<p>  </p> <p>This dissertation attempts to explain a uniquely modern phenomenon—totalitarianism—through a case study of Chinese totalitarianism. It seeks to solve the puzzle of why the Chinese people’s inclinations, manners, customs, and morals were particularly suitable for totalitarian rule, and its thesis is that <em>Confucianism</em> laid the moral and psychological foundations of Chinese totalitarianism, paving the way for socialism and communism’s takeover of China in the twentieth century. </p> <p>It is this Confucian substratum that distinguishes Chinese totalitarianism from Western parallels. It is true that socialist and communist ideas were significant in advancing the Chinese Communist Party’s dictatorship, but the Chinese did not succumb to a socialism or communism imported from abroad. In the West, totalitarian ideologies bewitched masses suffering from economic crises and social unrest, who were thus willing to accept a centralized government led by a “strong man” promising economic renewal and restoral of order. In China, those ideologies only took root because of and on the basis of their accordance with the preexisting Confucianism. </p> <p>This dissertation includes in-depth and extensive textual analysis of original Confucian texts. Its theoretical analysis of Confucius’s original thought, in particular his ethical and political teachings, illustrates how traditional Chinese political culture, nurtured in Confucian ethics, predisposed the Chinese people to a totalitarian solution to political problems. </p> <p>Chapter 2 presents the analysis’s method and terminology, which are unconventional. It explicates a few key terms which are essential to the Confucian canon, but which have long been mistranslated in the English literature. Chapter 3 reviews the literature of totalitarianism and proposes a (re)conceptualization of totalitarianism deviating from conventional treatments. Chapter 4 turns to the analysis of the intellectual characteristics of the ru school of thought, explaining the amenability of Chinese society to a totalitarian rule depending on mass obedience and the inability of individuals to think for themselves. It is shown that human hermeneutics—modes of interpreting and understanding phenomena—are realized fundamentally differently in China than in the West. Chapter 5 examines ru ethics, the moral foundation of traditional Chinese politics, which is here termed <em>family politics</em>. Comparing Western accounts of ethics with 伦理 (<em>lun li</em>) demonstrates the essential differences between Chinese and Western morality. Chapter 6 concerns China’s traditional political culture, which shaped China’s imperial politics and is still robust in today’s China. Finally, Chapter 7 explains why European socialism, an ideology seemingly alien to Chinese culture, nonetheless was able to flourish in China. This chapter also addresses the question of why other East Asian countries, also influenced by the ru school of thought, did not follow the same totalitarian pathway as China.  </p>

The United States (U.S.) military withdrawal from Afghanistan in August 2021 was met with turmoil as Taliban regained control of most of the country, including Kabul. These events have affected many and were widely discussed on social media, especially in the U.S. In this work, we focus on Twitter discourse regarding these events, especially potential opinion shifts over time and the effect social media posts by established U.S. legislators might have had on online public perception. To this end, we investigate two datasets on the war in Afghanistan, consisting of Twitter posts by self-identified U.S. accounts and conversation threads initiated by U.S. politicians. We find that Twitter users' discussions revolve around the Kabul airport event, President Biden's handling of the situation, and people affected by the U.S. withdrawal. Microframe analysis indicates that discourse centers the humanitarianism underlying these occurrences and politically leans liberal, focusing on care and fairness. Lastly, network analysis shows that Republicans are far more active on Twitter compared to Democrats and there is more positive sentiment than negative in their conversations. / Master of Science / The United States (U.S.) military withdrawal from Afghanistan in August 2021 was met with turmoil as Taliban regained control of most of the country, including Kabul. These events have affected many and were widely discussed on social media, especially in the U.S. In this work, we focus on Twitter regarding these events, and study if public's opinion change over time especially by the posts of legislators. Therefore, we used two datasets about unrest in Afghanistan after the Taliban takeover. One datasets consists of of Twitter posts by self-identified U.S. accounts and the other one are the conversation threads initiated by U.S. politicians. We find that Twitter users' discussions revolve around the Kabul airport event, President Biden's handling of the situation, and people affected by the U.S. withdrawal. According to our findings based on several methods analyzing the content of the posts of Twitter users, the pressing issues are the humanitarian concerns for the people who could be the target of Taliban. Last but not least, we also studied the relationship between legislators and twitter users along with the dominant sentiment about the topic. Our analysis shows that Republicans are far more active on Twitter compare to Democrats and there is more positive sentiment than negative in their conversations.

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Prior literature has investigated the connection between school shootings and factors of familial trauma and mental health. Specifically, experiences related to parental suicide, physical or sexual abuse, neglect, marital violence, or severe bullying have been associated with a propensity for carrying out a mass shooting. Given prior research has shown common histories among school shooters, it follows that a person's violent tendencies can be revealed by their previous communications with others, thus aiding in predicting an individual's proclivity for school shootings. However, previous literature found no conclusions were drawn from online posts made by the shooters prior to the mass shootings. This thesis applies NVivo-supported thematic analysis and Natural Language Processing (NLP) to study school shootings by comparing the online speech patterns of known school terrorists versus those of non-violent extremists and ordinary teenagers online. Findings indicate that out of all the possible NLP indicators, conversation, HarmVice, negative tone, and conflict are the most suitable school shootings indicators. Ordinary people score eight times higher than known school shooters and online extremists in conversation. Known shooters score more than 14 times higher in HarmVice, than in both ordinary people and online extremists. Known shooters also score higher in negative tone (1.37 times higher than ordinary people and 1.78 times higher than online extremists) and conflict (more than three times higher than ordinary people and 1.8 times higher than online extremists). The implications for domestic violence prediction and prevention can be used to protect citizens inside educational infrastructure by linking the flagged accounts to the schools or colleges that they attend. Further research is needed to determine the severity of emotional coping displayed in online posts, as well as the amount of information and frequency with which weapons and killing are discussed.

<p>Language “in the wild” is complex and ambiguous and relies on a shared understanding of the world for its interpretation. Most current natural language processing methods represent language by learning word co-occurrence patterns from massive amounts of linguistic data. This representation can be very powerful, but it is insufficient to capture the meaning behind written and spoken communication. </p> <p> </p> <p>In this dissertation, I will motivate neural-symbolic representations for dealing with these challenges. On the one hand, symbols have inherent explanatory power, and they can help us express contextual knowledge and enforce consistency across different decisions. On the other hand, neural networks allow us to learn expressive distributed representations and make sense of large amounts of linguistic data. I will introduce a holistic framework that covers all stages of the neural-symbolic pipeline: modeling, learning, inference, and its application for diverse discourse scenarios, such as analyzing online discussions, mining argumentative structures, and understanding public discourse at scale. I will show the advantages of neural-symbolic representations with respect to end-to-end neural approaches and traditional statistical relational learning methods.</p> <p><br></p> <p>In addition to this, I will demonstrate the advantages of neural-symbolic representations for learning in low-supervision settings, as well as their capabilities to decompose and explain high-level decision. Lastly, I will explore interactive protocols to help human experts in making sense of large repositories of textual data, and leverage neural-symbolic representations as the interface to inject expert human knowledge in the process of partitioning, classifying and organizing large language resources. </p>

<p>Nowadays an increasing number of people consume, share, and interact with information online. This results in posting and counter-posting on online media by different ideological groups on various polarized topics. Consequently, online media has become the primary platform for political and social influencers to directly interact with the citizens and share their perspectives, views, and stances with the goal of gaining support for their actions, bills, and legislation. Hence, understanding the perspectives and the influencing strategies in online media texts is important for an individual to avoid misinformation and improve trust between the general people and the influencers and the authoritative figures such as the government.</p> <p><br></p> <p>Automatically understanding the perspectives in online media is difficult because of two major challenges. Firstly, the proper grammar or mechanism to characterize the perspectives is not available. Recent studies in Natural Language Processing (NLP) have leveraged resources from social science to explain perspectives. For example, Policy Framing and Moral Foundation Theory are used for understanding how issues are framed and the moral appeal expressed in texts to gain support. However, these theories often fail to capture the nuances in perspectives and cannot generalize over all topics and events. Our research in this dissertation is one of the first studies that adapt social science theories in Natural Language Processing for understanding perspectives to the extent that they can capture differences in ideologies or stances. The second key challenge in understanding perspectives in online media texts is that annotated data is difficult to obtain to build automatic methods to detect the perspectives, that can generalize over the large corpus of online media text on different topics. To tackle this problem, in this dissertation, we used weak sources of supervision such as social network interaction of users who produce and interact with the messages, weak human interaction, or artificial few-shot data using Large Language Models. </p> <p><br></p> <p>Our insight is that various tasks such as perspectives, stances, sentiments toward entities, etc. are interdependent when characterizing online media messages. As a result, we proposed approaches that jointly model various interdependent problems such as perspectives, stances, sentiments toward entities, etc., and perform structured prediction to solve them jointly. Our research findings showed that the messaging choices and perspectives on online media in response to various real-life events and their prominence and contrast in different ideological camps can be efficiently captured using our developed methods.</p>