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- Creators: Computer Science and Engineering Program
- Creators: Gu, Bin
- Status: Published
Description
This thesis proposed a novel approach to establish the trust model in a social network scenario based on users' emails. Email is one of the most important social connections nowadays. By analyzing email exchange activities among users, a social network trust model can be established to judge the trust rate between each two users. The whole trust checking process is divided into two steps: local checking and remote checking. Local checking directly contacts the email server to calculate the trust rate based on user's own email communication history. Remote checking is a distributed computing process to get help from user's social network friends and built the trust rate together. The email-based trust model is built upon a cloud computing framework called MobiCloud. Inside MobiCloud, each user occupies a virtual machine which can directly communicate with others. Based on this feature, the distributed trust model is implemented as a combination of local analysis and remote analysis in the cloud. Experiment results show that the trust evaluation model can give accurate trust rate even in a small scale social network which does not have lots of social connections. With this trust model, the security in both social network services and email communication could be improved.
ContributorsZhong, Yunji (Author) / Huang, Dijiang (Thesis advisor) / Dasgupta, Partha (Committee member) / Syrotiuk, Violet (Committee member) / Arizona State University (Publisher)
Created2011
Description
Due to the growing popularity of the Internet and smart mobile devices, massive data has been produced every day, particularly, more and more users’ online behavior and activities have been digitalized. Making a better usage of the massive data and a better understanding of the user behavior become at the very heart of industrial firms as well as the academia. However, due to the large size and unstructured format of user behavioral data, as well as the heterogeneous nature of individuals, it leveled up the difficulty to identify the SPECIFIC behavior that researchers are looking at, HOW to distinguish, and WHAT is resulting from the behavior. The difference in user behavior comes from different causes; in my dissertation, I am studying three circumstances of behavior that potentially bring in turbulent or detrimental effects, from precursory culture to preparatory strategy and delusory fraudulence. Meanwhile, I have access to the versatile toolkit of analysis: econometrics, quasi-experiment, together with machine learning techniques such as text mining, sentiment analysis, and predictive analytics etc. This study creatively leverages the power of the combined methodologies, and apply it beyond individual level data and network data. This dissertation makes a first step to discover user behavior in the newly boosting contexts. My study conceptualize theoretically and test empirically the effect of cultural values on rating and I find that an individualist cultural background are more likely to lead to deviation and more expression in review behaviors. I also find evidence of strategic behavior that users tend to leverage the reporting to increase the likelihood to maximize the benefits. Moreover, it proposes the features that moderate the preparation behavior. Finally, it introduces a unified and scalable framework for delusory behavior detection that meets the current needs to fully utilize multiple data sources.
ContributorsLi, Chunxiao (Author) / Gu, Bin (Thesis advisor) / Chen, Pei-Yu (Committee member) / Xiong, Hui (Committee member) / Arizona State University (Publisher)
Created2019
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
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
By collecting and analyzing more than two million tweets, U.S. House Representatives’ voting records in 111th and 113th Congress, and data from other resources I study several aspects of adoption and use of Twitter by Representatives. In the first chapter, I study the overall impact of Twitter use by Representatives on their political orientation and their political alignment with their constituents. The findings show that Representatives who adopted Twitter moved closer to their constituents in terms of political orientation.
By using supervised machine learning and text mining techniques, I shift the focus to synthesizing the actual content shared by Representatives on Twitter to evaluate their effects on Representatives’ political polarization in the second chapter. I found support for the effects of repeated expressions and peer influence in Representatives’ political polarization.
Last but not least, by employing a recently developed dynamic network model (separable temporal exponential-family random graph model), I study the effects of homophily on formation and dissolution of Representatives’ Twitter communications in the third chapter. The results signal the presence of demographic homophily and value homophily in Representatives’ Twitter communications networks.
These three studies altogether provide a comprehensive picture about the overall consequences and dynamics of use of online social networking platforms by Representatives.
By using supervised machine learning and text mining techniques, I shift the focus to synthesizing the actual content shared by Representatives on Twitter to evaluate their effects on Representatives’ political polarization in the second chapter. I found support for the effects of repeated expressions and peer influence in Representatives’ political polarization.
Last but not least, by employing a recently developed dynamic network model (separable temporal exponential-family random graph model), I study the effects of homophily on formation and dissolution of Representatives’ Twitter communications in the third chapter. The results signal the presence of demographic homophily and value homophily in Representatives’ Twitter communications networks.
These three studies altogether provide a comprehensive picture about the overall consequences and dynamics of use of online social networking platforms by Representatives.
ContributorsMosuavi, Seyedreza (Author) / Gu, Bin (Thesis advisor) / Vinzé, Ajay S. (Committee member) / Shi, Zhan (Michael) (Committee member) / Arizona State University (Publisher)
Created2016
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