Online Social Behavior Research Articles

COVID-19 and Autism Research: Perspectives from Around the Globe.

COVID-19 and Autism Research: Perspectives from Around the Globe.

The Use and Abuse of Technology: The Relationship Between Personalities and Continuance Intention in Performing Internet Vigilantism Among Malaysians

The opportunity to research Internet Vigilantism (IV) is timely due to its wide prevalence and the social concern that surrounds it. There is still a lack of comprehensive academic understanding of why users continue to condone IV. A significant number of studies suggested that the role of personalities has instigated such intentions. This was based on significant relationships found between different types of personality to their continuance intention of performing cyberbullying, a subsequent result of IV. This study reviewed literature in the respective areas and intends to identify the level of extraversion, neuroticism, and psychoticism personality among Malaysians and their continuance intention to perform IV. This study also hypothesized that there were significant relationships between extraversion, neuroticism and psychoticism personality with continuance intention to perform IV. Data was collected from 247 Internet users in Malaysia and was analyzed with statistical analysis (SPSS) using mean, standard deviation, and regression analysis. It was found that Malaysians possessed mild neuroticism personality but strong psychoticism and extraversion personality. There were also significant relationships between extraversion, neuroticism, and psychoticism personality with continuance intention to perform IV among Malaysians. This study has contributed to the literatures of psychology works that correlate personality traits to online social behaviour.

The Relationship of Online Social Networking and Various Psychological Behaviors

The basis of this study is to evaluate and measure the correlation between the amount of time an individual spends online social networking and their self-reported levels of loneliness, depression, anxiety, and stress. With societies having increased connectivity, the researchers wanted to investigate how this affects our psychological functioning. This research is a follow-up study that targets a sample population of 189 people, over the age of 24, using a final scale that combined the UCLA Loneliness and DASS-21 scales. The final scale was created via Google Forms and was later published on Amazon Mechanical Turk. Results for statistical significance were analyzed through Spearman Rank Order Correlations, ANOVAS and descriptive statistics to indicate correlations between social networking and self-reported levels of loneliness, depression, anxiety, and stress from using both Facebook and YouTube platforms.

How Smoking Advocates are Connected Online: An Examination of Online Social Relationships Supporting Smoking Behaviors

Social media platforms can facilitate online relationship formation among people who engage in risky health behavior such as smoking or unprotected sex. The purpose of this study is to gain a better understanding of how individuals who promote risky health behavior are connected with similar others on social media. Focusing on smoking behavior, this study investigates the theoretical mechanisms that drive social connections among pro-smoking users, and examines an empirical instance of one such network structure on Twitter. Consistent with the social identity framework, the study finds that pro-smoking networks manifest higher stance homophily (pro-smoking vs. anti-smoking) and higher network cohesion than anti-smoking networks. Different from the hypothesis, however, the result shows lower network exclusivity than anti-smoking networks. Most pro-smoking users who had social ties with anti-smoking users were found to be individuals rather than pressure groups or organizations. Bridging users on both sides tended to be linked to pressure groups. This paper concluded with discussion of implications of the current findings.

A framework for understanding online group behaviors during a catastrophic event

This study investigated the underlying mechanisms of online social media group behaviors in an emergency. The proposed framework was designed to analyze group behaviors/interactions and examine the main topics of interest among numerous tweets generated in an emergency. We collected tweets sent during Hurricane Harvey in 2017 and applied the framework to demonstrate its effectiveness. The proposed framework enables us to understand the unique characteristics of group interactions and develop operational strategies to effectively communicate with the public, as well as other groups, as critical emergency information appears in an online social network.

Machine Learning Framework for Detection of Psychological Disorders at OSN

Now a days attractiveness of social networking sites indications to the problematic habit. For this reason, researchers devised stress detection systems based psychological disorders in social networks. In this work, we propose a system of psychological disorders detection (PDD) that can provide online social behaviour extraction. It offers an opportunity to identify disorder at an early stage. These PDD system are made a different and advanced for the preparation of disorder detection. Propose system a machine learning approach that is detection of psychological disorders in social networks and social interaction features from social network data for detect with precision possible cases of disorders detection. We perform an analysis of the characteristics, and we also apply machine learning classifier in large-scale data sets and analyse features of psychological mental disorders. After classification results show that user are in stress or not, will be detected by PDD system is used to recommend hospitals on a map and at the same time admin will send mail of precaution list to user for users healthy and happy in life. The proposed method could help in developing a social network diagnostic tool for stress detection. It is useful in the diagnosis of psychological disorder detection in social platforms.

The reflection of offline activities on users’ online social behavior: An observational study

The ever increasing presence of online social networks in users’ daily lives has led to the interplay between users’ online and offline activities. There have already been several works that have studied the impact of users’ online activities on their offline behavior, e.g., the impact of interaction with friends on an exercise social network on the number of daily steps. In this paper, we consider the inverse to what has already been studied and report on our extensive study that explores the potential causal effects of users’ offline activities on their online social behavior. The objective of our work is to understand whether the activities that users are involved with in their real daily life, which place them within or away from social situations, have any direct causal impact on their behavior in online social networks. Our work is motivated by the theory of normative social influence, which argues that individuals may show behaviors or express opinions that conform to those of the community for the sake of being accepted or from fear of rejection or isolation. We have collected data from two online social networks, namely Twitter and Foursquare, and systematically aligned user content on both social networks. On this basis, we have performed a natural experiment that took the form of an interrupted time series with a comparison group design to study whether users’ socially situated offline activities exhibited through their Foursquare check-ins impact their online behavior captured through the content they share on Twitter. Our main findings can be summarised as follows (1) a change in users’ offline behavior that affects the level of users’ exposure to social situations, e.g., starting to go to the gym or discontinuing frequenting bars, can have a causal impact on users’ online topical interests and sentiment; and (2) the causal relations between users’ socially situated offline activities and their online social behavior can be used to build effective predictive models of users’ online topical interests and sentiments.

Facebook short response: The 10/90 model and comment and-reply strategy

This paper presents an action research project designed to explore how a Facebook group wall discussion is integrated as the flexible learning activity (FLA) in an undergraduate teacher education program literature course. It describes the nature, patterns and nuances of the short responses and how teacher-student and student-student interactions in the online course enrichment activities are formed. Data was gathered through content analyses of comments and replies including an online forum. Although action research is limited by its generalizability and focus, the study yields innovative and interactive ways of keeping students to be academically and emotionally engaged through the consistently prevailing 10/90 model ratio of teacher and students online content contribution shared respectively. Positive theme generations and active interactions of students are mostly evident. Pedagogical online functions of teacher and student-student connections are recognized to support how the project became a viable platform for idea expression, fun, freedom and flexibility, and thus converting students' regular online social media behaviors into academic works. Considerations for the next cycle via Facebook group or related platforms are also extended. Finally, data-driven results with student stakeholders' inputs justifiably espouse relevant theoretical propositions and pedagogical model or lessons for both teacher-practitioners and academic researchers to try in different setting, in other courses, and/or to investigate further.

Does Identification Influence Continuous E-Commerce Consumption? The Mediating Role of Intrinsic Motivations

The motivation behind online consumption behavior is different from that of online social behavior, and research is lacking regarding the impact of identification on e-commerce consumption. The current research examines the influence of identification, which is perceived anonymity, and intrinsic motivation on the continuous purchasing behaviors on retailing e-commerce websites based on self-determination theory. The mediating role of intrinsic motivation was also empirically tested from a sample of 661 frequent consumers using the partial least squares approach. The findings were: (1) Identification negatively influences perceived anonymity, and its low, but significantly positive, influence on continuous e-commerce consumption were totally mediated by perceived competence, perceived autonomy, and perceived relatedness. (2) Perceived anonymity positively influences self-determination factors, which has partly mediating impact between perceived anonymity and continuous consumption. (3) The authenticity and concealment of identity are based on different mechanisms, but both of them are conducive to promoting continuous purchases. On retailing e-commerce websites, customers’ identity management should consider both identification in the background and anonymity perception in the service, and the contributions of the service to promote consumers’ perceived competence and perceived autonomy are important in continuous consumption.

A model for meme popularity growth in social networking systems based on biological principle and human interest dynamics.

We analyze five big data sets from a variety of online social networking (OSN) systems and find that the growth dynamics of meme popularity exhibit characteristically different behaviors. For example, there is linear growth associated with online recommendation and sharing platforms, a plateaued (or an "S"-shape) type of growth behavior in a web service devoted to helping users to collect bookmarks, and an exponential increase on the largest and most popular microblogging website in China. Does a universal mechanism with a common set of dynamical rules exist, which can explain these empirically observed, distinct growth behaviors? We provide an affirmative answer in this paper. In particular, inspired by biomimicry to take advantage of cell population growth dynamics in microbial ecology, we construct a base growth model for meme popularity in OSNs. We then take into account human factors by incorporating a general model of human interest dynamics into the base model. The final hybrid model contains a small number of free parameters that can be estimated purely from data. We demonstrate that our model is universal in the sense that, with a few parameters estimated from data, it can successfully predict the distinct meme growth dynamics. Our study represents a successful effort to exploit principles in biology to understand online social behaviors by incorporating the traditional microbial growth model into meme popularity. Our model can be used to gain insights into critical issues such as classification, robustness, optimization, and control of OSN systems.

The Influence of Online Social Marketing Behavior on Consumers' Purchase Intention

The development of mobile social e-commerce in China is not smooth, and the marketing behavior of social marketers is a key factor to the success of social marketing. In this study, data were collected by questionnaire survey, and the collected data were processed by SPSS19.0 software. On the surface, goodwill trust and capability trust have a positive impact on consumers' purchase intention, and goodwill trust has the biggest impact. Relationship maintenance and marketing information sharing positively affect customer trust. The innovation of this paper lies in the new exploration of how social marketing behaviors affect consumers' purchase intention, and the research has achieved certain results.

Group Event Recommendation in Event-Based Social Networks Considering Unexperienced Events

As users in event-based social networks (EBSNs) usually participate in events together with friends, classmates, colleagues, or family members, recommending events to a group of users is attracting increasing attention in EBSNs recent years. However, existing studies are lack of attention to the fact that groups in EBSNs may have potential desires for participating in the unexperienced events. In order to deal with the challenges, including mining implicit friendships between users, simulating the consulting process between users and their friends outside the groups, and simulating the negotiating process among members inside the groups, we propose a t wo- p hase g roup e vent r ecommendation (2PGER) model for EBSNs. First, we leverage information, such as online social behaviors, users’ event participation records, and topological structures of EBSNs to establish a global trust network among users and establish egotrust networks of all users. Then, we perform random walks on the pre-built egotrust network for each user to acquire the user’s predicted preferences on the unexperienced events. Third, we adopt a random walk with restarts (RWR) method to aggregate users’ preferences and recommend top N events to groups. In the end, we compare 2PGER with several baseline approaches on real datasets from Meetup. The results show that 2PGER outperforms baselines.

Do personality characteristics explain the associations between self-esteem and online social networking behaviour?

The relationships between online social networking (OSN) behaviour and users' self-esteem are as important as well as ambiguous: Both positive and negative self-esteem can encourage users to engage in OSNs. This work examined whether personality traits and attitudes toward traits can explain this controversy. Data from 830 users of Vk.com, a Russian OSN were analysed. I hypothesised that extraversion and attitudes toward extraversion eliminated correlations between positive self-esteem and users' popularity (the number of friends and likes). In contrast, neuroticism and attitudes toward neuroticism failed to eliminate a negative correlation between self-esteem and an indicator of users' self-validation (the number of impersonal avatars). This association also remained significant when conscientiousness as well as negative attitudes toward conscientiousness and agreeableness were controlled. However, self-esteem did not correlate with the two other self-validation indicators―the number of posts and portraits. This study casts doubt on the possibility of direct associations between positive self-esteem and users' popularity beyond such factors as extraversion. Nevertheless, it lends partial support to the association between negative self-esteem and users' self-validation such as the use of impersonal avatars even when other personality characteristics are considered.

Building online citizen engagement for enhancing emergency management in local European government

PurposeThe purpose of this paper is to examine European citizen engagement for enhancing emergency management and, more specifically, in the context of the terrorist attacks which occurred in Paris, France on November 15, 2015. To do so, two main research questions are raised. First, are there differences in the levels of citizen engagement between the country affected, France, and other European countries? Second, what factors foster a high level of citizen engagement in France?Design/methodology/approachFirst, a comparative content analysis of the Facebook pages of local governments in France and other capital cities of the European Union (EU) was carried. Second, a multivariate regression analysis was performed.FindingsAlthough the level of online citizen engagement was greater in France than in the other EU cities analyzed, similarities were detected in the messages sent, responses and moment of participation. Moreover, there are certain types of online social behavior that encourage interactive conversations among citizens as well as between citizens and their local governments.Practical implicationsThis research enables local governments to understand the similarities and differences between citizens and local governments from the affected country and those from outside it when using social media to engage in emergency management. It also provides further insight for managers of local governments in the country affected with regards to the need to be aware of the influence of online collective behavior that emerges from the information they publish. As a result, the attainment of a high level of citizens’ participation in their social media can differ.Originality/valueThis paper advances in the scarce knowledge of high levels of online engagement (conversational interactions) in emergency situations.

A Comprehensive Study on Social Network Mental Disorders Detection via Online Social Media Mining

The explosive growth in popularity of social networking leads to the problematic usage. An increasing number of social network mental disorders (SNMDs), such as Cyber-Relationship Addiction, Information Overload, and Net Compulsion, have been recently noted. Symptoms of these mental disorders are usually observed passively today, resulting in delayed clinical intervention. In this paper, we argue that mining online social behavior provides an opportunity to actively identify SNMDs at an early stage. It is challenging to detect SNMDs because the mental status cannot be directly observed from online social activity logs. Our approach, new and innovative to the practice of SNMD detection, does not rely on self-revealing of those mental factors via questionnaires in Psychology. Instead, we propose a machine learning framework, namely, Social Network Mental Disorder Detection (SNMDD) , that exploits features extracted from social network data to accurately identify potential cases of SNMDs. We also exploit multi-source learning in SNMDD and propose a new SNMD-based Tensor Model (STM) to improve the accuracy. To increase the scalability of STM, we further improve the efficiency with performance guarantee. Our framework is evaluated via a user study with 3,126 online social network users. We conduct a feature analysis, and also apply SNMDD on large-scale datasets and analyze the characteristics of the three SNMD types. The results manifest that SNMDD is promising for identifying online social network users with potential SNMDs.

A Comparison of Psychological and Socio-economic Factors Affecting Individual User’s Social Media Behaviour

Companies and business managers know about social media as a phenomenon and its power as a business tool. In various earlier researches many viewpoints, theories and models on online behaviour of individual consumers have been put across and build upon from time to time. However, keeping in mind the exponential growth in digital transactions in India, the digital platform managers of various companies are still data hungry and want data analytics to be done to understand various parameters affecting consumer black box. The focus of this study is on comparison of the psychological and socio-economic factors which affect the behaviour of individual user on social media. A model is proposed here by integrating psychological factors, social media behaviour elements and economic factors, and efforts to predict the impact for the social media usage are made. This research contributes by broadening the paradigm of various learning theories and predicts the online social media usage attitude development factors. This study in particular presents a different point of view about online social media behaviour by examining the effect of psychological and socio-economic elements and the moderating role of individual characteristics. It is an attempt by authors to empirically integrate and understand empirically, and draw a comparison between the impact of psychological and socio-economic factors that influence the consumers’ social media behaviour elements depending on their ability to affect the consumer’s attitude.

Size does not matter - in the virtual world. Comparing online social networking behaviour with business success of entrepreneurs

We explore what benefits network position in online business social networks like LinkedIn might confer to an aspiring entrepreneur. We compare two network attributes, size and embeddedness, and two actor attributes, location and diversity, between virtual and real-world networks. The promise of social networks like LinkedIn is that network friends enable easier access to critical resources such as legal and financial services, customers, and business partners. Our setting consists of one million public member profiles of the German business networking site XING (a German version of LinkedIn) from which we extracted the network structure of 15,000 start-up entrepreneurs from 12 large German universities. We find no positive effect of virtual network size and embeddedness, and small positive effects of location and diversity.

Research on Web Search Behavior: How Online Query Data Inform Social Psychology.

The widespread use of web searches in daily life has allowed researchers to study people's online social and psychological behavior. Using web search data has advantages in terms of data objectivity, ecological validity, temporal resolution, and unique application value. This review integrates existing studies on web search data that have explored topics including sexual behavior, suicidal behavior, mental health, social prejudice, social inequality, public responses to policies, and other psychosocial issues. These studies are categorized as descriptive, correlational, inferential, predictive, and policy evaluation research. The integration of theory-based hypothesis testing in future web search research will result in even stronger contributions to social psychology.

How Social Are Social Media? A Review of Online Social Behaviour and Connectedness

The use of social media is rapidly increasing, and one of the major discussions of the 21st century revolves around how the use of these applications will impact on the social relationships of users. To contribute to this discussion, we present a brief narrative review highlighting the advantages and disadvantages of social media use on three key aspects of social connectedness: social capital, sense of community, and loneliness. The results indicate that using social media can increase social capital, lead to the formation of friendships and communities, and reduce loneliness. However, some social media site users may experience weakening friendships, online ostracism, and heightened loneliness. Therefore, we argue that the use of social media has contradictory effects on social connectedness. Moreover, the direction of these outcomes is contingent upon who is using the site and how they are using it. Based on these arguments, possible directions for future research are discussed. It is recommended that discourse be continued relating to the association between online social behaviour and connectedness, as this will enable researchers to establish whether the positive outcomes of social media use outweigh the negative.

Elements of satisfactory online asynchronous teacher behaviour in higher education

In this study, differences were analysed between two groups of online teachers in a Master of Special Educational Needs program. One group scored high on student satisfaction and the second group received low student satisfaction ratings. Findings indicate that high satisfaction is associated with relatively long and pedagogically complex messages that are most often addressed to the whole group. These messages are characterised by careful listening to the students, elaborate on content knowledge, and show online personality and social behaviour. The research resulted in preliminary guidelines for crafting asynchronous teacher messages that positively affect student satisfaction and a scoring guideline that can be used to score the quality of online teaching as expressed in online asynchronous messages.