Thèses sur le sujet « Data mining – social aspects »
Créez une référence correcte selon les styles APA, MLA, Chicago, Harvard et plusieurs autres
Consultez les 50 meilleures thèses pour votre recherche sur le sujet « Data mining – social aspects ».
À côté de chaque source dans la liste de références il y a un bouton « Ajouter à la bibliographie ». Cliquez sur ce bouton, et nous générerons automatiquement la référence bibliographique pour la source choisie selon votre style de citation préféré : APA, MLA, Harvard, Vancouver, Chicago, etc.
Vous pouvez aussi télécharger le texte intégral de la publication scolaire au format pdf et consulter son résumé en ligne lorsque ces informations sont inclues dans les métadonnées.
Parcourez les thèses sur diverses disciplines et organisez correctement votre bibliographie.
Chen, Weidong. « Discovering communities by information diffusion and link density propagation ». HKBU Institutional Repository, 2012. https://repository.hkbu.edu.hk/etd_ra/1422.
Texte intégralNguyen, Ngoc Buu Cat. « Data Mining in Knowledge Management Processes : Developing an Implementing Framework ». Thesis, Umeå universitet, Institutionen för informatik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-149668.
Texte intégralYang, Shuang-Hong. « Predictive models for online human activities ». Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43689.
Texte intégralCai, Zhongming. « Technical aspects of data mining ». Thesis, Cardiff University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.395784.
Texte intégralEriksson, Jesper, et Samuel Björeqvist. « Datadriven Innovation : En komparativ studie om dataanalysmetoder och verktyg för små företag ». Thesis, Umeå universitet, Institutionen för informatik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-149865.
Texte intégralWang, Guan. « Graph-Based Approach on Social Data Mining ». Thesis, University of Illinois at Chicago, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3668648.
Texte intégralPowered by big data infrastructures, social network platforms are gathering data on many aspects of our daily lives. The online social world is reflecting our physical world in an increasingly detailed way by collecting people's individual biographies and their various of relationships with other people. Although massive amount of social data has been gathered, an urgent challenge remain unsolved, which is to discover meaningful knowledge that can empower the social platforms to really understand their users from different perspectives.
Motivated by this trend, my research addresses the reasoning and mathematical modeling behind interesting phenomena on social networks. Proposing graph based data mining framework regarding to heterogeneous data sources is the major goal of my research. The algorithms, by design, utilize graph structure with heterogeneous link and node features to creatively represent social networks' basic structures and phenomena on top of them.
The graph based heterogeneous mining methodology is proved to be effective on a series of knowledge discovery topics, including network structure and macro social pattern mining such as magnet community detection (87), social influence propagation and social similarity mining (85), and spam detection (86). The future work is to consider dynamic relation on social data mining and how graph based approaches adapt from the new situations.
Ip, Lai Cheng. « Mining on social network community for marketing ». Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3950661.
Texte intégralCosta, Alceu Ferraz. « Mining User Activity Data in Social Media Services ». Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-11092017-151000/.
Texte intégralO impacto dos serviços de mídia social em nossa sociedade é crescente. Indivíduos frequentemente utilizam mídias sociais para obter notícias, decidir quais os produtos comprar ou para se comunicar com amigos. Como consequência da adoção generalizada de mídias sociais, um grande volume de dados sobre como os usuários se comportam é gerado diariamente e armazenado em grandes bancos de dados. Aprender a analisar e extrair conhecimentos úteis a partir destes dados tem uma série de potenciais aplicações. Por exemplo, um entendimento mais detalhado sobre como usuários legítimos interagem com serviços de mídia social poderia ser explorado para projetar métodos mais precisos de detecção de spam e fraude. Esta pesquisa de doutorado baseia-se na seguinte hipótese: dados gerados por usuários de mídia social apresentam padrões que podem ser explorados para melhorar a eficácia de tarefas como previsão e modelagem no domínio das mídias sociais. Para validar esta hipótese, foram projetados métodos de mineração de dados adaptados aos dados de mídia social. As principais contribuições desta pesquisa de doutorado podem ser divididas em três partes. Primeiro, foi desenvolvido o Act-M, um modelo matemático que descreve o tempo das ações dos usuários. O autor demonstrou que o Act-M pode ser usado para detectar automaticamente bots entre usuários de mídia social com base apenas nos dados de tempo. A segunda contribuição desta tese é o VnC (Vote-and- Comment), um modelo que explica como o volume de diferentes tipos de interações de usuário evolui ao longo do tempo quando um conteúdo é submetido a um serviço de mídia social. Além de descrever precisamente os dados reais, o VnC é útil, pois pode ser empregado para prever o número de interações recebidas por determinado conteúdo de mídia social. Por fim, nossa terceira contribuição é o método MFS-Map. O MFS-Map fornece automaticamente anotações textuais para imagens de mídias sociais, combinando eficientemente características visuais e de metadados das imagens. As contribuições deste doutorado foram validadas utilizando dados reais de diversos serviços de mídia social. Os experimentos mostraram que os modelos Act-M e VnC forneceram um ajuste mais preciso aos dados quando comparados, respectivamente, a modelos existentes para dinâmica de comunicação e difusão de informação. O MFS-Map obteve precisão superior e tempo de execução reduzido quando comparado com outros métodos amplamente utilizados para anotação de imagens.
Meneghello, James. « A scalable framework for integrated social data mining ». Thesis, Meneghello, James (2017) A scalable framework for integrated social data mining. PhD thesis, Murdoch University, 2017. https://researchrepository.murdoch.edu.au/id/eprint/36690/.
Texte intégralAlsaleh, Slah. « Recommending people in social networks using data mining ». Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/61736/1/Slah_Alsaleh_Thesis.pdf.
Texte intégralIsah, Haruna. « Social Data Mining for Crime Intelligence : Contributions to Social Data Quality Assessment and Prediction Methods ». Thesis, University of Bradford, 2017. http://hdl.handle.net/10454/16066.
Texte intégralLiu, Lian. « PRIVACY PRESERVING DATA MINING FOR NUMERICAL MATRICES, SOCIAL NETWORKS, AND BIG DATA ». UKnowledge, 2015. http://uknowledge.uky.edu/cs_etds/31.
Texte intégralWang, Yan. « Student Modeling From Different Aspects ». Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-theses/205.
Texte intégralBergami, Giacomo. « Hypergraph Mining for Social Networks ». Master's thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amslaurea.unibo.it/7106/.
Texte intégralNhlabano, Valentine Velaphi. « Fast Data Analysis Methods For Social Media Data ». Diss., University of Pretoria, 2018. http://hdl.handle.net/2263/72546.
Texte intégralDissertation (MSc)--University of Pretoria, 2019.
National Research Foundation (NRF) - Scarce skills
Computer Science
MSc
Unrestricted
Li, Jingxuan. « Mining the Online Social Network Data : Influence, Summarization, and Organization ». FIU Digital Commons, 2014. http://digitalcommons.fiu.edu/etd/1241.
Texte intégralJiang, Fan. « Efficient frequent pattern mining from big data and its applications ». Springer, 2014. http://hdl.handle.net/1993/32083.
Texte intégralFebruary 2017
Chen, Nai Chun. « Urban data mining : social media data analysis as a complementary tool for urban design ». Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106414.
Texte intégralCataloged from PDF version of thesis.
Includes bibliographical references (pages 70-71).
The emergence of "big data" has resulted in a large amount of information documenting daily events, perceptions, thoughts, and emotions of citizens, all annotated with the location and time that they were recorded. This data presents an unprecedented opportunity to help identify and solve urban problems. This thesis aimed to explore the potential of machine learning and data mining in finding patterns in "big" urban data. We explored several different types of user generated urban data, including Call Detail Records (CDR) data and social media (Crunch Base, Yelp, Twitter, and Flickr, and Trip Advisor) data on two primary urban issues. First, we aimed to explore an important 21st century urban problem: how to make successful "Innovative district". Using data mining, we discovered several important characteristics of "innovative districts". Second, we aimed to see if big data is able to help diagnose and alleviate existing problems in cities. For this, we focused on the city of Andorra, and discovered potential reasons for recent declines in tourism in the city. We also discovered that we can learn the travel patterns of tourists to Andorra from their past behavior. In this way, we can predict their future travel plans and help their travels, showing the power of data mining urban data in helping to solve future urban problems as well as diagnose and improve existing problems.
by Nai Chun Chen.
S.M.
Akay, Altug. « A Novel Method to Intelligently Mine Social Media to Assess Consumer Sentiment of Pharmaceutical Drugs ». Doctoral thesis, KTH, Systemsäkerhet och organisation, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-203119.
Texte intégralQC 20170314
Goyal, Amit. « Social influence and its applications : an algorithmic and data mining study ». Thesis, University of British Columbia, 2013. http://hdl.handle.net/2429/43935.
Texte intégralPochet, Gilberto Flores. « Analysis of online virtual environments using Data Mining and social networks ». reponame:Repositório Institucional da UFABC, 2015.
Trouver le texte intégralCorley, Courtney D. Mikler Armin. « Social network simulation and mining social media to advance epidemiology ». [Denton, Tex.] : University of North Texas, 2009. http://digital.library.unt.edu/permalink/meta-dc-11053.
Texte intégralGRIMAUDO, LUIGI. « Data Mining Algorithms for Internet Data : from Transport to Application Layer ». Doctoral thesis, Politecnico di Torino, 2014. http://hdl.handle.net/11583/2537089.
Texte intégralMengwe, Moses Seargent. « Towards social impact assessment of copper-nickel mining in Botswana ». Thesis, Nelson Mandela Metropolitan University, 2010. http://hdl.handle.net/10948/1443.
Texte intégralATTANASIO, ANTONIO. « Mining Heterogeneous Urban Data at Multiple Granularity Layers ». Doctoral thesis, Politecnico di Torino, 2018. http://hdl.handle.net/11583/2709888.
Texte intégralDegnen, Cathrine. « Mining experience : the ageing self, narrative, and social memory in Dodworth, England ». Thesis, McGill University, 2003. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=19487.
Texte intégralFan, Xiaoguang, et 樊晓光. « Study of social-network-based information propagation ». Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hub.hku.hk/bib/B50899600.
Texte intégralpublished_or_final_version
Electrical and Electronic Engineering
Doctoral
Doctor of Philosophy
Jonathan, Joan. « Prediction of Factors Influencing Rats Tuberculosis Detection Performance Using Data Mining Techniques ». Thesis, Uppsala universitet, Institutionen för informatik och media, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385471.
Texte intégralEpstein, Greg. « Harnessing User Data to Improve Facebook Features ». Thesis, Boston College, 2010. http://hdl.handle.net/2345/1211.
Texte intégralThe recent explosion of online social networking through sites like Twitter, MySpace, Facebook has millions of users spending hours a day sorting through information on their friends, coworkers and other contacts. These networks also house massive amounts of user activity information that is often used for advertising purposes but can be utilized for other activities as well. Facebook, now the most popular in terms of registered users, active users and page rank, has a sparse offering of built-in filtering and predictive tools such as ``suggesting a friend'' or the ``Top News'' feed filter. However these basic tools seem to underutilize the information that Facebook stores on all of its users. This paper explores how to better use available Facebook data to create more useful tools to assist users in sorting through their activities on Facebook
Thesis (BS) — Boston College, 2010
Submitted to: Boston College. College of Arts and Sciences
Discipline: Computer Science Honors Program
Discipline: College Honors Program
Discipline: Computer Science
Hassanzadeh, Reza. « Anomaly detection in online social networks : using data-mining techniques and fuzzy logic ». Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/78679/1/Reza_Hassanzadeh_Thesis.pdf.
Texte intégralMalliaros, Fragkiskos. « Mining Social and Information Networks : Dynamics and Applications ». Palaiseau, Ecole polytechnique, 2015. https://theses.hal.science/tel-01245134/document.
Texte intégralCasas, Roma Jordi. « Privacy-preserving and data utility in graph mining ». Doctoral thesis, Universitat Autònoma de Barcelona, 2014. http://hdl.handle.net/10803/285566.
Texte intégralIn recent years, an explosive increase of graph-formatted data has been made publicly available. Embedded within this data there is private information about users who appear in it. Therefore, data owners must respect the privacy of users before releasing datasets to third parties. In this scenario, anonymization processes become an important concern. However, anonymization processes usually introduce some kind of noise in the anonymous data, altering the data and also their results on graph mining processes. Generally, the higher the privacy, the larger the noise. Thus, data utility is an important factor to consider in anonymization processes. The necessary trade-off between data privacy and data utility can be reached by using measures and metrics to lead the anonymization process to minimize the information loss, and therefore, to maximize the data utility. In this thesis we have covered the fields of user's privacy-preserving in social networks and the utility and quality of the released data. A trade-off between both fields is a critical point to achieve good anonymization methods for the subsequent graph mining processes. Part of this thesis has focused on data utility and information loss. Firstly, we have studied the relation between the generic information loss measures and the clustering-specific ones, in order to evaluate whether the generic information loss measures are indicative of the usefulness of the data for subsequent data mining processes. We have found strong correlation between some generic information loss measures (average distance, betweenness centrality, closeness centrality, edge intersection, clustering coefficient and transitivity) and the precision index over the results of several clustering algorithms, demonstrating that these measures are able to predict the perturbation introduced in anonymous data. Secondly, two measures to reduce the information loss on graph modification processes have been presented. The first one, Edge neighbourhood centrality, is based on information flow throw 1-neighbourhood of a specific edge in the graph. The second one is based on the core number sequence and it preserves better the underlying graph structure, retaining more data utility. By an extensive experimental set up, we have demonstrated that both methods are able to preserve the most important edges in the network, keeping the basic structural and spectral properties close to the original ones. The other important topic of this thesis has been privacy-preserving methods. We have presented our random-based algorithm, which utilizes the concept of Edge neighbourhood centrality to drive the edge modification process to better preserve the most important edges in the graph, achieving lower information loss and higher data utility on the released data. Our method obtains a better trade-off between data utility and data privacy than other methods. Finally, two different approaches for k-degree anonymity on graphs have been developed. First, an algorithm based on evolutionary computing has been presented and tested on different small and medium real networks. Although this method allows us to fulfil the desired privacy level, it presents two main drawbacks: the information loss is quite large in some graph structural properties and it is not fast enough to work with large networks. Therefore, a second algorithm has been presented, which uses the univariate micro-aggregation to anonymize the degree sequence and reduce the distance from the original one. This method is quasi-optimal and it results in lower information loss and better data utility.
Straub, Kayla Marie. « Data Mining Academic Emails to Model Employee Behaviors and Analyze Organizational Structure ». Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/71320.
Texte intégralMaster of Science
Velichety, Srikar, et Srikar Velichety. « Essays on Data Driven Insights from Crowd Sourcing, Social Media and Social Networks ». Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/620677.
Texte intégralSpomer, Judith E. « Latent semantic analysis and classification modeling in applications for social movement theory / ». Abstract Full Text (HTML) Full Text (PDF), 2008. http://eprints.ccsu.edu/archive/00000552/02/1996FT.htm.
Texte intégralThesis advisor: Roger Bilisoly. "... in partial fulfillment of the requirements for the degree of Master of Science in Data Mining." Includes bibliographical references (leaves 122-127). Also available via the World Wide Web.
Rossi, Maria. « Graph Mining for Influence Maximization in Social Networks ». Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLX083/document.
Texte intégralModern science of graphs has emerged the last few years as a field of interest and has been bringing significant advances to our knowledge about networks. Until recently the existing data mining algorithms were destined for structured/relational data while many datasets exist that require graph representation such as social networks, networks generated by textual data, 3D protein structures and chemical compounds. It has become therefore of crucial importance to be able to extract meaningful information from that kind of data and towards this end graph mining and analysis methods have been proven essential. The goal of this thesis is to study problems in the area of graph mining focusing especially on designing new algorithms and tools related to information spreading and specifically on how to locate influential entities in real-world networks. This task is crucial in many applications such as information diffusion, epidemic control and viral marketing. In the first part of the thesis, we have studied spreading processes in social networks focusing on finding topological characteristics that rank entities in the network based on their influential capabilities. We have specifically focused on the K-truss decomposition which is an extension of the core decomposition of the graph. Extensive experimental analysis showed that the nodes that belong to the maximal K-truss subgraph show a better spreading behavior when compared to baseline criteria. Such spreaders can influence a greater part of the network during the first steps of a spreading process but also the total fraction of the influenced nodes at the end of the epidemic is greater. We have also observed that node members of such dense subgraphs are those achieving the optimal spreading in the network.In the second part of the thesis, we focused on identifying a group of nodes that by acting all together maximize the expected number of influenced nodes at the end of the spreading process, formally called Influence Maximization (IM). The IM problem is actually NP-hard though there exist approximation guarantees for efficient algorithms that can solve the problem while obtaining a solution within the 63% of optimal classes of models. As those guarantees propose a greedy approximation which is computationally expensive especially for large graphs, we proposed the MATI algorithm which succeeds in locating the group of users that maximize the influence while also being scalable. The algorithm takes advantage the possible paths created in each node’s neighborhood to precalculate each node’s potential influence and produces competitive results in quality compared to those of baseline algorithms such as the Greedy, LDAG and SimPath. In the last part of the thesis, we study the privacy point of view of sharing such metrics that are good influential indicators in a social network. We have focused on designing an algorithm that addresses the problem of computing through an efficient, correct, secure, and privacy-preserving algorithm the k-core metric which measures the influence of each node of the network. We have specifically adopted a decentralization approach where the social network is considered as a Peer-to-peer (P2P) system. The algorithm is built based on the constraint that it should not be possible for a node to reconstruct partially or entirely the graph using the information they obtain during its execution. While a distributed algorithm that computes the nodes’ coreness is already proposed, dynamic networks are not taken into account. Our main contribution is an incremental algorithm that efficiently solves the core maintenance problem in P2P while limiting the number of messages exchanged and computations. We provide a security and privacy analysis of the solution regarding network de-anonimization and show how it relates to previously defined attacks models and discuss countermeasures
Soni, Swapnil. « Domain-Specific Document Retrieval Framework for Near Real-time Social Health Data ». Wright State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1440954750.
Texte intégralMY, DO TRA. « Apply data mining to segment retail market based on purchasing portfolios ». Thesis, Högskolan i Borås, Institutionen Handels- och IT-högskolan, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-20774.
Texte intégralZulfiqar, Omer. « Detecting Public Transit Service Disruptions Using Social Media Mining and Graph Convolution ». Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/103745.
Texte intégralMaster of Science
Millions of people worldwide rely on public transit networks for their daily commutes and day to day movements. With the growth in the number of people using the service, there has been an increase in the number of daily passengers affected by service disruptions. This thesis and research involves proposing and developing three different approaches to help aid in the timely detection of these disruptions. In this work we have developed a pure data mining approach along with two deep learning models using neural networks and live data from Twitter to identify these disruptions. The data mining approach uses a set of dirsuption related input keywords to identify similar keywords within the live Twitter data. By collecting historical data we were able to create deep learning models that represent the vocabulary from the disruptions related Tweets in the form of a graph. A graph is a collection of data values where the data points are connected to one another based on their relationships. A longer chain of connection between two words defines a weak relationship, a shorter chain defines a stronger relationship. In our graph, words with similar contextual meanings are connected to each other over shorter distances, compared to words with different meanings. At the end we use a neural network as a classifier to scan this graph to learn the semantic relationships within our data. Afterwards, this learned information can be used to accurately classify the disruption related Tweets within a pool of random Tweets. Once all the proposed approaches have been developed, a benchmark evaluation is performed against other existing text classification techniques, to justify the effectiveness of the approaches. The final results indicate that the proposed graph based models achieved a higher accuracy, compared to the data mining model, and also outperformed all the other baseline models. Our Tweet-Level GCN had the highest accuracy of 89.9%.
Michel, Pablo Anaxágoras. « Análise explorátoria de dados sócio-econômicos de vestibulandos ». Florianópolis, SC, 2002. http://repositorio.ufsc.br/xmlui/handle/123456789/83690.
Texte intégralMade available in DSpace on 2012-10-20T02:25:29Z (GMT). No. of bitstreams: 0
Ao longo dos anos, as mais variadas organizações acumularam milhares de informações que ajudaram as empresas a evoluir e conquistar mercado, permitindo que os administradores, baseados nelas, de diferentes formas, tomassem decisões. Novas e avançadas ferramentas automatizadas de apoio a decisão têm sido desenvolvidas para auxiliar o administrador a decidir os rumos de seu negócio, possibilitando descobrir, no meio da massa de dados, aquilo que realmente interessa. Data mining ou mineração de dados é o processo de extração de informação de grandes bancos de dados. Este processo pode ser visto como uma nova disciplina na interface da estatística, do aprendizado de máquina, do reconhecimento de padrão e da tecnologia de bancos de dados. O objetivo desse trabalho é propor um modelo utilizando a metodologia do data mining e aplicar as técnicas mais conhecidas (Associação e Agrupamento), na base de dados dos inscritos no vestibular de uma instituição de ensino superior, visando obter um conhecimento aprofundado e informações desconhecidas sobre os candidatos. Os dados analisados são provenientes da ficha sócio-econômica e, para se executar todas as três técnicas citadas, necessitou-se aplicar previamente rotinas de limpeza, removendo dados ausentes (em branco) e convertendo em um valor padrão dados sujos (não pertencentes aos limites válidos). A apresentação dos resultados obtidos sugerirá alguns passos ou ações a serem tomadas no intuito de melhorar a qualidade do ensino superior e a satisfação dos alunos.
Corley, Courtney David. « Social Network Simulation and Mining Social Media to Advance Epidemiology ». Thesis, University of North Texas, 2009. https://digital.library.unt.edu/ark:/67531/metadc11053/.
Texte intégralTorres, Alvarez Hernán. « Mineral exploration, junior mining companies and aspects to be considered for its promotion ». IUS ET VERITAS, 2016. http://repositorio.pucp.edu.pe/index/handle/123456789/122605.
Texte intégralEl autor hace un análisis acerca de las medidas a tomar en cuenta para promover las actividades mineras, poniendo especial énfasis en el área de la exploración como actividad principal de la industria minera. De tal forma, que el presente artículo se centra en todo aquello que compone dicha actividad, desde sus principales actores hasta las consideraciones a tomar en cuenta para su regulación y la eficacia de la misma. Finalmente el autor expone sus conclusiones centrándose en la importancia que genera la inversión y por tanto la expedición para implementar los mejores mecanismos en el rubro minero.
Deller, Yannick. « Raw Data for Peace and Security - The Extraction and Mining of People's Behaviour ». Thesis, Malmö universitet, Fakulteten för kultur och samhälle (KS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-22599.
Texte intégralKumar, Arun. « Ground control ramifications and economic impact of retreat mining on room and pillar coal mines ». Diss., Virginia Polytechnic Institute and State University, 1986. http://hdl.handle.net/10919/49815.
Texte intégralPh. D.
incomplete_metadata
Franzke, Maximilian [Verfasser], et Matthias [Akademischer Betreuer] Renz. « Querying and mining heterogeneous spatial, social, and temporal data / Maximilian Franzke ; Betreuer : Matthias Renz ». München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2019. http://d-nb.info/1190563630/34.
Texte intégral« Algorithmic aspects of social network mining ». 2013. http://library.cuhk.edu.hk/record=b5884351.
Texte intégralThesis (Ph.D.)--Chinese University of Hong Kong, 2013.
Includes bibliographical references (leaves 157-171).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Abstract also in Chinese.
Li, Ronghua = She hui wang luo wa jue de suan fa wen ti yan jiu / Li Ronghua.
Miskelly, Kenna Jill. « Exploring ethical issues in data mining : the role of collective privacy ». Thesis, 2006. http://hdl.handle.net/1828/2302.
Texte intégralNkosi, Lolah. « Social impact of mining ». Thesis, 2015. http://hdl.handle.net/10210/13886.
Texte intégralMining is an activity which contributes greatly and positively to a country’s economic development by creating job opportunities, development of roads, health care centres and educational facilities. However, mining in certain instances can also have a long lasting negative environmental and social impact on communities. The focus of this dissertation will be to address those instances where mining has a negative social impact on the communities where such mining projects are taking place. The negative social impact of mining in certain cases is regarded as a universal phenomenon. Citizens of many countries where mining activities take place i.e. “mining counties” especially in the under-developed, developing and countries with economies in transition, such as Ghana, Mali, South Africa and Tanzania in an African Continent are confronted with an array of negative consequences associated with the negative social impact of mining activities. However this does not mean that other continents are immune from this. Asian countries such as Paupau New Guinea, India, and China are also faced with the negative social impact of mining.
Sigodi, Mzontsundu Gugulethu. « Corporate social investment by mining companies ». Thesis, 2014. http://hdl.handle.net/10210/11865.
Texte intégralCorporate social investment (CSI) does not have a universal definition, but corporations tend to interpret it according to the extent of their activity in community social programmes of development. It is of particular importance in South Africa given the fact that South Africa is still a developing country that struggles with high unemployment and inequality. This dissertation explores this concept of CSI in research that was conducted in the community of Letswaleng (Embalenhle), in Mpumalanga, in order to establish whether there is a relationship between the mining company that operates in the community and the community within which it operates. Mining corporations continue to assume little responsibility for the health, education or housing of the families of their black employees while operating in monopolistic conditions and making exorbitant profits. A wide variety of these mining opportunities have attracted multinational enterprises and local firms to invest in the region of Mpumalanga. The purpose of the research was to explore the relationship between the community and the mining company in terms of CSI initiatives. It was also to establish if there are any community structures to ensure that the mining company does consult with the community in making sure that they are kept informed concerning the plans of the mining house within the community. The nature of this research was exploratory, qualitative research and, for this reason, structured interviews were conducted and these were face-to-face. Corporate social investment is an issue that the government needs to take seriously by setting up audit committees to monitor the implementation of these ventures. Government structures such as the Department of Trade and Industry need to fund community structures in order for them to be more effective.
LIN, YUNG-JIE, et 林勇傑. « Data Mining For Social Medlia Marketing Application ». Thesis, 2019. http://ndltd.ncl.edu.tw/handle/8hyrvq.
Texte intégral國立勤益科技大學
資訊工程系
107
With the development of the Internet, a platform for consumers to share their products on the Internet, but less to discuss the platform of food, in order to understand the satisfaction of consumers, traditional catering companies can only Using paper to let customers fill in, this method additionally increases the printing cost of paper, and cannot give feedback immediately. In this thesis, we use web crawler technology to capture webpage comments of social media to collect data and understand consumers’ degree of satisfaction through text analysis. By researching consumer reviews, we can improve the restaurant's business model and enhance the restaurant's performance. The restaurant can keep abreast of the information and let the restaurant know more about the customer's needs.