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- All Subjects: Interpersonal communication
- Creators: Liu, Huan
- Creators: Romero, Mary
Description
This thesis deals with the analysis of interpersonal communication dynamics in online social networks and social media. Our central hypothesis is that communication dynamics between individuals manifest themselves via three key aspects: the information that is the content of communication, the social engagement i.e. the sociological framework emergent of the communication process, and the channel i.e. the media via which communication takes place. Communication dynamics have been of interest to researchers from multi-faceted domains over the past several decades. However, today we are faced with several modern capabilities encompassing a host of social media websites. These sites feature variegated interactional affordances, ranging from blogging, micro-blogging, sharing media elements as well as a rich set of social actions such as tagging, voting, commenting and so on. Consequently, these communication tools have begun to redefine the ways in which we exchange information, our modes of social engagement, and mechanisms of how the media characteristics impact our interactional behavior. The outcomes of this research are manifold. We present our contributions in three parts, corresponding to the three key organizing ideas. First, we have observed that user context is key to characterizing communication between a pair of individuals. However interestingly, the probability of future communication seems to be more sensitive to the context compared to the delay, which appears to be rather habitual. Further, we observe that diffusion of social actions in a network can be indicative of future information cascades; that might be attributed to social influence or homophily depending on the nature of the social action. Second, we have observed that different modes of social engagement lead to evolution of groups that have considerable predictive capability in characterizing external-world temporal occurrences, such as stock market dynamics as well as collective political sentiments. Finally, characterization of communication on rich media sites have shown that conversations that are deemed "interesting" appear to have consequential impact on the properties of the social network they are associated with: in terms of degree of participation of the individuals in future conversations, thematic diffusion as well as emergent cohesiveness in activity among the concerned participants in the network. Based on all these outcomes, we believe that this research can make significant contribution into a better understanding of how we communicate online and how it is redefining our collective sociological behavior.
ContributorsDe Choudhury, Munmun (Author) / Sundaram, Hari (Thesis advisor) / Candan, K. Selcuk (Committee member) / Liu, Huan (Committee member) / Watts, Duncan J. (Committee member) / Seligmann, Doree D. (Committee member) / Arizona State University (Publisher)
Created2011
Description
The division of domestic labor has far-reaching implications for "private" life (e.g. relational satisfaction and conflict) and for "public" paid labor (e.g. time and dedication in the workplace and career advancement). Although several theories have been developed and tested, they do not sufficiently explain the consistent findings that women in mixed sex households perform a majority of the domestic labor. Without understanding the causes for differences in task performance, past research encouraging communicative solutions to ameliorate conflict was ineffective in changing task allocation and performance. Therefore, it is necessary to understand theoretical explanations that drive domestic labor behavior to develop effective solutions. The recent integrative theory of the division of domestic labor attempts to explain how individuals interact with household partners to allocate domestic tasks. Recognizing the complexity of the division of domestic labor, the integrative theory considers individual, dyadic, and societal factors that influence task allocation. Because clear differences in task performance have been found in mixed sex households, this study separates sex and gender as distinct variables by considering same-sex roommate relationships, essentially removing sex differences from the living arrangement. Furthermore, this study considers individual threshold levels as described by the integrative theory in order to test the theoretical underpinnings. Specifically, this study is designed to investigate the relationships between individual cleanliness threshold levels and gender, sex, perceptions of satisfaction, equity, and frequency of conflict in same-sex roommate relationships. Results indicate support of the integrative theory of the division of domestic labor. Regarding gender differences, partial support for the theory appeared in that feminine individuals have lower threshold levels than masculine individuals. Regarding sex differences, women possess lower individual threshold levels (i.e. more bothered when a task is undone) compared to men, which likely accounts for why existing research indicates that women spend more time performing domestic tasks. What is more, individuals with higher threshold levels report greater relational satisfaction. Further, individuals whose threshold levels differ from their living partner report lower relational satisfaction and greater conflict frequency. Finally, in terms of equity, both overbenefited and underbenefited individuals experience more conflict than those who feel their relationship is equitable. These results provide theoretical support for the integrative theory of the division of labor. Furthermore, the development and testing of a threshold measure scale can be used practically for future research and for better roommate pairings by universities. In addition, communication scholars, family practitioners and counselors, and universities can apply these theoretically grounded research findings to develop and test strategies to reduce conflict and increase relational satisfaction among roommates and couples.
ContributorsRiforgiate, Sarah (Author) / Alberts, Jess K. (Thesis advisor) / Mongeau, Paul (Thesis advisor) / Roberto, Anthony (Committee member) / Romero, Mary (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 goals of this dissertation were to develop a measurement called the
Empathetic Expressions Scale (EES) for Negative and Positive Events, to evaluate expressions of empathy from the receiver perspective, and to provide initial evidence for empathetic expressions as a separate construct from the empathy experience. A series of studies were conducted using three separately collected sets of data. Through the use of Exploratory Factor Analysis (EFA), the EES for Negative Events and the EES for Positive Events were created from the emerged factors. A five-factor structure emerged for the EES for Negative Events, which include Verbal Affirmation, Experience Sharing, Empathetic Voice, Emotional Reactivity, and Empathetic Touch. This scale was found to have good convergent and discriminant validity through the process of construct validation and good local and model fit through Confirmatory Factor Analysis (CFA). A four-factor structure and two-factor structure emerged for the EES for Positive Events. The four factors include Verbal Affirmation, Experience Sharing, Empathetic Voice, and Emotional Reactivity. The two factors in the second structure include Celebratory Touch and Hugs.The final study focused on evaluating different empathetic expressions from the receiver perspective. From the receiver perspective, the participants rated five types of empathetic expressions in response to negative or positive events disclosure. According to the findings, Emotional Reactivity was rated as the most effective empathetic expression in negative events on both levels of supportiveness and message quality scales whereas Verbal Affirmation received the lowest ratings on both criteria. In positive events, Experience Sharing was evaluated as the most supportive and highest quality message whereas Verbal Affirmation was evaluated the lowest on both criteria. Taken together, the series of studies presented in this dissertation provided evidence for the development and validity of the EES for Negative and Positive Events.
Empathetic Expressions Scale (EES) for Negative and Positive Events, to evaluate expressions of empathy from the receiver perspective, and to provide initial evidence for empathetic expressions as a separate construct from the empathy experience. A series of studies were conducted using three separately collected sets of data. Through the use of Exploratory Factor Analysis (EFA), the EES for Negative Events and the EES for Positive Events were created from the emerged factors. A five-factor structure emerged for the EES for Negative Events, which include Verbal Affirmation, Experience Sharing, Empathetic Voice, Emotional Reactivity, and Empathetic Touch. This scale was found to have good convergent and discriminant validity through the process of construct validation and good local and model fit through Confirmatory Factor Analysis (CFA). A four-factor structure and two-factor structure emerged for the EES for Positive Events. The four factors include Verbal Affirmation, Experience Sharing, Empathetic Voice, and Emotional Reactivity. The two factors in the second structure include Celebratory Touch and Hugs.The final study focused on evaluating different empathetic expressions from the receiver perspective. From the receiver perspective, the participants rated five types of empathetic expressions in response to negative or positive events disclosure. According to the findings, Emotional Reactivity was rated as the most effective empathetic expression in negative events on both levels of supportiveness and message quality scales whereas Verbal Affirmation received the lowest ratings on both criteria. In positive events, Experience Sharing was evaluated as the most supportive and highest quality message whereas Verbal Affirmation was evaluated the lowest on both criteria. Taken together, the series of studies presented in this dissertation provided evidence for the development and validity of the EES for Negative and Positive Events.
ContributorsSuwinyattichaiporn, Tara (Author) / Guerrero, Laura K. (Thesis advisor) / Broome, Benjamin (Committee member) / Romero, Mary (Committee member) / Arizona State University (Publisher)
Created2016
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