Matching Items (6)
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- All Subjects: Interpersonal communication
- All Subjects: Interpersonal relations
- Creators: Kambhampati, Subbarao
- Creators: Kassing, Jeffrey W.
Description
In most social networking websites, users are allowed to perform interactive activities. One of the fundamental features that these sites provide is to connecting with users of their kind. On one hand, this activity makes online connections visible and tangible; on the other hand, it enables the exploration of our connections and the expansion of our social networks easier. The aggregation of people who share common interests forms social groups, which are fundamental parts of our social lives. Social behavioral analysis at a group level is an active research area and attracts many interests from the industry. Challenges of my work mainly arise from the scale and complexity of user generated behavioral data. The multiple types of interactions, highly dynamic nature of social networking and the volatile user behavior suggest that these data are complex and big in general. Effective and efficient approaches are required to analyze and interpret such data. My work provide effective channels to help connect the like-minded and, furthermore, understand user behavior at a group level. The contributions of this dissertation are in threefold: (1) proposing novel representation of collective tagging knowledge via tag networks; (2) proposing the new information spreader identification problem in egocentric soical networks; (3) defining group profiling as a systematic approach to understanding social groups. In sum, the research proposes novel concepts and approaches for connecting the like-minded, enables the understanding of user groups, and exposes interesting research opportunities.
ContributorsWang, Xufei (Author) / Liu, Huan (Thesis advisor) / Kambhampati, Subbarao (Committee member) / Sundaram, Hari (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
ABSTRACT
This study examines the ways in which employees experience moral emotions that violate employee treatment and how employees co-construct moral emotions and subsequent expressions of dissent. This qualitative study consisted of 123 full-time employees and utilized open-coding, content analysis, constant comparison analysis, and concept mapping. The analysis revealed that employees expressed dissent laterally as a series of sensemaking processes, such as validation of feelings, moral assessments, and assessing the fear of moral transgressions. Employees also expressed dissent as a series of risk assessments that overlapped with the ways in which employees made sense of the perceived infraction. Employees' lateral dissent expression manifested as a form of social support which occasionally led to co-rumination. Employees expressed dissent upwardly when seeking a desired action or change. Circumvention was utilized as a direct reflection to the type and degree of moral transgression related to the person responsible for the mistreatment. Results indicated that experiencing moral emotions that led to expressing dissent with a designated audience was determined by where employees were situated in the cyclical model of communicating moral emotions and in relation to the co-construction of both the infraction related to employee mistreatment and the experience of moral emotions. Results contribute to the existing body of literature on dissent and emotions. A discussion synthesizing the findings and analysis is presented, in addition to the implications for future research.
KEYWORDS: Emotion, Dissent, Moral Emotions, Sensemaking, Risk-Assessment, Social Support, Co-Rumination
This study examines the ways in which employees experience moral emotions that violate employee treatment and how employees co-construct moral emotions and subsequent expressions of dissent. This qualitative study consisted of 123 full-time employees and utilized open-coding, content analysis, constant comparison analysis, and concept mapping. The analysis revealed that employees expressed dissent laterally as a series of sensemaking processes, such as validation of feelings, moral assessments, and assessing the fear of moral transgressions. Employees also expressed dissent as a series of risk assessments that overlapped with the ways in which employees made sense of the perceived infraction. Employees' lateral dissent expression manifested as a form of social support which occasionally led to co-rumination. Employees expressed dissent upwardly when seeking a desired action or change. Circumvention was utilized as a direct reflection to the type and degree of moral transgression related to the person responsible for the mistreatment. Results indicated that experiencing moral emotions that led to expressing dissent with a designated audience was determined by where employees were situated in the cyclical model of communicating moral emotions and in relation to the co-construction of both the infraction related to employee mistreatment and the experience of moral emotions. Results contribute to the existing body of literature on dissent and emotions. A discussion synthesizing the findings and analysis is presented, in addition to the implications for future research.
KEYWORDS: Emotion, Dissent, Moral Emotions, Sensemaking, Risk-Assessment, Social Support, Co-Rumination
ContributorsKamrath, Jessica K (Author) / Kassing, Jeffrey W. (Thesis advisor) / Waldron, Vincent R. (Committee member) / Meân, Lindsey J. (Committee member) / Arizona State University (Publisher)
Created2015
Description
The current study is the first qualitative investigation aimed solely at understanding what it means to communicate conditional forgiveness in serious romantic relationships. Conditional forgiveness is forgiveness that has been offered with the stipulation that the errant behavior cease. It is a provocative topic because some argue genuine forgiveness is not conditional, but recent discoveries that have associated its use with severe transgressions and relational deterioration suggest it is a critical site for investigation. This inductive analysis of open-ended data from 201 anonymous surveys identified both distinctions between and intersections of conditional forgiveness, forgiveness, and reconciliation. A relational dialectics analysis also revealed that reconcilable-irreconcilable was the overarching tension for conditional forgivers and six additional tensions also were also discovered: individual identity-couple identity, safety-risk, certainty-uncertainty, mercy-justice, heart-mind, and expression-suppression. Of particular intrigue, the current analysis supports the previous discovery of implicit conditional forgiveness--suppressing conditions, sometimes in response to physical and substance abuse. Ultimately, the current analysis contributes to the enduring conversation aimed at understanding the communication and pursuit of forgiveness and reconciliation. It addresses one of the basic instincts and paradoxes of existing with others--the balance between vulnerability and protection.
ContributorsKloeber, Dayna Noel (Author) / Waldron, Vincent R. (Thesis advisor) / Kelley, Douglas L. (Committee member) / Kassing, Jeffrey W. (Committee member) / Fisher, Carla L. (Committee member) / Arizona State University (Publisher)
Created2011
Description
With the rise of social media, user-generated content has become available at an unprecedented scale. On Twitter, 1 billion tweets are posted every 5 days and on Facebook, 20 million links are shared every 20 minutes. These massive collections of user-generated content have introduced the human behavior's big-data.
This big data has brought about countless opportunities for analyzing human behavior at scale. However, is this data enough? Unfortunately, the data available at the individual-level is limited for most users. This limited individual-level data is often referred to as thin data. Hence, researchers face a big-data paradox, where this big-data is a large collection of mostly limited individual-level information. Researchers are often constrained to derive meaningful insights regarding online user behavior with this limited information. Simply put, they have to make thin data thick.
In this dissertation, how human behavior's thin data can be made thick is investigated. The chief objective of this dissertation is to demonstrate how traces of human behavior can be efficiently gleaned from the, often limited, individual-level information; hence, introducing an all-inclusive user behavior analysis methodology that considers social media users with different levels of information availability. To that end, the absolute minimum information in terms of both link or content data that is available for any social media user is determined. Utilizing only minimum information in different applications on social media such as prediction or recommendation tasks allows for solutions that are (1) generalizable to all social media users and that are (2) easy to implement. However, are applications that employ only minimum information as effective or comparable to applications that use more information?
In this dissertation, it is shown that common research challenges such as detecting malicious users or friend recommendation (i.e., link prediction) can be effectively performed using only minimum information. More importantly, it is demonstrated that unique user identification can be achieved using minimum information. Theoretical boundaries of unique user identification are obtained by introducing social signatures. Social signatures allow for user identification in any large-scale network on social media. The results on single-site user identification are generalized to multiple sites and it is shown how the same user can be uniquely identified across multiple sites using only minimum link or content information.
The findings in this dissertation allows finding the same user across multiple sites, which in turn has multiple implications. In particular, by identifying the same users across sites, (1) patterns that users exhibit across sites are identified, (2) how user behavior varies across sites is determined, and (3) activities that are observed only across sites are identified and studied.
This big data has brought about countless opportunities for analyzing human behavior at scale. However, is this data enough? Unfortunately, the data available at the individual-level is limited for most users. This limited individual-level data is often referred to as thin data. Hence, researchers face a big-data paradox, where this big-data is a large collection of mostly limited individual-level information. Researchers are often constrained to derive meaningful insights regarding online user behavior with this limited information. Simply put, they have to make thin data thick.
In this dissertation, how human behavior's thin data can be made thick is investigated. The chief objective of this dissertation is to demonstrate how traces of human behavior can be efficiently gleaned from the, often limited, individual-level information; hence, introducing an all-inclusive user behavior analysis methodology that considers social media users with different levels of information availability. To that end, the absolute minimum information in terms of both link or content data that is available for any social media user is determined. Utilizing only minimum information in different applications on social media such as prediction or recommendation tasks allows for solutions that are (1) generalizable to all social media users and that are (2) easy to implement. However, are applications that employ only minimum information as effective or comparable to applications that use more information?
In this dissertation, it is shown that common research challenges such as detecting malicious users or friend recommendation (i.e., link prediction) can be effectively performed using only minimum information. More importantly, it is demonstrated that unique user identification can be achieved using minimum information. Theoretical boundaries of unique user identification are obtained by introducing social signatures. Social signatures allow for user identification in any large-scale network on social media. The results on single-site user identification are generalized to multiple sites and it is shown how the same user can be uniquely identified across multiple sites using only minimum link or content information.
The findings in this dissertation allows finding the same user across multiple sites, which in turn has multiple implications. In particular, by identifying the same users across sites, (1) patterns that users exhibit across sites are identified, (2) how user behavior varies across sites is determined, and (3) activities that are observed only across sites are identified and studied.
ContributorsZafarani, Reza, 1983- (Author) / Liu, Huan (Thesis advisor) / Kambhampati, Subbarao (Committee member) / Xue, Guoliang (Committee member) / Leskovec, Jure (Committee member) / Arizona State University (Publisher)
Created2015
Description
The rapid advancements of technology have greatly extended the ubiquitous nature of smartphones acting as a gateway to numerous social media applications. This brings an immense convenience to the users of these applications wishing to stay connected to other individuals through sharing their statuses, posting their opinions, experiences, suggestions, etc on online social networks (OSNs). Exploring and analyzing this data has a great potential to enable deep and fine-grained insights into the behavior, emotions, and language of individuals in a society. This proposed dissertation focuses on utilizing these online social footprints to research two main threads – 1) Analysis: to study the behavior of individuals online (content analysis) and 2) Synthesis: to build models that influence the behavior of individuals offline (incomplete action models for decision-making).
A large percentage of posts shared online are in an unrestricted natural language format that is meant for human consumption. One of the demanding problems in this context is to leverage and develop approaches to automatically extract important insights from this incessant massive data pool. Efforts in this direction emphasize mining or extracting the wealth of latent information in the data from multiple OSNs independently. The first thread of this dissertation focuses on analytics to investigate the differentiated content-sharing behavior of individuals. The second thread of this dissertation attempts to build decision-making systems using social media data.
The results of the proposed dissertation emphasize the importance of considering multiple data types while interpreting the content shared on OSNs. They highlight the unique ways in which the data and the extracted patterns from text-based platforms or visual-based platforms complement and contrast in terms of their content. The proposed research demonstrated that, in many ways, the results obtained by focusing on either only text or only visual elements of content shared online could lead to biased insights. On the other hand, it also shows the power of a sequential set of patterns that have some sort of precedence relationships and collaboration between humans and automated planners.
A large percentage of posts shared online are in an unrestricted natural language format that is meant for human consumption. One of the demanding problems in this context is to leverage and develop approaches to automatically extract important insights from this incessant massive data pool. Efforts in this direction emphasize mining or extracting the wealth of latent information in the data from multiple OSNs independently. The first thread of this dissertation focuses on analytics to investigate the differentiated content-sharing behavior of individuals. The second thread of this dissertation attempts to build decision-making systems using social media data.
The results of the proposed dissertation emphasize the importance of considering multiple data types while interpreting the content shared on OSNs. They highlight the unique ways in which the data and the extracted patterns from text-based platforms or visual-based platforms complement and contrast in terms of their content. The proposed research demonstrated that, in many ways, the results obtained by focusing on either only text or only visual elements of content shared online could lead to biased insights. On the other hand, it also shows the power of a sequential set of patterns that have some sort of precedence relationships and collaboration between humans and automated planners.
ContributorsManikonda, Lydia (Author) / Kambhampati, Subbarao (Thesis advisor) / Liu, Huan (Committee member) / Li, Baoxin (Committee member) / De Choudhury, Munmun (Committee member) / Kamar, Ece (Committee member) / Arizona State University (Publisher)
Created2019
Description
College sports in America represent a multibillion dollar industry. Recruiting collegiate student-athletes not only is costly for university teams, but is integral for their long-term success. Universities spend substantial amounts of money to recruit student-athletes, yet relatively little academic work has focused on understanding the athletic recruiting process. While NCAA policy regulates when communication is allowed between coaches and student-athletes, there is a lack of literature investigating what the communicative aspects of athletic recruiting entail. Thus, the purpose of this dissertation is to unpack the student-athlete experience of collegiate athletic recruitment. It builds on theoretical work from organizational and interpersonal communication, as well as management and marketing, to extend existing knowledge of student-athletes’ college choice. Specifically, a conceptual model is presented that includes how student-athletes’ expectations and relationships during athletic recruitment contribute to an overall affinity for the university that, in turn, influences choice.
Thirty Division I student-athletes from six different sports participated in focus groups to discuss their recruitment experiences. Taking a grounded theory approach to the focus group transcripts, thematic analysis illuminated what was most memorable for student-athletes about their recruitment, what expectations they had for the process, and what relational benefits they sought when making their college choice decision. Findings reinforced the prominence of communication in the recruitment process, and indicated the importance of interpersonal relationships, authentic communication, and a customized recruiting experience. This work represents the start of a scholarly trajectory which will further conceptualize and test the relational elements of athletic recruiting. Future directions, as well as theoretical and practical implications, are discussed.
Thirty Division I student-athletes from six different sports participated in focus groups to discuss their recruitment experiences. Taking a grounded theory approach to the focus group transcripts, thematic analysis illuminated what was most memorable for student-athletes about their recruitment, what expectations they had for the process, and what relational benefits they sought when making their college choice decision. Findings reinforced the prominence of communication in the recruitment process, and indicated the importance of interpersonal relationships, authentic communication, and a customized recruiting experience. This work represents the start of a scholarly trajectory which will further conceptualize and test the relational elements of athletic recruiting. Future directions, as well as theoretical and practical implications, are discussed.
ContributorsPosteher, Karlee A (Author) / Kassing, Jeffrey W. (Thesis advisor) / Mongeau, Paul A. (Thesis advisor) / Mandel, Naomi (Committee member) / Arizona State University (Publisher)
Created2019