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The Global ETD Search service is a free service for researchers
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Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
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Showing 21 to 26 of 26 (0.097 seconds)Spelling suggestions: "subject:"computational social science"" "subject:"eomputational social science""
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Model trees with topic model preprocessing: an approach for data journalism illustrated with the WikiLeaks Afghanistan war logsRusch, Thomas, Hofmarcher, Paul, Hatzinger, Reinhold, Hornik, Kurt 06 1900 (has links) (PDF)
The WikiLeaks Afghanistan war logs contain nearly 77,000 reports of
incidents in the US-led Afghanistan war, covering the period from January
2004 to December 2009. The recent growth of data on complex social systems
and the potential to derive stories from them has shifted the focus of
journalistic and scientific attention increasingly toward data-driven journalism
and computational social science. In this paper we advocate the usage
of modern statistical methods for problems of data journalism and beyond,
which may help journalistic and scientific work and lead to additional insight.
Using the WikiLeaks Afghanistan war logs for illustration, we present an approach
that builds intelligible statistical models for interpretable segments in
the data, in this case to explore the fatality rates associated with different circumstances
in the Afghanistan war. Our approach combines preprocessing by
Latent Dirichlet Allocation (LDA) with model trees. LDA is used to process
the natural language information contained in each report summary by estimating
latent topics and assigning each report to one of them. Together with
other variables these topic assignments serve as splitting variables for finding
segments in the data to which local statistical models for the reported number
of fatalities are fitted. Segmentation and fitting is carried out with recursive
partitioning of negative binomial distributions. We identify segments with
different fatality rates that correspond to a small number of topics and other
variables as well as their interactions. Furthermore, we carve out the similarities
between segments and connect them to stories that have been covered in
the media. This gives an unprecedented description of the war in Afghanistan
and serves as an example of how data journalism, computational social science
and other areas with interest in database data can benefit from modern
statistical techniques. (authors' abstract)
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Network structure, brokerage, and framing : how the internet and social media facilitate high-risk collective actionEtling, Bruce January 2016 (has links)
This thesis investigates the role of network structure, brokerage, and framing in high-risk collective action. I use the protest movement that emerged in Russia following falsified national elections in 2011 and 2012 as an empirical case study. I draw on a unique dataset of nearly 30,000 online documents and the linking structure of over 3,500 Russian Web sites. I employ a range of computational social science methods, including Exponential Random Graph Modeling, an advanced statistical model for social networks, social network analysis, machine learning, and latent semantic analysis. I address three research questions in this thesis. The first asks if a protest network challenging a hybrid regime will have a polycentric or hierarchical structure, and if that structure changes over time. Polycentric networks are conducive to high-risk collective action and are robust to the targeted removal of key nodes, while hierarchical networks can more easily mobilize protesters and spread information. I find that the Russian protest network has a polycentric structure only at the beginning of the protests, and moves towards a less effective hierarchical structure as the movement loses popular support. The second research question seeks to understand if brokered text is actually novel, and if that text is more novel in polycentric networks than in hierarchical ones. Brokers are the individuals or nodes in a network that connect disparate groups through weak ties and close structural holes. Brokers are advantageous because they have access to and spread novel information. I find that the text among nodes in brokered relationships is indeed novel, but that information novelty decreases when networks have a hierarchical structure. The last research question asks if a protest movement in a high-risk political setting can be more successful than the government at spreading its preferred frames, and within such a movement, whether moderate or extremist framing is more prevalent. I find that the opposition is far more effective than the government in spreading its frames, even when the government organizes massive counter protests. Within the movement, moderates are more likely to have their framing adopted online than extremists, unless violence occurs at protests. The findings suggest that movements should build flatter, more diffuse networks by ensuring that brokers tie together diverse protest constituencies. The findings also provide evidence against those who claim that authoritarian governments are more effective in shaping online discourse than oppositional movements, and also suggest that movements should advance moderate framing in order to attract a wider base of support among the general population.
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Understanding the behaviour and influence of automated social agentsGilani, Syed Zafar ul Hussan January 2018 (has links)
Online social networks (OSNs) have seen a remarkable rise in the presence of automated social agents, or social bots. Social bots are the new computing viral, that are surreptitious and clever. What facilitates the creation of social agents is the massive human user-base and business-supportive operating model of social networks. These automated agents are injected by agencies, brands, individuals, and corporations to serve their work and purpose; utilising them for news and emergency communication, marketing, social activism, political campaigning, and even spam and spreading malicious content. Their influence was recently substantiated by coordinated social hacking and computational political propaganda. The thesis of my dissertation argues that automated agents exercise a profound impact on OSNs that transforms into an array of influence on our society and systems. However, latent or veiled, these agents can be successfully detected through measurement, feature extraction and finely tuned supervised learning models. The various types of automated agents can be further unravelled through unsupervised machine learning and natural language processing, to formally inform the populace of their existence and impact.
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Measuring and Enhancing the Resilience of Interdependent Power Systems, Emergency Services, and Social CommunitiesValinejad, Jaber 28 January 2022 (has links)
Several calamities occur throughout the world each year, resulting in varying losses. Disasters wreak havoc on infrastructures and impair operation. They result in human deaths and injuries and stress people's mental and emotional states. These negative impacts of natural disasters induce significant economic losses, as demonstrated by the $ 423 billion loss in 2011 in Tohoku, Japan, and the $ 133 billion loss in hurricane Harvey, U.S.A. Every year, hurricanes and tropical storms result in 10,000 human deaths worldwide. To mitigate losses, communities' readiness, flexibility, and resilience must be strengthened. To this end, appropriate techniques for forecasting a community's capacity and functionality in the face of impending crises must be developed and suitable community resilience metrics and their quantification must be established. Collaboration between critical infrastructures such as power systems and emergency services and social networks is critical for building a resilient community. As a result, we require metrics that account for both the social and infrastructure aspects of the community. While the literature on critical infrastructures such as power systems discusses the effect of social factors on resilience, they do not model these social factors and metrics due to their complexity. On the other hand, it turns out that the role of critical infrastructures and some critical social characteristics is overlooked in the computational social science literature on community resilience. Thus, this dissertation presents a multi-agent socio-technical model of community resilience, taking into account the interconnection of power systems, emergency services, and social communities. We offer relevant measures for each section and describe dynamic change and its dependence on other metrics using a variety of theories and expertise from social science, psychology, electrical engineering, and emergency services. To validate the model, we used data on two hurricanes (Irma and Harvey) collected from Twitter, GoogleTrends, FEMA, power utilities, CNN, and Snopes (a fact-checking organization). We also describe methods for quantifying social metrics such as anxiety, risk perception, cooperation using social sensing, natural language processing, and text mining tools. / Doctor of Philosophy / Power systems serve social communities that consist of residential, commercial, and industrial customers. The social behavior and degree of collaboration of all stakeholders, such as consumers, prosumers, and utilities, affect the level of preparedness, mitigation, recovery, adaptability, and, thus, power system resilience. Nonetheless, the literature pays scant attention to stakeholders' social characteristics and collaborative efforts when confronted with a disaster and views the problem solely as a cyber-physical system. However, power system resilience, which is not a standalone discipline, is inherently a cyber-physical-social problem, making it complex to address. To this end, in this dissertation, we develop a socio-technical power system resilience model based on neuroscience, social science, and psychological theories and use the threshold model to simulate the behavior of power system stakeholders during a disaster. We validate our model using datasets of hurricane Harvey of Category 4 that hit Texas in August 2017 and hurricane Irma of Category 5 that made landfall in Florida in September 2017. We retrieve these datasets from Twitter and GoogleTrend and then apply natural language processing and language psychology analysis tools to deduce the social behavior of the end-users.
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[en] COMBINING A PROCESS AND TOOLS TO SUPPORT THE ANALYSIS OF ONLINE COMMUNITIES APPLIED TO HEALTHCARE / [pt] COMBINANDO UM PROCESSO E FERRAMENTAS PARA APOIAR A ANÁLISE DE COMUNIDADE ONLINE APLICADOS À ÁREA DE SAÚDEDARLINTON BARBOSA FERES CARVALHO 05 November 2014 (has links)
[pt] Esta pesquisa de tese teve como objetivo explorar a análise de mídias sociais, especialmente as disponíveis em comunidades online de sites de redes sociais, a fim de realizar estudos sociais sobre questões de saúde. Com base em uma abordagem prática foi definido um processo para realizar esses estudos. Este processo contou com ferramentas computacionais adaptados para fornecer apoio em tarefas específicas, tais como recuperação de conteúdo, seleção e análise. Duas ferramentas que se destacam são apresentadas por causa de sua utilidade e a complexidade do processo em que a sua construção se baseou.
Para o benefício da análise de comunidades online, o Mapa de Associação de Comunidades é um processo desenvolvido para apoiar especialistas em compreender os interesses dos usuários com base em suas associações dentro de suas comunidades. A outra ferramenta visa auxiliar analistas a selecionar discussões de fóruns online a serem analisados manualmente com técnicas de pesquisa qualitativa, por exemplo, análise de conteúdo e do discurso. Esta ferramenta, TorchSR, foi criada baseada em aprendizado de máquina não supervisionado, usando agrupamento hierárquico, para dar suporte na resolução do problema de seleção de conteúdo. Um estudo de caso exploratório mostra que esta ferramenta ajuda na resolução do problema. O processo proposto foi utilizado em dois estudos sobre questões relevantes de saúde (hepatite C e o abuso de drogas), que resultou em descobertas relevantes sobre saúde pública. Em conclusão, este trabalho apresenta a aplicação prática de ciência social computacional no campo da saúde, através do desenvolvimento de um processo e ferramentas utilizadas para apoiar os analistas e melhorar a sua aplicação. / [en] This research thesis is aiming to exploit valuable social media, especially those available in online communities of social network sites, in order to perform social studies about healthcare issues. Based on a practical approach, a process was defined to conduct such studies. This process relied on tailored computational tools to provide support for specific tasks such as contente retrieval, selection, and analysis. Two tools that stand out are presented because of their utility and the complexity of the process in which their development was based on. The first tool, for the benefit of online community analysis, is the Community Association Map, a process developed to support experts in understanding users’ interests based on their associations within their communities. Our second tool (TorchSR) aims to aid analysts in the
selection of discussions from online forums to be manually analyzed by (qualitative) research techniques (e.g. content and discourse analysis). This task, which was defined as solving the content selection problem, was tackled with a tool based on unsupervised machine learning techniques, such as hierarchical clustering. An exploratory study case shows that TorchSR helps analysts in dealing with the problem. The proposed process was employed in two studies about relevant healthcare issues (i.e. hepatitis C and drug abuse) which resulted in interesting findings in the field of public health. In conclusion, this thesis presents a practical application of computational social science to the field of health, through development of a process and tools used to support analysts and improve its application.
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The Salience of Issues in Parliamentary Debates : Its Development and Relation to the Support of the Sweden DemocratsAlexander, Ödlund Lindholm January 2020 (has links)
The aim of this study was to analyze the salience of issue dimensions in the Swedish parliament debates by the established parties during the rise of the Sweden Democrats Party (SD). Structural topic modeling was used to construct a measurement of the salience of issues, examining the full body of speeches in the Swedish parliament between September 2006 and December 2019. Trend analysis revealed a realignment from a focus on socio-economic to socio-cultural issues in Swedish politics. Cross-correlation analyses had conflicting results, indicating a weak positive relationship between the salience of issues and the support of SD – but low predictive ability; it also showed that changes in the support of SD did lead (precede) changes in the salience of issues in the parliament. The ramifications of socio-cultural issues being the most salient are that so-called radical right-wing populist parties (RRPs), or neo-nationalist parties, has a greater opportunity to gain support. It can make voters more inclined to base their voting decision on socio-cultural issues, which favors parties who fight for and are trustworthy in those issues – giving them more valence in the eyes of the voters.
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