Academic literature on the topic 'Data mining – social aspects'
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Journal articles on the topic "Data mining – social aspects"
Eltaher, Mohammed, and Jeongkyu Lee. "Social User Mining." International Journal of Multimedia Data Engineering and Management 4, no. 4 (October 2013): 58–70. http://dx.doi.org/10.4018/ijmdem.2013100104.
Full textAl-Saggaf, Yeslam, and Md Zahidul Islam. "Data Mining and Privacy of Social Network Sites’ Users: Implications of the Data Mining Problem." Science and Engineering Ethics 21, no. 4 (June 12, 2014): 941–66. http://dx.doi.org/10.1007/s11948-014-9564-6.
Full textWang, Chen-Ya, and Hsia-Ching Chang. "Choice Modeling of Enterprise Social Media Adoptions." International Journal of E-Adoption 11, no. 1 (January 2019): 12–24. http://dx.doi.org/10.4018/ijea.2019010102.
Full textMir, J., A. Mahmood, and S. Khatoon. "Aspect Βased Classification Model for Social Reviews." Engineering, Technology & Applied Science Research 7, no. 6 (December 18, 2017): 2296–302. http://dx.doi.org/10.48084/etasr.1578.
Full textLICCUD-AMBEGUIA, FLORENCE H. "ENHANCING MINING COMMUNITY SERVICES THROUGH CORPORATE SOCIAL RESPONSIBILITY AND SOCIAL DEVELOPMENT MANAGEMENT STRATEGIES." Cognizance Journal of Multidisciplinary Studies 3, no. 11 (November 30, 2023): 58–97. http://dx.doi.org/10.47760/cognizance.2023.v03i11.006.
Full textDahish, Zahra, and Shah J. Miah. "EXPLORING SENTIMENT ANALYSIS RESEARCH: A SOCIAL MEDIA DATA PERSPECTIVE." International Journal on Soft Computing 14, no. 1 (February 27, 2023): 1–12. http://dx.doi.org/10.5121/ijsc.2023.14101.
Full textHuang, Chi-Yo, Chia-Lee Yang, and Yi-Hao Hsiao. "A Novel Framework for Mining Social Media Data Based on Text Mining, Topic Modeling, Random Forest, and DANP Methods." Mathematics 9, no. 17 (August 25, 2021): 2041. http://dx.doi.org/10.3390/math9172041.
Full textTrandafili, Evis, and Marenglen Biba. "A Review of Machine Learning and Data Mining Approaches for Business Applications in Social Networks." International Journal of E-Business Research 9, no. 1 (January 2013): 36–53. http://dx.doi.org/10.4018/jebr.2013010103.
Full textCAO, LONGBING, and CHENGQI ZHANG. "THE EVOLUTION OF KDD: TOWARDS DOMAIN-DRIVEN DATA MINING." International Journal of Pattern Recognition and Artificial Intelligence 21, no. 04 (June 2007): 677–92. http://dx.doi.org/10.1142/s0218001407005612.
Full textSeadle, Michael S. "Managing and mining historical research data." Library Hi Tech 34, no. 1 (March 21, 2016): 172–79. http://dx.doi.org/10.1108/lht-09-2015-0086.
Full textDissertations / Theses on the topic "Data mining – social aspects"
Chen, Weidong. "Discovering communities by information diffusion and link density propagation." HKBU Institutional Repository, 2012. https://repository.hkbu.edu.hk/etd_ra/1422.
Full textNguyen, 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.
Full textYang, Shuang-Hong. "Predictive models for online human activities." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43689.
Full textCai, Zhongming. "Technical aspects of data mining." Thesis, Cardiff University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.395784.
Full textEriksson, Jesper, and 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.
Full textWang, Guan. "Graph-Based Approach on Social Data Mining." Thesis, University of Illinois at Chicago, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3668648.
Full textPowered 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.
Full textCosta, 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/.
Full textO 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/.
Full textAlsaleh, 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.
Full textBooks on the topic "Data mining – social aspects"
1954-, Eyob Ephrem, ed. Social implications of data mining and information privacy: Interdisciplinary frameworks and solutions. Hershey PA: Information Science Reference, 2009.
Find full textAbraham, Ajith. Computational Social Networks: Mining and Visualization. London: Springer London, 2012.
Find full textBoellstorff, Tom, and Bill Maurer. Data, now bigger and better! Chicago: Prickly Paradigm Press, 2015.
Find full textEric, Hunter. The Sherlock syndrome: Strategic success through big data and the Darwinian disruption. London: Ark Group, 2014.
Find full textMayer-Schönberger, Viktor. Big data: Rewolucja, która zmieni nasze myślenie, pracę i życie. Warszawa: MT Biznes, 2014.
Find full textSin, Tong-hŭi. Pik teit'ŏllŏji. Sŏul-si: K'ŏmyunik'eisyŏn Puksŭ, 2015.
Find full textMayer-Schönberger, Viktor. Dữ liệu lớn: Cuộc cách mạng sẽ làm thay đổi cách chúng ta sống, làm việc và tư duy. TP. Hồ Chí Minh: Nhà xuất bản Trẻ, 2014.
Find full textMayer-Schönberger, Viktor. Big data: A revolution that will transform how we live, work, and think. Boston: Mariner Books, Houghton Mifflin Harcourt, 2014.
Find full textMayer-Schonberger, Viktor. Da shu ju shi dai. 8th ed. Hangzhou: Zhejiang ren min chu ban she, 2013.
Find full textMemon, Nasrullah, and Reda Alhajj. From sociology to computing in social networks: Theory, foundations and applications. Wien: Springer, 2010.
Find full textBook chapters on the topic "Data mining – social aspects"
Hochreiter, Ronald, and Christoph Waldhauser. "Data Mining Cultural Aspects of Social Media Marketing." In Advances in Data Mining. Applications and Theoretical Aspects, 130–43. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08976-8_10.
Full textTung, Kuan-Chieh, En Tzu Wang, and Arbee L. P. Chen. "Mining Event Sequences from Social Media for Election Prediction." In Advances in Data Mining. Applications and Theoretical Aspects, 266–81. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41561-1_20.
Full textJin, Fang, Wei Wang, Prithwish Chakraborty, Nathan Self, Feng Chen, and Naren Ramakrishnan. "Tracking Multiple Social Media for Stock Market Event Prediction." In Advances in Data Mining. Applications and Theoretical Aspects, 16–30. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62701-4_2.
Full textNohuddin, Puteri N. E., Rob Christley, Frans Coenen, and Christian Setzkorn. "Trend Mining in Social Networks: A Study Using a Large Cattle Movement Database." In Advances in Data Mining. Applications and Theoretical Aspects, 464–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14400-4_36.
Full textAl Essa, Ali, and Miad Faezipour. "MapReduce and Spark-Based Analytic Framework Using Social Media Data for Earlier Flu Outbreak Detection." In Advances in Data Mining. Applications and Theoretical Aspects, 246–57. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62701-4_19.
Full textHuang, Chun-Che, Yu-Jie Fang, Shian-Hua Lin, Wen-Yau Liang, and Shu-Rong Wu. "Development of Issue Sets from Social Big Data: A Case Study of Green Energy and Low-Carbon." In Advances in Data Mining. Applications and Theoretical Aspects, 139–53. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41561-1_11.
Full textBorms, Samuel, Kris Boudt, Frederiek Van Holle, and Joeri Willems. "Semi-supervised Text Mining for Monitoring the News About the ESG Performance of Companies." In Data Science for Economics and Finance, 217–39. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66891-4_10.
Full textAggarwal, Charu C. "Social Network Analysis." In Data Mining, 619–61. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14142-8_19.
Full textKapoor, Komal, and Jaideep Srivastava. "Data Mining." In Encyclopedia of Social Network Analysis and Mining, 332–41. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6170-8_56.
Full textMarcus, Sherry E., Melanie Moy, and Thayne Coffman. "Social Network Analysis." In Mining Graph Data, 443–68. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2006. http://dx.doi.org/10.1002/9780470073049.ch17.
Full textConference papers on the topic "Data mining – social aspects"
Gino, Henrique L. S., Diogenes S. Pedro, Jean R. Ponciano, Claudio D. G. Linhares, and Agma J. M. Traina. "Exploratory Analysis on Market Basket Data using Network Visualization." In Brazilian Workshop on Social Network Analysis and Mining. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/brasnam.2023.229505.
Full textChang, Chia-Hui, and Kun-Chang Tsai. "Aspect Summarization from Blogsphere for Social Study." In 2007 Seventh IEEE International Conference on Data Mining - Workshops (ICDM Workshops). IEEE, 2007. http://dx.doi.org/10.1109/icdmw.2007.42.
Full text"E-Government Development Models: Review of Social-Technical Security Aspect." In International conference on Intelligent Systems, Data Mining and Information Technology. International Institute of Engineers, 2014. http://dx.doi.org/10.15242/iie.e0414057.
Full textSouza, Bruno Á., Alice A. F. Menezes, Carlos M. S. Figueiredo, Fabíola G. Nakamura, and Eduardo F. Nakamura. "Detecção de Categorias de Aspectos Utilizando Redes Neurais Profundas em Avaliações Online." In VII Brazilian Workshop on Social Network Analysis and Mining. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/brasnam.2018.3582.
Full textKim, Seungbae, Jyun-Yu Jiang, and Wei Wang. "Discovering Undisclosed Paid Partnership on Social Media via Aspect-Attentive Sponsored Post Learning." In WSDM '21: The Fourteenth ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3437963.3441803.
Full textVukašinović, Sandra. "Demographic aspects of the planned resettlement of settlements in Lazarevac municipality." In Population in Post-Yugoslav Countries: (Dis)Similarities and Perspectives. Institute of Social Sciences, 2024. http://dx.doi.org/10.59954/ppycdsp2024.50.
Full textInácio, Andrei De Souza, Leandro Takeshi Hatori, Matheus Gutoski, André Eugênio Lazzaretti, and Heitor Silvério Lopes. "Análise da fragmentação partidária na Assembleia Legislativa do Rio Grande do Sul com Métodos de Mineração de Dados." In VIII Brazilian Workshop on Social Network Analysis and Mining. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/brasnam.2019.6558.
Full textDuluta, Andreistefan, Stefan Mocanu, Daniela Saru, Radu nicolae Pietraru, and Mihai Craciunescu. "MODERN TECHNIQUES ON LEARNING STRATEGIES SUPPORTED BY DATA MINING ANALYSIS." In eLSE 2019. Carol I National Defence University Publishing House, 2019. http://dx.doi.org/10.12753/2066-026x-19-100.
Full textMorina, Gazmend, and Gani Kastrati. "ENVIRONMENTAL EXPENDITURE OF ENTERPRISES, IN MINING SECTOR IN KOSOVO." In 22nd SGEM International Multidisciplinary Scientific GeoConference 2022. STEF92 Technology, 2022. http://dx.doi.org/10.5593/sgem2022/5.1/s21.072.
Full textBustamante, Juan, Leonardo Kuffo, Edgar Izquierdo, and Carmen Vaca. "Automated Detection of Customer Experience through Social Platforms." In CARMA 2018 - 2nd International Conference on Advanced Research Methods and Analytics. Valencia: Universitat Politècnica València, 2018. http://dx.doi.org/10.4995/carma2018.2018.8347.
Full textReports on the topic "Data mining – social aspects"
Fairchild, Geoffrey, Rian Mustafa Bahran, and Garrett Earl McMath. Working Group on Social Internet Data Mining and Analytics for Nuclear Nonproliferation. Office of Scientific and Technical Information (OSTI), October 2015. http://dx.doi.org/10.2172/1223758.
Full textPriester, Michael, Malaika Masson, and Martin Walter. Incentivizing Clean Technology in the Mining Sector in Latin America and the Caribbean: The Role of Public Mining Institutions. Inter-American Development Bank, December 2013. http://dx.doi.org/10.18235/0009148.
Full textRipoll, Santiago, Jennifer Cole, Olivia Tulloch, Megan Schmidt-Sane, and Tabitha Hrynick. SSHAP: 6 Ways to Incorporate Social Context and Trust in Infodemic Management. Institute of Development Studies (IDS), January 2021. http://dx.doi.org/10.19088/sshap.2021.001.
Full textRipoll, Santiago, Jennifer Cole, Olivia Tulloch, Megan Schmidt-Sane, and Tabitha Hrynick. SSHAP: 6 Ways to Incorporate Social Context and Trust in Infodemic Management. Institute of Development Studies (IDS), January 2021. http://dx.doi.org/10.19088/sshap.2021.001.
Full textSchmidt-Sane, Megan, Tabitha Hrynick, Jennifer Cole, Santiago Ripoll, and Olivia Tulloch. SSHAP: 6 Ways to Incorporate Social Context and Trust in Infodemic Management. Institute of Development Studies (IDS), January 2021. http://dx.doi.org/10.19088/sshap.2021.009.
Full textOppel, Annalena. Beyond Informal Social Protection – Personal Networks of Economic Support in Namibia. Institute of Development Studies (IDS), November 2020. http://dx.doi.org/10.19088/ids.2020.002.
Full textLora, Eduardo. The Distance between Perception and Reality in the Social Domains of Life. Inter-American Development Bank, August 2013. http://dx.doi.org/10.18235/0011489.
Full textMayne, Alison, Christina Noble, Paula Duffy, Kirsten Gow, Alexander Glasgow, Kevin O’Neill, Jeni Reid, and Diana Valero. Navigating Digital Ethics for Rural Research: Guidelines and recommendations for researchers and administrators of social media groups. DigiEthics: Navigating Digital Ethics for Rural Research, November 2023. http://dx.doi.org/10.57064/2164/22326.
Full textDeJaeghere, Joan, Vu Dao, Bich-Hang Duong, and Phuong Luong. Inequalities in Learning in Vietnam: Teachers’ Beliefs About and Classroom Practices for Ethnic Minorities. Research on Improving Systems of Education (RISE), February 2021. http://dx.doi.org/10.35489/bsg-rise-wp_2021/061.
Full textWickenden, Mary. Practical Guides for Participatory methods: Disability Inclusive Research. Institute of Development Studies, August 2023. http://dx.doi.org/10.19088/ids.2023.045.
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