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- Creators: Liu, Huan
- Creators: Barrett, The Honors College
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
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
In this thesis we deal with the problem of temporal logic robustness estimation. We present a dynamic programming algorithm for the robust estimation problem of Metric Temporal Logic (MTL) formulas regarding a finite trace of time stated sequence. This algorithm not only tests if the MTL specification is satisfied by the given input which is a finite system trajectory, but also quantifies to what extend does the sequence satisfies or violates the MTL specification. The implementation of the algorithm is the DP-TALIRO toolbox for MATLAB. Currently it is used as the temporal logic robust computing engine of S-TALIRO which is a tool for MATLAB searching for trajectories of minimal robustness in Simulink/ Stateflow. DP-TALIRO is expected to have near linear running time and constant memory requirement depending on the structure of the MTL formula. DP-TALIRO toolbox also integrates new features not supported in its ancestor FW-TALIRO such as parameter replacement, most related iteration and most related predicate. A derivative of DP-TALIRO which is DP-T-TALIRO is also addressed in this thesis which applies dynamic programming algorithm for time robustness computation. We test the running time of DP-TALIRO and compare it with FW-TALIRO. Finally, we present an application where DP-TALIRO is used as the robustness computation core of S-TALIRO for a parameter estimation problem.
ContributorsYang, Hengyi (Author) / Fainekos, Georgios (Thesis advisor) / Sarjoughian, Hessam S. (Committee member) / Shrivastava, Aviral (Committee member) / Arizona State University (Publisher)
Created2013
Description
Twitter, the microblogging platform, has grown in prominence to the point that the topics that trend on the network are often the subject of the news and other traditional media. By predicting trends on Twitter, it could be possible to predict the next major topic of interest to the public. With this motivation, this paper develops a model for trends leveraging previous work with k-nearest-neighbors and dynamic time warping. The development of this model provides insight into the length and features of trends, and successfully generalizes to identify 74.3% of trends in the time period of interest. The model developed in this work provides understanding into why par- ticular words trend on Twitter.
ContributorsMarshall, Grant A (Author) / Liu, Huan (Thesis director) / Morstatter, Fred (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2015-05
Description
Astrobiology, as it is known by official statements and agencies, is “the study of the origin, evolution, distribution, and future of life in the universe” (NASA Astrobiology Insitute , 2018). This definition should suit a dictionary, but it may not accurately describe the research and motivations of practicing astrobiologists. Furthermore, it does little to characterize the context in which astrobiologists work. The aim of this project is to explore various social network structures within a large body of astrobiological research, intending to both further define the current motivations of astrobiological research and to lend context to these motivations. In this effort, two Web of Science queries were assembled to search for two contrasting corpora related to astrobiological research. The first search, for astrobiology and its close synonym, exobiology, returned a corpus of 3,229 journal articles. The second search, which includes the first and supplements it with further search terms (see Table 1) returned a corpus of 19,017 journal articles. The metadata for these articles were then used to construct various networks. The resulting networks describe an astrobiology that is well entrenched in other related fields, showcasing the interdisciplinarity of astrobiology in its emergence. The networks also showcase the entrenchment of astrobiology in the sociological context in which it is conducted—namely, its relative dependence on the United States government, which should prompt further discussion amongst astrobiology researchers.
ContributorsBromley, Megan Rachel (Author) / Manfred, Laubichler (Thesis director) / Sara, Walker (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / School of Earth and Space Exploration (Contributor) / Department of English (Contributor) / Barrett, The Honors College (Contributor)
Created2019-12
Description
Binge drinking has clear consequences but subtle influences among undergraduate students. While theories of perceived drinking norms and social identity have been determined to be predictive of binge drinking behavior, few studies have tested these influences outside of fraternities, sororities, and athletic teams and little research exists employing social network analysis (SNA) to quantify social ties. In this study, a small, undergraduate dance team was identified to test social identity theory using social network analysis in a peripheral social group. Each member was interviewed for demographic information, personal drinking habits, personal network structure, perceptions of peer drinking within both the personal network and the whole-network (the dance team), and sociometric position within the dance team. Personal network characteristics, whole-network dynamics and perceptions of peer drinking were tested for predictive value of individual binge drinking behavior utilizing binary logistic regression analysis. Results for predictor variables were weakened due to the small sample size (n = 13) and low variability within some constant variables, returning no statistically significant (p < 0.05) independent variables. However, while odds ratios could not be used to construct regression equations, four models were statistically significant overall. Each model was tested again without the constants; no models nor variables were statistically significant. These models indicated, within this sample, that 1) the proportion of a group that adopts binge drinking behavior is predictive of that behavior for the interviewee (in terms of the overall personal network as well as the triads within the personal network); and 2) the perception of the average team member's maximum alcohol intake along with the proportion of the personal network composed of team members is predictive of individual binge drinking behavior. Low variance in the variables and the small sample size warrant further research to test the viability of targeting anti-binge drinking campaigns toward peripheral social groups.
ContributorsOlivas, Elijah (Author) / Schaefer, David (Thesis director) / Stotts, Rhian (Committee member) / Barrett, The Honors College (Contributor)
Created2017-05
Description
My aims with this research project were to conduct a network analysis on collaborators in the ¡Viva Maryvale! project, a diabetes prevention program in Maryvale, AZ. The goals of the social network analysis were to measure the connections that collaborating organizations have to each other, the strength of these connections, and the activities that connected organizations collaborate on. I hypothesized that performing a network analysis would inform me of the strengths and weaknesses of the ¡Viva Maryvale! project in order to advise the next steps of a targeted approach to diabetes prevention among vulnerable populations, thus affecting public health outcomes in the greater Phoenix Valley.
ContributorsKellog, Anna (Author) / Shaibi, Gabriel (Thesis director) / Soltero, Erica (Committee member) / School of Public Affairs (Contributor) / School of Human Evolution and Social Change (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Social media has become a direct and effective means of transmitting personal opinions into the cyberspace. The use of certain key-words and their connotations in tweets portray a meaning that goes beyond the screen and affects behavior. During terror attacks or worldwide crises, people turn to social media as a means of managing their anxiety, a mechanism of Terror Management Theory (TMT). These opinions have distinct impacts on the emotions that people express both online and offline through both positive and negative sentiments. This paper focuses on using sentiment analysis on twitter hash-tags during five major terrorist attacks that created a significant response on social media, which collectively show the effects that 140-character tweets have on perceptions in social media. The purpose of analyzing the sentiments of tweets after terror attacks allows for the visualization of the effect of key-words and the possibility of manipulation by the use of emotional contagion. Through sentiment analysis, positive, negative and neutral emotions were portrayed in the tweets. The keywords detected also portray characteristics about terror attacks which would allow for future analysis and predictions in regards to propagating a specific emotion on social media during future crisis.
ContributorsHarikumar, Swathikrishna (Author) / Davulcu, Hasan (Thesis director) / Bodford, Jessica (Committee member) / Computer Science and Engineering Program (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Machine learning models can pick up biases and spurious correlations from training data and projects and amplify these biases during inference, thus posing significant challenges in real-world settings. One approach to mitigating this is a class of methods that can identify filter out bias-inducing samples from the training datasets to force models to avoid being exposed to biases. However, the filtering leads to a considerable wastage of resources as most of the dataset created is discarded as biased. This work deals with avoiding the wastage of resources by identifying and quantifying the biases. I further elaborate on the implications of dataset filtering on robustness (to adversarial attacks) and generalization (to out-of-distribution samples). The findings suggest that while dataset filtering does help to improve OOD(Out-Of-Distribution) generalization, it has a significant negative impact on robustness to adversarial attacks. It also shows that transforming bias-inducing samples into adversarial samples (instead of eliminating them from the dataset) can significantly boost robustness without sacrificing generalization.
ContributorsSachdeva, Bhavdeep Singh (Author) / Baral, Chitta (Thesis advisor) / Liu, Huan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021
Description
Nations censor specific information in accordance with their political, legal, and cultural standards. Each country adopts unique approaches and regulations for censorship, whether it involves moderating online content or prohibiting protests. This paper seeks to study the underlying motivations for the disparate behaviors exhibited by authorities and individuals. To achieve this, we develop a mathematical model designed to understand the dynamics between authority figures and individuals, analyzing their behaviors under various conditions. We argue that individuals essentially act in three phases - compliance, self-censoring, and defiance when faced with different situations under their own desires and the authority's parameters. We substantiate our findings by conducting different simulations on the model and visualizing the outcomes. Through these simulations, we realize why individuals exhibit behaviors falling into one of three categories, who are influenced by factors such as the level of surveillance imposed by the authority, the severity of punishments, the tolerance for dissent, or the individuals' boldness. This also helped us to understand why certain populations in a country exhibit defiance, self-censoring behavior, or compliance as they interact with each other and behave under specific conditions within a small network world.
ContributorsNahar, Anish Ashish (Author) / Daymude, Joshua (Thesis director) / Forrest, Stephanie (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05