Bibliografie tematiche / Data mining – social aspects

Letteratura scientifica selezionata sul tema "Data mining – social aspects"

Autore: Grafiati

Pubblicato: 26 luglio 2024

Ultima modifica: 26 luglio 2024

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Articoli di riviste sul tema "Data mining – social aspects"

Eltaher, Mohammed, e Jeongkyu Lee. "Social User Mining". International Journal of Multimedia Data Engineering and Management 4, n. 4 (ottobre 2013): 58–70. http://dx.doi.org/10.4018/ijmdem.2013100104.

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In recent years, the pervasive use of social media has generated huge amounts of data that starts to gain a lot of attentions. Each social media source utilizes different data types such as textual and visual. For example, Twitter1 is for a short text message, Flickr2 is for images and videos, and Facebook3 allows all of these data types. It is highly desired to find patterns of social media users from such different data formats. With the use of data mining techniques, the social media data opens a lot of opportunities for researchers. Despite of its short history, social media mining has become very active research area. This paper provides a comprehensive survey on recent research on social user mining. In particular, the survey focuses on two aspects: (1) social user mining based on data types, such as textual, visual, and both textual and visual information, and (2) social user mining based on mining techniques. In addition, we present our current research on social user mining as well as its future directions.

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Al-Saggaf, Yeslam, e Md Zahidul Islam. "Data Mining and Privacy of Social Network Sites’ Users: Implications of the Data Mining Problem". Science and Engineering Ethics 21, n. 4 (12 giugno 2014): 941–66. http://dx.doi.org/10.1007/s11948-014-9564-6.

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Wang, Chen-Ya, e Hsia-Ching Chang. "Choice Modeling of Enterprise Social Media Adoptions". International Journal of E-Adoption 11, n. 1 (gennaio 2019): 12–24. http://dx.doi.org/10.4018/ijea.2019010102.

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To date, many studies focusing on the adoption rates of social media platforms in Fortune 500 firms have been conducted; however, little is known of the adoption time of such platforms, and the relationships between different social media adoptions. This study explores these aspects of social media using a proposed analysis integrating econometric analysis and data mining. Granger causality assists in constructing causal forecasting models of social media adoption time, whereas association rule mining, which can be visualized by dependency network graphs, contributes to understanding hidden relationships among enterprise social media adoption choices. The proposed analysis can account for the unexplained phenomena in a complementary way because different aspects can be drawn from the results of both econometric analysis and data mining.

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Mir, J., A. Mahmood e S. Khatoon. "Aspect Βased Classification Model for Social Reviews". Engineering, Technology & Applied Science Research 7, n. 6 (18 dicembre 2017): 2296–302. http://dx.doi.org/10.48084/etasr.1578.

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Aspect based opinion mining investigates deeply, the emotions related to one’s aspects. Aspects and opinion word identification is the core task of aspect based opinion mining. In previous studies aspect based opinion mining have been applied on service or product domain. Moreover, product reviews are short and simple whereas, social reviews are long and complex. However, this study introduces an efficient model for social reviews which classifies aspects and opinion words related to social domain. The main contributions of this paper are auto tagging and data training phase, feature set definition and dictionary usage. Proposed model results are compared with CR model and Naïve Bayes classifier on same dataset having accuracy 98.17% and precision 96.01%, while recall and F1 are 96.00% and 96.01% respectively. The experimental results show that the proposed model performs better than the CR model and Naïve Bayes classifier.

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LICCUD-AMBEGUIA, FLORENCE H. "ENHANCING MINING COMMUNITY SERVICES THROUGH CORPORATE SOCIAL RESPONSIBILITY AND SOCIAL DEVELOPMENT MANAGEMENT STRATEGIES". Cognizance Journal of Multidisciplinary Studies 3, n. 11 (30 novembre 2023): 58–97. http://dx.doi.org/10.47760/cognizance.2023.v03i11.006.

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The Philippine Mining Act of 1995 mandated mining companies to contribute to the development of host and neighboring communities through Social Development Management Programs (SDMP). The subsequent CSR Act of 2011 and 2013 further institutionalized Corporate Social Responsibility (CSR) nationwide. This research examines the implementation of CSR and SDMP by two mining companies in Benguet, Lepanto Consolidated Mining Company and Philex Mining Corporation. It explores the strategies employed, their effectiveness, challenges encountered, and their association with project implementation. Data was collected through questionnaires and interviews, supported by primary and secondary sources. Findings show that programs related to education, livelihood, and infrastructure development were more extensively and effectively implemented, with integrated planning and a mix of top-down and bottom-up strategies contributing to success. The impact on host communities was particularly positive in social and economic aspects, with minimal effects on technological, political, and environmental aspects. Challenges ranged from resource limitations to community ambivalence, natural events, and legal complications. The study concludes that a dual standard exists due to the prioritization of mandatory SDMP programs over non-mandatory CSR initiatives, despite communities perceiving them as nearly identical. Recommendations include making CSR mandatory and complementary to SDMP, enhancing monitoring, and securing support from local governments and NGOs. Strategic implementation and effective strategies are crucial for realizing responsible mining in Benguet.

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Dahish, Zahra, e Shah J. Miah. "EXPLORING SENTIMENT ANALYSIS RESEARCH: A SOCIAL MEDIA DATA PERSPECTIVE". International Journal on Soft Computing 14, n. 1 (27 febbraio 2023): 1–12. http://dx.doi.org/10.5121/ijsc.2023.14101.

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Sentiment analysis has been rapidly employed for business decision support. New data mining researchers are yet to have an adequate understanding of the various applications of sentiment analysis while utilising social media data. As a result, it is critical to define the data mining and text analytics research trend holistically using existing literature. The study explores sentiment analysis research for its application in transforming social media data and identifies relevant research aspects through a comprehensive bibliometric review of 523 research articles published in the Scopus database (between 2018 and 2022) to discern the content and thematic analysis. Findings suggested that key purposes of the sentiment analysis are mainly related to innovation, transparency, and efficiency. Our review also highlights the distinctiveness of sentiment analysis for synthesising social media information to investigate various features, including the knowledge-domain map that detects author collaboration networks in the past.

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Huang, Chi-Yo, Chia-Lee Yang e Yi-Hao Hsiao. "A Novel Framework for Mining Social Media Data Based on Text Mining, Topic Modeling, Random Forest, and DANP Methods". Mathematics 9, n. 17 (25 agosto 2021): 2041. http://dx.doi.org/10.3390/math9172041.

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The huge volume of user-generated data on social media is the result of the aggregation of users’ personal backgrounds, past experiences, and daily activities. This huge size of the generated data, the so-called “big data,” has been studied and investigated intensively during the past few years. In spite of the impression one may get from the media, a great deal of data processing has not been uncovered by existing techniques of data engineering and processing. However, very few scholars have tried to do so, especially from the perspective of multiple-criteria decision-making (MCDM). These MCDM methods can derive influence relationships and weights associated with aspects and criteria, which can hardly be achieved by traditional data analytics and statistical approaches. Therefore, in this paper, we aim to propose an analytic framework to mine social networks, feed the meaningful information via MCDM methods based on a theoretical framework, derive causal relationships among the aspects of the theoretical framework, and finally compare the causal relationships with a social theory. Latent Dirichlet allocation (LDA) will be adopted to derive topic models based on the data retrieved from social media. By clustering the topics into aspects of the social theory, the probability associated with each aspect will be normalized and then transformed to a Likert-type 5-point scale. Afterwards, for every topic, the feature importance of all other topics will be derived using the random forest (RF) algorithm. The feature importance matrix will be transformed to the initial influence matrix of the decision-making trial and evaluation laboratory (DEMATEL). The influence relationships among the aspects and criteria and influence weights can then be derived by using the DEMATEL-based analytic network process (DANP). The influence weight versus each criterion can be derived by using DANP. To verify the feasibility of the proposed framework, Taiwanese users’ attitudes toward air pollution will be analyzed based on the value–belief–norm (VBN) theory by using social media data retrieved from Dcard (dcard.tw). Based on the analytic results, the causal relationships are fully consistent with the VBN framework. Further, the mutual influences derived in this work that were seldom discussed by earlier works, i.e., the mutual influences between altruistic concerns and egoistic concerns, as well as those between altruistic concerns and biosphere concerns, are worth further investigation in future.

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Trandafili, Evis, e Marenglen Biba. "A Review of Machine Learning and Data Mining Approaches for Business Applications in Social Networks". International Journal of E-Business Research 9, n. 1 (gennaio 2013): 36–53. http://dx.doi.org/10.4018/jebr.2013010103.

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Social networks have an outstanding marketing value and developing data mining methods for viral marketing is a hot topic in the research community. However, most social networks remain impossible to be fully analyzed and understood due to prohibiting sizes and the incapability of traditional machine learning and data mining approaches to deal with the new dimension in the learning process related to the large-scale environment where the data are produced. On one hand, the birth and evolution of such networks has posed outstanding challenges for the learning and mining community, and on the other has opened the possibility for very powerful business applications. However, little understanding exists regarding these business applications and the potential of social network mining to boost marketing. This paper presents a review of the most important state-of-the-art approaches in the machine learning and data mining community regarding analysis of social networks and their business applications. The authors review the problems related to social networks and describe the recent developments in the area discussing important achievements in the analysis of social networks and outlining future work. The focus of the review in not only on the technical aspects of the learning and mining approaches applied to social networks but also on the business potentials of such methods.

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CAO, LONGBING, e CHENGQI ZHANG. "THE EVOLUTION OF KDD: TOWARDS DOMAIN-DRIVEN DATA MINING". International Journal of Pattern Recognition and Artificial Intelligence 21, n. 04 (giugno 2007): 677–92. http://dx.doi.org/10.1142/s0218001407005612.

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Traditionally, data mining is an autonomous data-driven trial-and-error process. Its typical task is to let data tell a story disclosing hidden information, in which domain intelligence may not be necessary in targeting the demonstration of an algorithm. Often knowledge discovered is not generally interesting to business needs. Comparably, real-world applications rely on knowledge for taking effective actions. In retrospect of the evolution of KDD, this paper briefly introduces domain-driven data mining to complement traditional KDD. Domain intelligence is highlighted towards actionable knowledge discovery, which involves aspects such as domain knowledge, people, environment and evaluation. We illustrate it through mining activity patterns in social security data.

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Seadle, Michael S. "Managing and mining historical research data". Library Hi Tech 34, n. 1 (21 marzo 2016): 172–79. http://dx.doi.org/10.1108/lht-09-2015-0086.

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Purpose – The purpose of this paper is to review how historical research data are managed and mined today. Design/methodology/approach – The methodology builds on observations over the last decade. Findings – Reading speed is a factor in managing the quantity of text in historical research. Twenty years ago historical research involved visits to physical libraries and archives, but today much of the information is online. The granularity of reading has changed over recent decades and recognizing this change is an important factor in improving acce. Practical implications – Computer-based humanities text mining could be simpler if publishers and libraries would manage the data in ways that facilitate the process. Some aspects still need development, including better context awareness, either by writing context awareness into programs or by encoding it in the text. Social implications – Future researchers who want to make use of text mining and distant reading techniques will need more thorough technical training than they get today. Originality/value – There is relatively little discussion of text mining and distant reading in the LIS literature.

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Tesi sul tema "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.

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Nguyen, 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.

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Analyzing a huge amount of data becomes a tricky challenge and an opportunity for data miners and businessmen today. Knowledge management processes can deal with big knowledge source to find tacit intelligence making businesses more agile and effective. Data mining is a powerful tool working with big data to create capabilities of forecasting and analysis. Yet there is a lack of research on where and how data mining can add value in knowledge management processes in organizations to maximize valuable knowledge for innovation and business management. The knowledge management processes of a psychiatry section in a Swedish hospital was used as a case study for this thesis. Interviews with manager, psychiatrist, auxiliary nurse and data scientists are conducted. Collected data is analyzed to create values of data mining based on a value creation framework through the knowledge management processes of psychiatry section in the hospital. Relying on this process, the limitations and strengths are exposed; whereby, a data mining implementing framework is formulated, and potentials of data mining for the process are suggested to support for all employees of psychiatry section in the hospital in decision making and caring for patients.

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Yang, Shuang-Hong. "Predictive models for online human activities". Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43689.

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The availability and scale of user generated data in online systems raises tremendous challenges and opportunities to analytic study of human activities. Effective modeling of online human activities is not only fundamental to the understanding of human behavior, but also important to the online industry. This thesis focuses on developing models and algorithms to predict human activities in online systems and to improve the algorithmic design of personalized/socialized systems (e.g., recommendation, advertising, Web search systems). We are particularly interested in three types of online user activities, i.e., decision making, social interactions and user-generated contents. Centered around these activities, the thesis focuses on three challenging topics: 1. Behavior prediction, i.e., predicting users' online decisions. We present Collaborative-Competitive Filtering, a novel game-theoretic framework for predicting users' online decision making behavior and leverage the knowledge to optimize the design of online systems (e.g., recommendation systems) in respect of certain strategic goals (e.g., sales revenue, consumption diversity). 2. Social contagion, i.e., modeling the interplay between social interactions and individual behavior of decision making. We establish the joint Friendship-Interest Propagation model and the Behavior-Relation Interplay model, a series of statistical approaches to characterize the behavior of individual user's decision making, the interactions among socially connected users, and the interplay between these two activities. These techniques are demonstrated by applications to social behavior targeting. 3. Content mining, i.e., understanding user generated contents. We propose the Topic-Adapted Latent Dirichlet Allocation model, a probabilistic model for identifying a user's hidden cognitive aspects (e.g., knowledgability) from the texts created by the user. The model is successfully applied to address the challenge of ``language gap" in medical information retrieval.

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Cai, Zhongming. "Technical aspects of data mining". Thesis, Cardiff University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.395784.

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Eriksson, Jesper, e 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.

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Businesses today are often operating in a highly competitive environment where information is a noticeably valuable asset. Businesses are therefore in need of powerful tools for extracting actionable business knowledge. Research show that SME companies are lagging behind large companies in the use of data analytics; even though they know the potential benefits. We want to study and compare different tools for data analytics and how they can be used by small companies. Our research questions are therefore: what analytical tools are today available on the market, and what are their possibilities and challenges for small companies? And: how can these analytical tools aid in the development of a business, product or service? We conclude in our research that there are several data analytics tools available for small businesses, that their different usages can be applied successfully and without big cost, and that their relevance, both in business development and innovation, depends on the business objectives and goals of their utilization.

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Wang, Guan. "Graph-Based Approach on Social Data Mining". Thesis, University of Illinois at Chicago, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3668648.

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Powered 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.

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Ip, Lai Cheng. "Mining on social network community for marketing". Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3950661.

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Costa, 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/.

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Social media services have a growing impact in our society. Individuals often rely on social media to get their news, decide which products to buy or to communicate with their friends. As consequence of the widespread adoption of social media, a large volume of data on how users behave is created every day and stored into large databases. Learning how to analyze and extract useful knowledge from this data has a number of potential applications. For instance, a deeper understanding on how legitimate users interact with social media services could be explored to design more accurate spam and fraud detection methods. This PhD research is based on the following hypothesis: data generated by social media users present patterns that can be exploited to improve the effectiveness of tasks such as prediction, forecasting and modeling in the domain of social media. To validate our hypothesis, we focus on designing data mining methods tailored to social media data. The main contributions of this PhD can be divided into three parts. First, we propose Act-M, a mathematical model that describes the timing of users actions. We also show that Act-M can be used to automatically detect bots among social media users based only on the timing (i.e. time-stamp) data. Our second contribution is VnC (Vote-and-Comment), a model that explains how the volume of different types of user interactions evolve over time when a piece of content is submitted to a social media service. In addition to accurately matching real data, VnC is useful, as it can be employed to forecast the number of interactions received by social media content. Finally, our third contribution is the MFS-Map method. MFS-Map automatically provides textual annotations to social media images by efficiently combining visual and metadata features. Our contributions were validated using real data from several social media services. Our experiments show that the Act-M and VnC models provided a more accurate fit to the data than existing models for communication dynamics and information diffusion, respectively. MFS-Map obtained both superior precision and faster speed when compared to other widely employed image annotation methods.
O 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.

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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/.

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Social Networking Sites (SNS) are ubiquitous within modern society, forming communications networks that span across cultural and geographical boundaries. The information posted to these sites provide useful insights into individuals, but can also provide a wealth of information that can be used for further analysis into the surrounding environment. Three main challenges limit the use of this information in applications: the quantity of data is often unmanageable, there is a significant amount of data unavailable for use due to a lack of generic interfaces for access, and there is difficulty in integrating multiple disparate social data sources. The overall aim of the research described in this thesis is to advance the field of data science and improve accessibility of social data in analytical applications, in both academic and commercial settings. This aim has been addressed with three primary contributions; new algorithms to efficiently locate and collect relevant social data, new methods of performing unsupervised data extraction from generic social sites, and the development and subsequent empirical evaluation of a framework to facilitate the collection, integration, storage and presentation of social data for use in applications. The first contribution was the presentation of a search query optimisation algorithm designed to reduce the amount of noise resulting from social data collection by learning from collected content and iteratively building new query keyword sets. The algorithm was empirically evaluated and the results indicated that it provides significantly more data than existing search tools while minimising signal-to-noise ratio. The second contribution aimed to improve access to social data available on Web 2.0 sites but without any existing interface access to the data. The algorithm is designed to extract social data from sites without any a priori knowledge of design or page layout. Its efficacy was empirically evaluated against a testbed consisting of popular news and current affairs websites. Results indicated that the algorithm was very effective at unsupervised retrieval of social data. The third major contribution presented a framework that integrated the previous two contributions into a framework designed to streamline use of social data in academic and commercial applications. The generic, component-based design was evaluated in real-world scenarios and determined to provide a full social collection and analytics workflow in an extensible and scalable manner. This research has theoretical and practical implications for the use of social data in analytical research and commercial use. It extends the data extraction field to include user-generated content, while providing new avenues for performing semi-intelligent social data sourcing, and significantly improves the accessibility of social data.

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Alsaleh, 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.

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This thesis improves the process of recommending people to people in social networks using new clustering algorithms and ranking methods. The proposed system and methods are evaluated on the data collected from a real life social network. The empirical analysis of this research confirms that the proposed system and methods achieved improvements in the accuracy and efficiency of matching and recommending people, and overcome some of the problems that social matching systems usually suffer.

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Più fonti

Libri sul tema "Data mining – social aspects"

1954-, Eyob Ephrem, a cura di. Social implications of data mining and information privacy: Interdisciplinary frameworks and solutions. Hershey PA: Information Science Reference, 2009.

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Abraham, Ajith. Computational Social Networks: Mining and Visualization. London: Springer London, 2012.

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Boellstorff, Tom, e Bill Maurer. Data, now bigger and better! Chicago: Prickly Paradigm Press, 2015.

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Eric, Hunter. The Sherlock syndrome: Strategic success through big data and the Darwinian disruption. London: Ark Group, 2014.

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Mayer-Schönberger, Viktor. Big data: Rewolucja, która zmieni nasze myślenie, pracę i życie. Warszawa: MT Biznes, 2014.

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Sin, Tong-hŭi. Pik teit'ŏllŏji. Sŏul-si: K'ŏmyunik'eisyŏn Puksŭ, 2015.

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Mayer-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.

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Mayer-Schönberger, Viktor. Big data: A revolution that will transform how we live, work, and think. Boston: Mariner Books, Houghton Mifflin Harcourt, 2014.

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author, Cukier Kenneth, Sheng Yangyan translator e Zhou Tao translator, a cura di. Da shu ju shi dai. Hangzhou: Zhejiang ren min chu ban she, 2013.

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From sociology to computing in social networks: Theory, foundations and applications. Wien: Springer, 2010.

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Capitoli di libri sul tema "Data mining – social aspects"

Hochreiter, Ronald, e 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.

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Tung, Kuan-Chieh, En Tzu Wang e 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.

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Jin, Fang, Wei Wang, Prithwish Chakraborty, Nathan Self, Feng Chen e 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.

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Nohuddin, Puteri N. E., Rob Christley, Frans Coenen e 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.

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Al Essa, Ali, e 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.

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Huang, Chun-Che, Yu-Jie Fang, Shian-Hua Lin, Wen-Yau Liang e 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.

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Borms, Samuel, Kris Boudt, Frederiek Van Holle e 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.

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AbstractWe present a general monitoring methodology to summarize news about predefined entities and topics into tractable time-varying indices. The approach embeds text mining techniques to transform news data into numerical data, which entails the querying and selection of relevant news articles and the construction of frequency- and sentiment-based indicators. Word embeddings are used to achieve maximally informative news selection and scoring. We apply the methodology from the viewpoint of a sustainable asset manager wanting to actively follow news covering environmental, social, and governance (ESG) aspects. In an empirical analysis, using a Dutch-written news corpus, we create news-based ESG signals for a large list of companies and compare these to scores from an external data provider. We find preliminary evidence of abnormal news dynamics leading up to downward score adjustments and of efficient portfolio screening.

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Aggarwal, 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.

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Kapoor, Komal, e 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.

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Marcus, Sherry E., Melanie Moy e 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.

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Atti di convegni sul tema "Data mining – social aspects"

Gino, Henrique L. S., Diogenes S. Pedro, Jean R. Ponciano, Claudio D. G. Linhares e 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.

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Market basket analysis is a powerful technique for understanding customer behavior and optimizing business strategies based on that understanding. Market-based analysis over time using visualization techniques can provide insights into market trends and relations, simplify complex data, and communicate insights effectively, which can help organizations make more informed decisions. This paper leverages a dataset focused on the users’ incomes and temporal aspects of market purchases. We modeled this dataset as three distinct temporal networks and performed an exploratory evaluation identifying patterns and anomalies in the data. More specifically, we identified groups of related products, indicating thematic purchases, and evaluated the impact of demographic factors, such as income, on customer spending.

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Chang, Chia-Hui, e 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.

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"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.

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Souza, Bruno Á., Alice A. F. Menezes, Carlos M. S. Figueiredo, Fabíola G. Nakamura e 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.

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Virtual environments such as online stores (e.g. Amazon, Google Play and Booking) adopt a collaborative strategy of evaluation and reputation, where users classify products and services. User's opinion represents the satisfaction level of a rated item. The set of ratings of an item is a reference to its reputation/quality. Therefore, the automatic identification of a usersatisfaction related to an item, considering its textual evaluation, is a tool with singular economic potential. With deep learning researches evolution in sentiment analysis based in aspects, opportunities to apply several neural networks in this context arisen. However, the data representation models applied in these works focus only on Embeddings pre-trained networks as a way to perform feature extraction. In this way, this work aims to present a comparison between data representation techniques and deep networks approaches, to analyze which of them have better results in classifying categories of aspects. Thus, we can seethat TF-IDF with a Convolution Neural Network (CNN) had an F1 measure of 0.93%, being at least 0.02% higher than the others approaches applied in this work.

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Kim, Seungbae, Jyun-Yu Jiang e 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.

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Vukašinović, 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.

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The planned resettlement of settlements in the course of the expansion of open-cast mining in the Kolubara mining basin has an impact on all aspects of social life in the municipality of Lazarevac. Primarily, this impact is reflected in the demographic development of the area. The expansion of coal mining and the immediate proximity to Belgrade, are the cause of significant demographic shifts. The municipality has 33 settlements, a third of which are located in the exploitation area. The number of inhabitants in some settlements is drastically decreasing, and some of them are completely displaced, while on the other side, there is a sudden influx of population in the city of Lazarevac and the secondary centres of the municipality (Veliki Crljeni, Stepojevac), both from the municipal territory and from other parts of Serbia, motivated by the economic advantages of Lazarevac and its surroundings. This fact determines the trend of a constant increase in the number of inhabitants in urban settlements in all observed intercensal periods from 1948 to 2022, while the decline in the number of inhabitants in rural settlements has been observed since the 1981-1991 census period. The sudden increase in the number of inhabitants in urban settlements since the 1980s can be explained by the intensification of mining operations and the process of expropriation taking place in rural settlements. According to the last census in 2022 – 55,146 inhabitants live on the territory of Lazarevac municipality, of which 27,635 live in the only urban settlement in the municipality and 27,511 in rural settlements. The data indicates that for the first time, the urban population in the municipality exceeds the rural population. This paper focuses on analysing the impact of the decades-long development of mining activity on the territory of the municipality of Lazarevac on the population and the comprehensive development of the settlement, both in a positive and negative context. The research also focuses on the transformation of the settlement environment caused by the expansion of open-cast mining, with an emphasis on the changes in the demographic characteristics of the settlement. The paper is based on the analysis of data from the Statistical Office of the Republic of Serbia, settlement regulation plans in the process of resettlement, the plan for the mining area of the Kolubara lignite basin and other sources relevant to the understanding of the given problem, as well as on the spatial representation of data using GIS software and tools. If we compare the dynamics of the expansion of the mines and the population of the Lazarevac municipality (before the start of open-cast mining and today), certain trends can be observed that make it possible to understand the demographic aspect of the process of planned displacement.

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Inácio, Andrei De Souza, Leandro Takeshi Hatori, Matheus Gutoski, André Eugênio Lazzaretti e 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.

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Este trabalho apresenta uma análise, utilizando técnicas de data mining, da fragmentação partidária existente na Assembléia Legislativa do Estado do Rio Grande do Sul. Para isso, foram coletados dados de votação registrados pelos deputados nas diferentes proposituras no perı́odo entre 2000 e 2017. Resultados obtidos sugerem uma alta similaridade entre os diversos partidos polı́ticos existentes com ideologias similares, demonstrando que a quantidade de partidos poderia ser reduzida sem afetar os aspectos ideológicos existentes.

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Duluta, Andreistefan, Stefan Mocanu, Daniela Saru, Radu nicolae Pietraru e 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.

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An attractive learning environment represents one of the key requirements for the students in order to accomplish their educational objectives. Not only the physical location and context influence their capacity to achieve knowledge and to use it creatively, but also the culture in which they learn. Switching from one learning environment to another proves to be a serious challenge for young students like those graduating the high-school and acceding to the university. The two learning environments expose different schedules, requirements, teaching methods, types of interaction (teacher - student and student - student), ways of life (for people who change their social environment), evaluation criteria and consequences of not passing the examinations. Furthermore, within the framework of an undergraduate environment, teaching and learning disciplines like "Computer Programming" or "Introduction to Operating Systems" become even more challenging due to the significant differences in prior knowledge achieved by the students. In order to overcome this issue, teaching strategies should consider active and differentiated learning, otherwise student's motivation could decrease and, as a result, their personal and professional skills might not be developed to their full potential. This paper outlines some particular aspects involved in teaching first year students from the Faculty of Automatic Control and Computer Science - UPB, Systems Engineering section. In addition, the study tries to identify whether there exist strong and objective indicators (besides the direct feedback from the students and the intuition of the tenured professors) for supporting differentiated learning. To establish this, we continue and develop a previous study based on Data Mining analysis performed over various data related to "Computer Programming" course. This time, we investigate if students' results are somehow correlated by extending the Data Warehouse with information regarding the "Introduction to Operating Systems" course. As suggested above, the students' target group is the same, the courses are taught in the same semester and they belong to the same field, so we try to identify if previous background is of some importance and how does it affect current and future learning behavior of the students. The conducted analysis reveals interesting facts which might be used as a baseline for implementing student-oriented learning strategies.

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Morina, Gazmend, e 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.

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Environmental expenditures include all environmental protection expenditures to prevent, reduce and control environmental aspects, impacts and hazards, in addition to the costs of disposal, treatment, hygiene and cleaning. Environmental protection expenditures are defined as investments of enterprises allocated to reduce direct environmental pollution. In this scientific paper we will address the topic of environmental costs of enterprises in the mining sector in Kosovo. All enterprises of the mining industry in Kosovo are obliged by legislation to allocate or plan a budget for environmental expenditures. The Independent Commission for Mines and Minerals is an independent agency defined by the Constitution of the Republic of Kosovo, which regulates mining activities in Kosovo in accordance with the Law on Mines and Minerals, bylaws issued in accordance with the Law on Mines and Minerals and Kosovo Mining Strategy. This institution has determined by administrative instruction the expenses which the enterprises of the mining sector are obliged to deposit in the form of bank guarantees, for the closure of the mine, after the expiration of the license or permit. This type of expense for the company is otherwise called insurance "for all risks to third parties". Collecting high quality and reliable environmental expenditure data is essential for policymakers to develop effective environmental policies and for donors and financial institutions. Environmental criteria consider how a company performs as a nature manager. Mining areas often experience a theme of social tension due to the potential compromise between the expected impact of employment and concerns about environmental damage. Pollution control is a necessary condition for welfare benefits despite new job opportunities in the mining sector. Mining operations often require intensive use of water resources, require land and can create severe environmental externalities, including soil erosion and pollution, air and water, pollution from acid mine drainage, to chemical leakage and sedimentation. During this paper we will be based on some methods of scientific research such as: analysis, synthesis, generalization, specification, etc. We will be based on publications or official reports of relevant institutions, Kosovo and international legislation related to the topics addressed as well as field visits to the mining sector enterprises in Kosovo, which allocate more budget for environmental expenditures, for due to the activity they exercise. Finally, we will give our conclusions regarding the adequacy of environmental expenditures made by mining sector companies in Kosovo, the legislation in force and the need to amend or supplement this legislation, etc.

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Bustamante, Juan, Leonardo Kuffo, Edgar Izquierdo e 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.

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The emergence and acceptance of social media have become a crucial aspect of daily lives in the worldwide population. As a result of this phenomenon, it is not surprising that customers’ buying patterns exhibit continuous change. For capturing the experience of consumers during their visit to a retail store, previous studies have proposed in-store customer experience (ISCX) scale from data captured through traditional methods like survey research. Accordingly, ISCX is conceived as a subjective internal response to and interaction with the physical retail environment. The present study builds upon prior research and we take the concept of ISCX with the purpose of developing an automated model for capturing ISCX from data collected through a social network like Facebook. This approach offers a low-cost, real-time alternative to traditional elicitation methods. We gathered data from English written contents by Facebook users and collected approximately 1,6 million comments made in public sites belonging to 50 companies worldwide (e.g. Clothing and jewelry retailers, whole Box and electronics Stores), including IKEA, Samsung, Whole Foods, Walmart, Tiffany, Victoria Secret, and Dillards. Five reviewers manually checked the messages filtered by the automated model, resulting in a high accuracy, confirming the high effectiveness of the model in classifying Facebook written messages. Keywords: Customer Experience; Machine Learning; Data Classification; Text Mining.

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Rapporti di organizzazioni sul tema "Data mining – social aspects"

Fairchild, Geoffrey, Rian Mustafa Bahran e Garrett Earl McMath. Working Group on Social Internet Data Mining and Analytics for Nuclear Nonproliferation. Office of Scientific and Technical Information (OSTI), ottobre 2015. http://dx.doi.org/10.2172/1223758.

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Priester, Michael, Malaika Masson e 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, dicembre 2013. http://dx.doi.org/10.18235/0009148.

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How can LAC governments promote the use of clean and green technology in the mining sector and what are the supporting instruments, regulations, infrastructure and institutional aspects that are needed to reinforce this role within public supervisory mining agencies? This technical note explores opportunities for incentivizing cleaner technologies in mining in Latin America and the Caribbean (LAC) region. It focuses on two aspects: key conceptual notions related to clean technologies/process in mining and the practical efforts required by governments to monitor and regulate their use in LAC. It showcases the case of Bolivia, Guyana, and Peru, and identifies specific avenues for the improved capture of economic value from mining, while minimizing negative environmental and social impacts.

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Ripoll, Santiago, Jennifer Cole, Olivia Tulloch, Megan Schmidt-Sane e Tabitha Hrynick. SSHAP: 6 Ways to Incorporate Social Context and Trust in Infodemic Management. Institute of Development Studies (IDS), gennaio 2021. http://dx.doi.org/10.19088/sshap.2021.001.

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Information epidemiology or infodemiology is the study of infodemics - defined by the World Health Organization as an overabundance of information, some accurate and some not, that occurs during a pandemic or other significant event that may impact public health. Infodemic management is the practice of infodemiology and may sit within the risk communication and community engagement (RCCE) pillar of a public health response. However, it is relevant to all aspects of preparedness and response, including the development and evaluation of interventions. Social scientists have much to contribute to infodemic management as, while it must be data and evidence driven, it must also be built on a thorough understanding of affected communities in order to develop participatory approaches, reinforce local capacity and support local solutions.

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Schmidt-Sane, Megan, Tabitha Hrynick, Jennifer Cole, Santiago Ripoll e Olivia Tulloch. SSHAP: 6 Ways to Incorporate Social Context and Trust in Infodemic Management. Institute of Development Studies (IDS), gennaio 2021. http://dx.doi.org/10.19088/sshap.2021.009.

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Oppel, Annalena. Beyond Informal Social Protection – Personal Networks of Economic Support in Namibia. Institute of Development Studies (IDS), novembre 2020. http://dx.doi.org/10.19088/ids.2020.002.

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This paper poses a different lens on informal social protection (ISP). ISP is generally understood as practices of livelihood support among individuals. While studies have explored the social dynamics of such, they rarely do so beyond the conceptual space of informalities and poverty. For instance, they discuss aspects of inclusion, incentives and disincentives, efficiency and adequacy. This provides important insights on whether and to what extent these practices provide livelihood support and for whom. However, doing so in part disregards the socio-political context within which support practices take place. This paper therefore introduces the lens of between-group inequality through the Black Tax narrative. It draws on unique mixed method data of 205 personal support networks of Namibian adults. The results show how understanding these practices beyond the lens of informal social protection can provide important insights on how economic inequality resonates in support relationships, which in turn can play a part in reproducing the inequalities to which they respond.

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Lora, Eduardo. The Distance between Perception and Reality in the Social Domains of Life. Inter-American Development Bank, agosto 2013. http://dx.doi.org/10.18235/0011489.

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The distance between perception and reality with respect to the social domains of life is often striking. Using survey data collected on Latin American countries, this paper provides an overview of the main empirical findings on the gaps between perception and reality in four social domains--health, employment, the perception of security, and social ranking. The overview emphasizes the psychological biases that may explain the gaps. Biases associated with cultural values are very relevant with respect to health and job satisfaction. Cultural differences across countries are pronounced in perceptions of health, while cultural differences across socioeconomic groups are more apparent with respect to job satisfaction. Affect and availability heuristics are the dominant sources of bias in the case of perceptions of security. The formation of subjective social rankings appears to be less culturally dependent but more dependent on the socioeconomic development in the country. The gaps between objective and subjective indicators in the social domains of life are a rich source of data to help understand how perceptions are formed, identify important aspects of people's lives that do not appear in official indicators, inform public debate on social policy, and shed light on public attitudes on key social issues.

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Mayne, Alison, Christina Noble, Paula Duffy, Kirsten Gow, Alexander Glasgow, Kevin O’Neill, Jeni Reid e 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, novembre 2023. http://dx.doi.org/10.57064/2164/22326.

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Social media creates new spaces for connecting people digitally and provides a forum for the exchange of information and discussion. Online spaces such as Facebook groups (FGs) have become part of the fabric of social interaction in many rural areas, with both residents and others living away from the community maintaining a connection in the virtual space. Community FGs are routinely used to share place-based information about resources, events or issues, and to discuss topics of shared interest. In research, these groups allow researchers to connect directly with people who have an interest in what happens within specific communities and offer rich opportunities for participants to likewise engage with research. We can reflect on how FGs in rural communities have the potential to enhance and/or complement existing approaches by making research with dispersed communities more accessible and affordable, while considering challenges around confidentiality and digital inclusion given the characteristics and size of the population. Social media has developed at pace during the last decade, and digital ethics is a shifting methods sub-field that poses challenges to social sciences and humanities researchers. Apart from platforms’ changing terms and conditions, research with and on social media groups has specific ethical challenges (e.g. around anonymity, confidentiality, and data access) that require tailored consideration. In particular, when approaching netnography and similar methods with social media groups, dialogic approaches which aim to engage, respect and protect participants are critical. There is consensus on the need to agree the access conditions with the group administrator as a first step, but there is no guidance on good practice on developing these conditions. To create these guidelines, we have worked collaboratively across disciplines and with administrators of Facebook groups to explore what such process could look like: aspects to address, pros and cons of potential approaches, and potential challenges and solutions.

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DeJaeghere, Joan, Vu Dao, Bich-Hang Duong e Phuong Luong. Inequalities in Learning in Vietnam: Teachers’ Beliefs About and Classroom Practices for Ethnic Minorities. Research on Improving Systems of Education (RISE), febbraio 2021. http://dx.doi.org/10.35489/bsg-rise-wp_2021/061.

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Global and national education agendas are concerned with improving quality and equality of learning outcomes. This paper provides an analysis of the case of Vietnam, which is regarded as having high learning outcomes and less inequality in learning. But national data and international test outcomes may mask the hidden inequalities that exist between minoritized groups and majority (Kinh) students. Drawing on data from qualitative videos and interviews of secondary teachers across 10 provinces, we examine the role of teachers’ beliefs, curricular design and actions in the classroom (Gale et al., 2017). We show that teachers hold different beliefs and engage in curricular design – or the use of hegemonic curriculum and instructional practices that produce different learning outcomes for minoritized students compared to Kinh students. It suggests that policies need to focus on the social-cultural aspects of teaching in addition to the material and technical aspects.

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Wickenden, Mary. Practical Guides for Participatory methods: Disability Inclusive Research. Institute of Development Studies, agosto 2023. http://dx.doi.org/10.19088/ids.2023.045.

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In the past, people with disabilities have been left out of many aspects of life including research. They have not usually been included in ‘mainstream’ studies about key topics such as health, education, WASH, gender empowerment, social and political participation, while other groups in populations are more routinely asked for their views and their qualitative data is collected. It is often perceived to be too difficult or expensive to include disabled people. This is discriminatory and leads to continued lack of understanding about their lives. We need to collect disability inclusive data to understand disabled people’s situations and needs, alongside others’ views. Additionally, disability-specific research has been rare and poorly funded. Now, partly in response to the game-changing UN Convention on the Rights of Persons with Disability (UNCRPD, 2007), the rights of disabled people to participate in all aspects of life are recognised, and research priorities are changing to include disability data and disabled people’s perspectives on many topics.

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