashboard. Finally, this paper discusses future directions in regard to improving LAD so that it better supports students' learning performance, which might be helpful for those who develop learning analytics applications for students.

E-service quality perceptions: an empirical analysis of the Chinese e-retailing industry

China will become the largest online retail market in the world in the near future. Chinese consumers may have their own patterns of online consumption and behaviour that are different from those of consumers in the West. However, most research on consumer behaviour and e-service quality relies on theoretical frameworks developed in Western societies. The aim of this paper is to propose and empirically verify a conceptual framework for how customers judge e-service quality in the context of the Chinese e-retailing industry. Meanwhile, the key dimensions of e-service quality are searched with a survey to understand the e-service quality perceptions of Chinese consumers to enhance their e-satisfaction and e-loyalty. The empirical results suggest that e-retailers should pay more attention to process quality and service recovery in the context of Chinese culture and Chinese personality with high uncertainty avoidance taking consideration of service guarantee as one aspect.

Effect of Task-induced Online Learning Behavior on Incidental Vocabulary Acquisition by Chinese Learners—Revisiting Involvement Load Hypothesis

In the present study, Laufer & Hulstijn’s Involvement Load Hypothesis was tested by a different methodology from that adopted by the previous similar studies. That is, instead of focusing only on the product of learning, the present study also attended to the details of learners’ task-induced online learning behavior via a specially designed computer program. Eighty-one participants were randomly assigned to one of the four tasks with different amount of involvement load. Once completing the task, the participants were unexpectedly tested on the retention of the target words that appeared in the texts. Two weeks later they were given two delayed posttests.The data were analyzed both quantitatively and qualitatively. The results suggested that different tasks did elicit different patterns of on-line learning behavior in terms of frequency of look-ups and amount of time spent on target words. It was also found that tasks assumed with higher involvement load hypothesis did not necessarily lead to higher retention scores. It was concluded that the criteria that Laufer & Hulstijn have proposed for grading the task effectiveness may be too simplified and idealized, failing to reveal the sophisticated nature of the cognitive processes that the different task induces.