This thesis utilizes social influence theory and computational tools to examine the disparate impact of positive and negative ties in nuclear weapons proliferation. The thesis is broadly in two sections: a simulation section, which focuses on government stakeholders, and a large-scale data analysis section, which focuses on the public and domestic actor stakeholders. In the simulation section, it demonstrates that the nonproliferation norm is an emergent behavior from political alliance and hostility networks, and that alliances play a role in current day nuclear proliferation. This model is robust and contains second-order effects of extended hostility and alliance relations. In the large-scale data analysis section, the thesis demonstrates the role that context plays in sentiment evaluation and highlights how Twitter collection can provide useful input to policy processes. It first highlights the results of an on-campus study where users demonstrated that context plays a role in sentiment assessment. Then, in an analysis of a Twitter dataset of over 7.5 million messages, it assesses the role of ‘noise’ and biases in online data collection. In a deep dive analyzing the Iranian nuclear agreement, we demonstrate that the middle east is not facing a nuclear arms race, and show that there is a structural hole in online discussion surrounding nuclear proliferation. By combining both approaches, policy analysts have a complete and generalizable set of computational tools to assess and analyze disparate stakeholder roles in nuclear proliferation.

Computational approaches to opinion mining have mostly focused on polarity detection of product reviews by classifying the given text as positive, negative or neutral. While, there is less effort in the direction of socio-political opinion mining to determine favorability towards given targets of interest, particularly for social media data like news comments and tweets. In this research, we explore the task of automatically determining from the text whether the author of the text is in favor of, against, or neutral towards a proposition or target. The target may be a person, an organization, a government policy, a movement, a product, etc. Moreover, we are interested in detecting the reasons behind authors’ positions. This thesis is organized into three main parts: the first part on Twitter stance detection and interaction of stance and sentiment labels, the second part on detecting stance and the reasons behind it in online news comments, and the third part on multi-target stance classification. One may express favor (or disfavor) towards a target by using positive or negative language. Here, for the first time, we present a dataset of tweets annotated for whether the tweeter is in favor of or against pre-chosen targets, as well as for sentiment. These targets may or may not be referred to in the tweets, and they may or may not be the target of opinion in the tweets. We develop a simple stance detection system that outperforms all 19 teams that participated in a recent shared task competition on the same dataset (SemEval-2016 Task #6). Additionally, access to both stance and sentiment annotations allows us to conduct several experiments to tease out their interactions. Next, we proposed a novel framework for joint learning of stance and reasons behind it. This framework relies on topic modeling. Unlike other machine learning approaches for argument tagging which often require a large set of labeled data, our approach is minimally supervised. The extracted arguments are subsequently employed for stance classification. Furthermore, we create and make available the first dataset of online news comments manually annotated for stance and arguments. Experiments on this dataset demonstrate the benefits of using topic modeling, particularly Non-Negative Matrix Factorization, for argument detection. Previous models for stance classification often treat each target independently, ignoring the potential (sometimes very strong) dependency that could exist among targets. However, in many applications, there exist natural dependencies among targets. In this research, we relieve such independence assumptions in order to jointly model the stance expressed towards multiple targets. We present a new dataset that we built for this task and make it publicly available. Next, we show that an attention-based encoder-decoder framework is very effective for this problem, outperforming several alternatives that jointly learn dependent subjectivity through cascading classification or multi-task learning.

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京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第20737号 / 情博第651号 / 新制||情||112(附属図書館) / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 吉川 正俊, 教授 石田 亨, 教授 美濃 導彦, 准教授 馬 強 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

<p dir="ltr">Easy access, variety of content, and fast widespread interactions are some of the reasons that have made social media increasingly popular in our society. This has lead to many people use social media everyday for a variety of reasons, such as interacting with friends or consuming news content. Thus, understanding content on social media is more important than ever.</p><p dir="ltr">An increased understanding on social media can lead to improvements on a large number of important tasks. In this work, we particularly focus on fake news detection and political bias detection. Fake news, text published by news sources with an intent to spread misinformation and sway beliefs, is ever prevalent in today's society. Detecting it is an important and challenging problem to prevent large scale misinformation and maintain a healthy society. In a similar way, detecting the political bias of news content can provide insights about the different perspectives on social media.</p><p dir="ltr">In this work, we view the problem of understanding social media as reasoning over the relationships between sources, the articles they publish, and the engaging users. We start by analyzing these relationships in a graph-based framework, and then use Large Language Models to do the same. We hypothesize that the key to understanding social media is understanding these relationships, such as identifying which users have similar perspectives, or which articles are likely to be shared by similar users.</p><p dir="ltr">Throughout this thesis, we propose several frameworks to capture the relationships on social media better. We initially tackle this problem using supervised learning systems, improving them to achieve strong performance. However, we find that automatedly modeling the complexities of the social media landscape is challenging. On the contrary, having humans analyze and interact with all news content to find relationships, is not scalable. Thus, we then propose to approach enhance our supervised approaches by approaching the social media understanding problem \textit{interactively}, where humans can interact to help an automated system learn a better social media representation quality.</p><p dir="ltr">On real world events, our experiments show performance improvements in detecting the factuality and political bias of news sources, both when trained with and without minimal human interactions. We particularly focus on one of the most challenging setups of this task, where test data is unseen and focuses on new topics when compared with the training data. This realistic setting shows the real world impact of our work in improving social media understanding.</p>

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This thesis describes the design and implementation of a system for social media analysis. This system provides a way of identifying social media user's influence. The system has been open sourced under the MIT license and is designed to be easily extensible. Example usage of this system is demonstrated for a chosen use case of analysing several selected Czech individuals and political parties which are active on the Facebook social network. The thesis compared their influence and activity. A new way of activity and influence prediction has been proposed, based on the identification of dedicated users.

Social media has been getting more and more attention from common users as well as businesses. Although the level of social media adoption varies by sector and geographical location, all companies strive to understand which social media platforms adopt and how to effectively use them. There is a specific position for the winery sector, which is widely recognised as traditional. However, more and more people search for information, share information and purchase goods or services online, which made presence on social media inevitable even for wineries. There is a body of literature concerning wineries' social media adoption and usage, however there is none of such kind to be found in the Czech Republic. Thus, this paper's aim is to fill this gap and to find out to what extent do Czech wineries use social media and why. This paper investigates 96 responses from wineries from all regions of the Czech Republic. The evidence is that the common communication channels of Czech wineries are rather traditional, being it personal communication, email or phone. However, the level of social media adoption in comparison with Australia, Germany or New Zealand is rather high. Although Czech wineries value social media, there is a significant lack of knowledge in how to use them efficiently.