Measuring Social Influence in Online Social Networks - Focus on Human Behavior Analytics

With the advent of online social networks (OSN) and their ever-expanding reach, researchers seek to determine a social media user’s social influence (SI) proficiency. Despite its exploding application across multiple domains, the research confronts unprecedented practical challenges due to a lack of systematic examination of human behavior characteristics that impart social influence. This work aims to give a methodical overview by conducting a targeted literature analysis to appraise the accuracy and usefulness of past publications. The finding suggests that first, it is necessary to incorporate behavior analytics into statistical measurement models. Second, there is a severe imbalance between the abundance of theoretical research and the scarcity of empirical work to underpin the collective psychological theories to macro-level predictions. Thirdly, it is crucial to incorporate human sentiments and emotions into any measure of SI, particularly as OSN has endowed everyone with the intrinsic ability to influence others. The paper also suggests the merits of three primary research horizons for future considerations

The Darkside of Online Social Networks: Measuring the Negative Effects of Social Influence in Online Social Networks

This research conceptualizes and develops a scale of Susceptibility to Social Influence in the context of Online Social Networks such as Facebook or Instagram. Three studies find support for the conceptualization and for a valid and reliable scale. Next steps for scale development and its future application are discussed

Online Social Networks’ Investigations of Individuals’ Healthy and Unhealthy Lifestyle Behaviors and Social Factors Influencing Them —Three Essays

More than half of U.S. adults suffer from one or more chronic diseases, which account for 86% of total U.S. healthcare costs. Major contributors to chronic diseases are unhealthy lifestyle behaviors, which include lack of physical activity, poor nutrition, tobacco use, and drinking too much alcohol. A reduction in the prevalence of health-risk behaviors could improve individuals’ longevity and quality of life and may halt the exponential growth of healthcare costs. Prior studies in the field have acknowledged that a comprehensive understanding of health behaviors requires the examination of individual’ behaviors in supra-dyadic social networks. In recent years, the growth of online social networks and popularity of location-based services have opened new research opportunities for observational studies on individuals’ healthy and unhealthy lifestyle behaviors. The goal of this three-essay dissertation is to examine the effect of various social factors, shared images, and communities of interest on healthy and unhealthy lifestyle behaviors of individuals. This dissertation makes novel contributions in terms of theoretical implications, data collection and analysis methods, and policy implications for promoting healthy lifestyle behaviors and inhibiting unhealthy behaviors. Essay 1 draws on a synthesis of social cognitive and social network theories to conceptualize a causal model for healthy and unhealthy behaviors. To test the conceptualized model, we developed a new method—dynamic sequential data extraction and integration—to collect and integrate data over time from Twitter and Foursquare. The captured dataset was then combined with relevant data from the U.S. Census Bureau. The final dataset has more than 32,000 individuals from all states in the United States. Using this dataset, we derived variables to measure healthy and unhealthy lifestyle behaviors and metrics for factors representing individuals’ social support, social influence, and homophily, as well as the socioeconomic status of the communities where they live. To capture the impacts of social factors, we collected individuals’ behaviors in two separate time periods. We used zero-inflated negative binomial regression method for data analysis. The results of this study uncover factors that have significant impacts on healthy and unhealthy lifestyle behaviors. Essay 2 focuses on embedded images in self-disclosed posts related to healthy and unhealthy lifestyle behaviors. While online photo-sharing has become widely popular, and neuroscience has reported the influence of images in brain activities, to our knowledge, there is no published research on the impacts of shared photos on health-related lifestyle behaviors. This study addresses this gap and examines the moderating role of shared images and the direct impacts of their contents. We relied on social learning and multimodality theories to argue that images can attract individuals’ attention and enhance the process of observational learning in online social networks. We developed a novel method for image analysis that involves the extraction, processing, dimensionality reduction, and categorization of images. The results show that the presence of photos in self-disclosed unhealthy lifestyle behaviors positively moderates friends’ social influence. Moreover, the results indicate that the contents of shared photos influence individuals’ health-related behaviors. Essay 3 focuses on the role of personal interests in individuals’ health-related lifestyle behaviors. Prior studies have demonstrated that health promotional programs can benefit from targeting individuals based on their interests. Specifically, prior studies have emphasized the role of interests as a factor influencing behaviors. However, current literature suffers from two major gaps. First, there is no systematic and comprehensive approach to capture individuals’ interests in online social networks. Second, to our knowledge, the role of interests in individuals’ healthy and unhealthy lifestyle behaviors as disclosed online has not been investigated. To address these gaps, we examine the role of individuals’ interests in their health-related behaviors. The theoretical foundation of this study is a synthesis of homophily and self-determination theories. We developed a novel method—the homophily-based interest detection method—that involves network simplification, network clustering, cluster labeling, and interest metrics. This method was applied to social networks of individuals in Essay 1 to measure individuals’ interests. The results show that health-related interests are associated with individuals’ healthy and unhealthy lifestyle behaviors. Our findings indicate that other forms of interest, such as music taste and political views, also play a role. Moreover, our results show that belonging to healthy (unhealthy) communities of interest has an inhibitive role that prevents postings of unhealthy (healthy) behaviors

Use of a simulated directional social network to compare measures of user influence

This paper proposes a new method for measuring user influence in directional social networks, derived from the works of Reilly et al. and Cha et al. The method being proposed in this paper considers an element from each of the two works. The first is the ratio of ‘messages forwarded’ over ‘messages posted’. The second element is the size of the audience. The second part of this study entails modeling and simulating an online social network. Using a data sample from the Twitter network to implement the simulation, it is going to allow us to compare the methods that are used to measure influence. The behaviors modeled include the act of gaining a follower, the act of creating a message, and the act of forwarding a message. These are the three behaviors we are using to compute influence

COMMUNITY DETECTION AND INFLUENCE MAXIMIZATION IN ONLINE SOCIAL NETWORKS

The detecting and clustering of data and users into communities on the social web are important and complex issues in order to develop smart marketing models in changing and evolving social ecosystems. These marketing models are created by individual decision to purchase a product and are influenced by friends and acquaintances. This leads to novel marketing models, which view users as members of online social network communities, rather than the traditional view of marketing to individuals. This thesis starts by examining models that detect communities in online social networks. Then an enhanced approach to detect community which clusters similar nodes together is suggested. Social relationships play an important role in determining user behavior. For example, a user might purchase a product that his/her friend recently bought. Such a phenomenon is called social influence and is used to study how far the action of one user can affect the behaviors of others. Then an original metric used to compute the influential power of social network users based on logs of common actions in order to infer a probabilistic influence propagation model. Finally, a combined community detection algorithm and suggested influence propagation approach reveals a new influence maximization model by identifying and using the most influential users within their communities. In doing so, we employed a fuzzy logic based technique to determine the key users who drive this influence in their communities and diffuse a certain behavior. This original approach contrasts with previous influence propagation models, which did not use similarity opportunities among members of communities to maximize influence propagation. The performance results show that the model activates a higher number of overall nodes in contemporary social networks, starting from a smaller set of key users, as compared to existing landmark approaches which influence fewer nodes, yet employ a larger set of key users

DO I DESERVE TO SPEND? SOCIAL SUPPORT AND SPENDING PLEASURE

Despite evidence of people posting their consumption experience on online social networks to fulfill the needs of social support, a systemic understanding of how social support obtained via online social networks affects post-consumption behaviors related to spending remains elusive. This dissertation aims to answer the question of how social support via online social networks affects consumer’s post-consumption behavior by investigating in what form and from whom consumers obtain online social support. To do so, the purpose of this dissertation is to examine how online social support from others influences perceptions of deservingness which then influences spending pleasure. This dissertation focuses on two types of social support sources, social support from friends and social support from the firm. In addition, this dissertation examines the role of relational factors (e.g., tie-strength with Facebook friends and relationship strength with firm) and a situational factor (e.g., social support aimed at others) that may influence the impact of social support on spending pleasure. This dissertation consists of two studies. In Study 1, a 2 (Social support; low vs. high) x 2 (Tie strength: strong vs. weak) x 2 (Self-construal: independent vs. interdependent) quasi-experimental between-subjects design is utilized, self-construal serving as a measured factor. A 2 (Social support; present vs. absent) x 2 (Relationship strength: strong vs. weak) x 2 (Social support aimed at others: present vs. absent) between-subjects factorial experiment is used for study 2. Across two studies, this research provides evidence that social support gained through online social networks influences consumers’ spending pleasure through perceptions of their own deservingness. More specifically, when people obtain social support from others on their consumption related post, they feel more deserving which then enhances their spending pleasure from that consumption. Notably, this study reveals that people obtain social support in online social networks through receiving ‘Likes’ and ‘Comments’ on their post. Furthermore, this result advances our knowledge of online social networks by demonstrating that not only the social networks friends but also firms can be social support sources by actively responding to customers’ post. In addition, this study also explores boundary conditions for when online social support is more effective on spending pleasure. The findings from two studies address the benefit to the service industry by understanding how social support can enhance spending pleasure. In addition, this dissertation may broaden the social support literature by highlighting the function of like and Comments, a new form of social support that are provided in the context of online social networks

Three Essays on Individuals’ Vulnerability to Security Attacks in Online Social Networks: Factors and Behaviors

With increasing reliance on the Internet, the use of online social networks (OSNs) for communication has grown rapidly. OSN platforms are used to share information and communicate with friends and family. However, these platforms can pose serious security threats to users. In spite of the extent of such security threats and resulting damages, little is known about factors associated with individuals’ vulnerability to online security attacks. We address this gap in the following three essays. Essay 1 draws on a synthesis of the epidemic theory in infectious disease epidemiology with the social capital theory to conceptualize factors that contribute to an individual’s role in security threat propagation in OSN. To test the model, we collected data and created a network of hacked individuals over three months from Twitter. The final hacked network consists of over 8000 individual users. Using this data set, we derived individual’s factors measuring threat propagation efficacy and threat vulnerability. The dependent variables were defined based on the concept of epidemic theory in disease propagation. The independent variables are measured based on the social capital theory. We use the regression method for data analysis. The results of this study uncover factors that have significant impact on threat propagation efficacy and threat vulnerability. We discuss the novel theoretical and managerial contributions of this work. Essay 2 explores the role of individuals’ interests in their threat vulnerability in OSNs. In OSNs, individuals follow social pages and post contents that can easily reveal their topics of interest. Prior studies show high exposure of individuals to topics of interest can decrease individuals’ ability to evaluate the risks associated with their interests. This gives attackers a chance to target people based on what they are interested in. However, interest-based vulnerability is not just a risk factor for individuals themselves. Research has reported that similar interests lead to friendship and individuals share similar interests with their friends. This similarity can increase trust among friends and makes individuals more vulnerable to security threat coming from their friends’ behaviors. Despite the potential importance of interest in the propagation of online security attacks online, the literature on this topic is scarce. To address this gap, we capture individuals’ interests in OSN and identify the association between individuals’ interests and their vulnerability to online security threats. The theoretical foundation of this work is a synthesis of dual-system theory and the theory of homophily. Communities of interest in OSN were detected using a known algorithm. We test our model using the data set and social network of hacked individuals from Essay 1. We used this network to collect additional data about individuals’ interests in OSN. The results determine communities of interests which were associated with individuals’ online threat vulnerability. Moreover, our findings reveal that similarities of interest among individuals and their friends play a role in individuals’ threat vulnerability in OSN. We discuss the novel theoretical and empirical contributions of this work. Essay 3 examines the role addiction to OSNs plays in individuals’ security perceptions and behaviors. Despite the prevalence of problematic use of OSNs and the possibility of addiction to these platforms, little is known about the functionalities of brain systems of users who suffer from OSN addiction and their online security perception and behaviors. In addressing these gaps, we have developed the Online addiction & security behaviors (OASB) theory by synthesizing dual-system theory and extended protection motivation theory (PMT). We collected data through an online survey. The results indicate that OSN addiction is rooted in the individual’s brain systems. For the OSN addicted, there is a strong cognitive-emotional preoccupation with using OSN. Our findings also reveal the positive and significant impact of OSN addiction on perceived susceptibility to and severity of online security threats. Moreover, our results show the negative association between OSN addiction and perceived self-efficacy. We discuss the theoretical and practical implications of this work

Time-Dependent Influence Measurement in Citation Networks

In every scientific discipline, researchers face two common dilemmas: where to find bleeding-edge papers and where to publish their own articles. We propose to answer these questions by looking at the influence between communities, e.g. conferences or journals. The influential conferences are those which papers are heavily cited by other conferences, i.e. they are visible, significant and inspiring. For the task of finding such influential places-to-publish, we introduce a Running Influence model that aims to discover pairwise influence between communities and evaluate the overall influence of each considered community. We have taken into consideration time aspects such as intensity of papers citations over time and difference of conferences starting years. The community influence analysis is tested on real-world data of Computer Science conferences

Using Online Social Network Technology To Increase Social Support For Physical Activity: The Internet Support For Healthy Associations Promoting Exercise (INSHAPE) Study

Online social networks, such as Facebook, have extensive reach and possess technology that could foster social support, an established determinant of physical activity. The purpose of this study was to design and test the efficacy and feasibility of a physical activity social support intervention primarily delivered through Facebook. In aim 1 of this study, formative interviews (n=15) were conducted with female undergraduates to inform the online social network intervention design and explore behavior and perceptions related to the exchange of social support for physical activity through Facebook. In aim 2, we conducted a randomized controlled intervention trial comparing two groups of female undergraduates; education controls receiving access to an exercise focused website (n=67) and intervention participants receiving access to the same website with physical activity self-monitoring and enrollment in a physical activity themed Facebook group (n=67). Physical activity, perceived social support for physical activity, and psychosocial mediators were assessed using previously validated questionnaires. Facebook interactions were recorded during the intervention. In Aim 3, we conducted interviews (n=9) and a survey (n=120) with intervention participants to assess the acceptability of the intervention and participants' perceptions of physical activity social support exchanged through Facebook. Results from the trial revealed no statistically significant differences between groups over time on perceived social support or physical activity. More than half (55%) of intervention participants indicated that they would recommend the program to friends. A path analysis examining the relationships between social support, psychosocial mediators, and physical activity among all participants found a significant indirect effect for companionship social support on physical activity mediated by intention (.09, p=.02). The majority of Facebook social support interactions collected during the intervention were classified as companionship. Qualitative analysis of formative and process interviews found that participants who received social support for physical activity through Facebook thought it was valuable. The results from this study indicate that participants will join and exchange important types of social support for physical activity using online social networks. More research is needed to determine if online social network interventions can effectively increase social support or physical activity.Doctor of Philosoph