Artykuły w czasopismach na temat „Data mining – social aspects”
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Eltaher, Mohammed, i Jeongkyu Lee. "Social User Mining". International Journal of Multimedia Data Engineering and Management 4, nr 4 (październik 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, i Md Zahidul Islam. "Data Mining and Privacy of Social Network Sites’ Users: Implications of the Data Mining Problem". Science and Engineering Ethics 21, nr 4 (12.06.2014): 941–66. http://dx.doi.org/10.1007/s11948-014-9564-6.
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Wang, Chen-Ya, i Hsia-Ching Chang. "Choice Modeling of Enterprise Social Media Adoptions". International Journal of E-Adoption 11, nr 1 (styczeń 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 i S. Khatoon. "Aspect Βased Classification Model for Social Reviews". Engineering, Technology & Applied Science Research 7, nr 6 (18.12.2017): 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, nr 11 (30.11.2023): 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, i Shah J. Miah. "EXPLORING SENTIMENT ANALYSIS RESEARCH: A SOCIAL MEDIA DATA PERSPECTIVE". International Journal on Soft Computing 14, nr 1 (27.02.2023): 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 i Yi-Hao Hsiao. "A Novel Framework for Mining Social Media Data Based on Text Mining, Topic Modeling, Random Forest, and DANP Methods". Mathematics 9, nr 17 (25.08.2021): 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, i Marenglen Biba. "A Review of Machine Learning and Data Mining Approaches for Business Applications in Social Networks". International Journal of E-Business Research 9, nr 1 (styczeń 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, i CHENGQI ZHANG. "THE EVOLUTION OF KDD: TOWARDS DOMAIN-DRIVEN DATA MINING". International Journal of Pattern Recognition and Artificial Intelligence 21, nr 04 (czerwiec 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, nr 1 (21.03.2016): 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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Lim, Chong-U., i D. Harrell. "Developing Social Identity Models of Players from Game Telemetry Data". Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 10, nr 1 (29.06.2021): 125–31. http://dx.doi.org/10.1609/aiide.v10i1.12723.
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In this paper, we present an approach to modeling aspects of the identities of videogame players by data mining game telemetry information on in-game player performance and customization preferences. Our model demonstrates that such data can be used to reveal aspects of the identities players express by their social networking profile information. We tested our model on players of the multiplayer first-person shooter videogame Team Fortress 2. It was able to significantly explain the variances of the players' number of friends (35.1%), number of uploaded screenshots (49.6%), and number of uploaded videos (39.2%) of their profiles on the gaming social network Steam. Our results revealed several findings, such as criteria indicating how players customized avatars differently according to notions of aesthetics and practicality, and how these notions contributed to predicting their number of friends on their social networking profiles.Responses evaluated from a conducted survey reaffirmed several of these findings.
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Nalepa, Grzegorz J., Szymon Bobek, Krzysztof Kutt i Martin Atzmueller. "Semantic Data Mining in Ubiquitous Sensing: A Survey". Sensors 21, nr 13 (24.06.2021): 4322. http://dx.doi.org/10.3390/s21134322.
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Mining ubiquitous sensing data is important but also challenging, due to many factors, such as heterogeneous large-scale data that is often at various levels of abstraction. This also relates particularly to the important aspects of the explainability and interpretability of the applied models and their results, and thus ultimately to the outcome of the data mining process. With this, in general, the inclusion of domain knowledge leading towards semantic data mining approaches is an emerging and important research direction. This article aims to survey relevant works in these areas, focusing on semantic data mining approaches and methods, but also on selected applications of ubiquitous sensing in some of the most prominent current application areas. Here, we consider in particular: (1) environmental sensing; (2) ubiquitous sensing in industrial applications of artificial intelligence; and (3) social sensing relating to human interactions and the respective individual and collective behaviors. We discuss these in detail and conclude with a summary of this emerging field of research. In addition, we provide an outlook on future directions for semantic data mining in ubiquitous sensing contexts.
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Marchetti, Stefano, Caterina Giusti, Monica Pratesi, Nicola Salvati, Fosca Giannotti, Dino Pedreschi, Salvatore Rinzivillo, Luca Pappalardo i Lorenzo Gabrielli. "Small Area Model-Based Estimators Using Big Data Sources". Journal of Official Statistics 31, nr 2 (1.06.2015): 263–81. http://dx.doi.org/10.1515/jos-2015-0017.
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Abstract The timely, accurate monitoring of social indicators, such as poverty or inequality, on a finegrained spatial and temporal scale is a crucial tool for understanding social phenomena and policymaking, but poses a great challenge to official statistics. This article argues that an interdisciplinary approach, combining the body of statistical research in small area estimation with the body of research in social data mining based on Big Data, can provide novel means to tackle this problem successfully. Big Data derived from the digital crumbs that humans leave behind in their daily activities are in fact providing ever more accurate proxies of social life. Social data mining from these data, coupled with advanced model-based techniques for fine-grained estimates, have the potential to provide a novel microscope through which to view and understand social complexity. This article suggests three ways to use Big Data together with small area estimation techniques, and shows how Big Data has the potential to mirror aspects of well-being and other socioeconomic phenomena.
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Al-Hashedi, Abdullah, Abdullah A. Sallam i Abdulqader M. Mohsen. "Exploring Cancer Risk Factors using Data Mining Techniques: A Case Study from Yemen". Journal of Science and Technology 23, nr 2 (16.12.2018): 1–30. http://dx.doi.org/10.20428/jst.v23i2.1429.
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Data Mining is a hot scientific research topic that has many applications in various life aspects. Healthcare and medicine are among those aspects that attracted data mining researchers who sought to solve decision-making problems. Cancer diagnosis, treatment, and prediction are procedures that have been using data mining for decades. In Yemen, some cancer risk factors seem to be different from those in other parts of the world. By mining the data available at National Cancer Control Foundation (NCCF), useful knowledge have been extracted. In this paper, decision tree classification was selected for building a model to predict cancer risk factors. As the NCCF database contained data describe some social life aspects, environmental circumstances, lifestyle, etc., mining those data can contribute in the endeavors of clearing ambiguity about cancer risk factors in Yemen. The informative attributes that were selected for model building included gender, marital status, number of family members, province, chewing Qat, chewing tobacco (Shamaa), smoking, age, relatives with cancer, and cancer class. These data was prepared for Knowledge Data Discovery process. Then, it was prepared for feeding into C4.5 learning algorithms. The results shown that smoking, chewing tobacco (Shamaa), province of residence, marital status, and age are the most important cancer risk factors. The model produced found of high performance. In addition, the rules extracted from the model tree can also be of high value for both people and healthcare sector.
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Al-Hashedi, Abdullah, Abdullah A. Sallam i Abdulqader M. Mohsen. "Exploring Cancer Risk Factors using Data Mining Techniques: A Case Study from Yemen". Journal of Science and Technology 23, nr 2 (16.12.2018): 1–30. http://dx.doi.org/10.20428/jst.23.2.1.
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Data Mining is a hot scientific research topic that has many applications in various life aspects. Healthcare and medicine are among those aspects that attracted data mining researchers who sought to solve decision-making problems. Cancer diagnosis, treatment, and prediction are procedures that have been using data mining for decades. In Yemen, some cancer risk factors seem to be different from those in other parts of the world. By mining the data available at National Cancer Control Foundation (NCCF), useful knowledge have been extracted. In this paper, decision tree classification was selected for building a model to predict cancer risk factors. As the NCCF database contained data describe some social life aspects, environmental circumstances, lifestyle, etc., mining those data can contribute in the endeavors of clearing ambiguity about cancer risk factors in Yemen. The informative attributes that were selected for model building included gender, marital status, number of family members, province, chewing Qat, chewing tobacco (Shamaa), smoking, age, relatives with cancer, and cancer class. These data was prepared for Knowledge Data Discovery process. Then, it was prepared for feeding into C4.5 learning algorithms. The results shown that smoking, chewing tobacco (Shamaa), province of residence, marital status, and age are the most important cancer risk factors. The model produced found of high performance. In addition, the rules extracted from the model tree can also be of high value for both people and healthcare sector.
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Farlan, Edi, Indra Indra i Ahmad Humam Hamid. "Dampak Pertambangan Emas Tradisional Terhadap Perubahan Sosial Ekonomi Masyarakat Di Gampong Mersak Kecamatan Kluet Tengah Kabupaten Aceh Selatan". Jurnal Ilmiah Mahasiswa Pertanian 1, nr 1 (1.11.2016): 329–36. http://dx.doi.org/10.17969/jimfp.v1i1.1255.
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During the existence of the traditional gold mining in the Mersak Village subdisdtrict of Central Kluet district South Aceh has been a lot changes in local community life. The research methodology used is descriptive method with qualitative approach. Data collection techniques used were interviews, observation, and literature study. Informants in this study consisted of village officials, community leaders and the community of gold miners. Data analysis technique used is the technique of interactive analysis by Milles and Huberman form of data reduction, data presentation and verification / conclusions. The results of the research tells us that the existence of gold mines in the Village Ruin has an impact on the social and economic condition of the community. Negative impact on the gold mining aspects of uncontrolled population movements and worrying, the incidence rate is increasing conflict and transition people's livelihood of farmers to miners who create agricultural infrastructure is not functioning optimally. Also, positive impact on the gold mining aspects of the comprehensive work opportunities for people and rising incomes that can be seen from the high purchasing power. Keywords: Mining, Impact, Social, Economic
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ÇELİK, Sadullah, i Fatma ZEREN. "The Analysis of Graduate Studies on Big-Data on Social Media through Text Mining". Türkiye Araştırmaları Literatür Dergisi 20, nr 39 (7.07.2022): 0. http://dx.doi.org/10.55842/talid.1115782.
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Texts may contain useful information on many topics. Analyzing texts can help people make better decisions, do more effective work, and access more information. Data obtained from rich sources such as social media constitute big data belonging to these texts. Various methods are employed to understand and interpret these data. Among them, text mining and data analytics are the most widely used techniques. In addition, there may be need for more data than available through structured data to excavate the information contained in a given text data. This article examines graduate theses prepared in Turkey employing big data approach obtained from social media. These studies have been prepared by various departments and hence big data has been examined from various aspects. In this regard, thispaper provides brief summaries of some these theses. The findings reveal that the majority of related theses were written in the field of computer engineering. However, their characteristics differ from each other. While some target the software aspects, others analyze social media information. The next most popular field is the various departments in the field of communications. It has been observed that the number of theses written on big data has increased over the years. This study has applied word analysis on theses written between 2008 and 2022 through the text mining method. The results confirm the congruity of the word distribution in theses to the power law distribution. The overall findings point to the problem of excessive focus in theses.
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Zabneva, Elvira, Elena Nagrelli i Julia Kuznetsova. "Ways of Social Tension Easing in the Coal-Mining Region". E3S Web of Conferences 105 (2019): 04050. http://dx.doi.org/10.1051/e3sconf/201910504050.
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The objective: to consider the peculiarities of social tension occurrence in the coal-mining region. Methods: the research is based on the awareness of the fact that social tension is a mass adaptation syndrome that reflects the degree of different population categories physiological, psychophysiological and social-psychological adaptation (or maladaptation) to chronic frustration, hardships (drop in living standards and social changes).Social tension is addressed through the lens of both population categories: the population living on the territory of a coal-mining region but not employed in coal-mining industry as well as population directly employed in coal-mining industry. Results: the factors influencing the degree of social tension of the population living in a coal-mining region and directly employed in coal-mining industry have been released. Conclusion: the ways of social tension in coal-mining region easingbased on integrated research data have been offered. It has been determined that sustainable development of a coal-mining region must be connected with the following interrelated aspects: ecosystem preservation, satisfaction of human needsas well as life quality satisfaction together with efficient resources distribution and others. It has been proved that maximum involvement of thecitizens in social and political life plays the discrete role in the process of social tension easing in a coal-mining region.
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Putri, Rizqy Fadhlina, Shita Tiara i Rini Fadhillah Putri. "PENGARUH PENGUNGKAPAN SUSTAINABILITY REPORTING TERHADAP KINERJA KEUANGAN PERUSAHAAN PERTAMBANGAN". Bisnis-Net Jurnal Ekonomi dan Bisnis 6, nr 1 (14.06.2023): 349–56. http://dx.doi.org/10.46576/bn.v6i1.3279.
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ABSTRACT Analysis of the effect of sustainability report disclosure on financial performance using profitability ratios (ROA). The population in this study are mining companies that submitted sustainability reports using the GRI G4 standard between 2016 and 2020 and are listed on the Indonesia Stock Exchange. The object of this case study is a mining company that is traded on the Indonesian Stock Exchange. Nine organizations became the entire company sample for this study, which was conducted over five years. Multiple linear regression is used as a data analysis method. The research findings show that the economic, environmental, and social aspects of transparency all have a beneficial impact on a company's financial performance.Keywords: Sustainability Report (SR), Disclosure of Economic Aspects, Disclosure of Environmental Aspects, Disclosure of Social Aspects, Return On Assets (ROA)
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Anthony Hodge, R., David V. Lefebure i Ian Thomson. "Social License, Mine Closure, and the Exploration Geologist". SEG Discovery, nr 137 (1.04.2024): 19–32. http://dx.doi.org/10.5382/geo-and-mining-23.
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Editor’s note: The aim of the Geology and Mining series is to introduce early career professionals and students to various aspects of mineral exploration, development, and mining in order to share the experiences and insight of each author on the myriad of topics involved with the mineral industry and the ways in which geoscientists contribute to each. Abstract This paper describes the potential value that can be gained when exploration geologists, early in the project life cycle, contribute to aspects of project development that have historically been outside of their purview. Increasingly strong societal pressures are being directed toward exploration and mining to ensure (1) an enduring social license to operate is in place and (2) closure and postclosure mine liabilities are fully addressed and funded. These actions are consistent with the professional and ethical obligation of caring for human and ecological well-being over both the short and long term. The roles and responsibilities of exploration geologists are evolving as a result. A singular focus on searching for and assessing the nature of ore deposits from a technical perspective has expanded to include (1) contributing to building the foundation for a social license to operate and (2) capturing increasing amounts of data and information relevant to the full mine life cycle, up to and including the closure and postclosure phases of activity. These activities are additional to the traditional geologic role of the exploration geologist and will further enhance the value of an exploration project, despite the rare transition of an exploration target to an operating mine. This expanded role is critical for aligning industry and social values, strengthening trust in the mining industry, and enhancing the appeal of the mining industry as a desirable career option.
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Junaidi, Junaidi. "Pertambangan emas tanpa izin (PETI) dan kesejahteraan keluarga di sekitar wilayah pertambangan". e-Jurnal Ekonomi Sumberdaya dan Lingkungan 11, nr 1 (30.04.2022): 61–74. http://dx.doi.org/10.22437/jels.v11i1.18988.
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This study aims to 1) analyze the impact of illegal gold mining (IGM) on social, economic, and environmental conditions as well as the problems faced by families in the IGM area; 2) Formulate strategies for handling the impact of IGM on family welfare. The research was conducted in Pangkalan Jambu District, Merangin Regency, Jambi Province. The type of data used is primary data sourced from family respondents and informants in the area around mining. Data were collected through questionnaires, in-depth interviews, and Focus Group Discussions (FGD). The results of the analysis found that IGM activities in general have been able to grow new job opportunities and increase people's income. However, from social and environmental aspects, there are various negative impacts of IGM on family welfare. Therefore, in dealing with the impact of IGM on family welfare, three policy strategies are recommended, namely: 1) increasing family capacity; 2) strengthening the values of local wisdom and social capital in society; 3) People's Mining Area and Small-Scale Mining programs.
Keywords: Family welfare, Illegal gold mining, Income shock
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Calderón Larrañaga, Yolanda, CESAR AUGUSTO GARCIA UBAQUE i Jorge Arturo Pineda Jaimes. "A data mining approach to the relationships between landslides and open-pit mining activity: a case study in Soacha (Cundinamarca)". DYNA 88, nr 217 (11.05.2021): 111–19. http://dx.doi.org/10.15446/dyna.v88n217.89558.
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Landslides caused by changes in land use, or by anthropic activities such as open-pit mining, constitute one of the most important socio-economic risk factors in countries with developing economies. This article presents an approach to the relationships between mining activity and the development of landslides in a pilot area located in Soacha, Cundinamarca. Through data mining analysis and the use of Geographic Information Systems (GIS), an evaluation of the possible relationships of these factors was carried out, including socioeconomic aspects. From an inventory of open-pit mining sites, the geomechanical characterization of soil and rock units, and the characterization of environmental and social variables, data were obtained to define variables whose relationships were determined by algorithms programmed in the GIS. The results show that there is an indirect relationship between open-pit mining activity and landslides development over the last four decades in the studied zone.
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Cai, Lanlan, i Xuhong Xu. "The Construction and Trend of Feminist Literature Theory Based on Social Media Data Mining". Mathematical Problems in Engineering 2022 (22.04.2022): 1–9. http://dx.doi.org/10.1155/2022/5791338.
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Up to now, the development path of Chinese women's liberation movement and modern Chinese women's literature is fundamentally different from that of the West. Therefore, the study of Chinese women's literature can not only rely on the essentialism of western feminist literary theory but also must return to the social reality and cultural reality of China. Based on social media data mining, this study uses the Word2vec model to map the text content to a more abstract word vector space, improves the original Text Rank algorithm from three aspects, semantic association between words, word frequency, and word directionality, then carries out feature extraction, and applies this algorithm to the generation of user tags. The feasibility and superiority of the model are verified by comparative experiments on LFR benchmark network. The research in this study provides a reference for the analysis of users' interests and behaviors and has certain theoretical significance and application value.
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CAO, LONGBING, YANCHANG ZHAO, CHENGQI ZHANG i HUAIFENG ZHANG. "ACTIVITY MINING: FROM ACTIVITIES TO ACTIONS". International Journal of Information Technology & Decision Making 07, nr 02 (czerwiec 2008): 259–73. http://dx.doi.org/10.1142/s0219622008002934.
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Activity data accumulated in real life, such as terrorist activities and governmental customer contacts, present special structural and semantic complexities. Activity data may lead to or be associated with significant business impacts, and result in important actions and decision making leading to business advantage. For instance, a series of terrorist activities may trigger a disaster to society, and large amounts of fraudulent activities in social security programs may result in huge government customer debt. Uncovering these activities or activity sequences can greatly evidence and/or enhance corresponding actions in business decisions. However, mining such data challenges the existing KDD research in aspects such as unbalanced data distribution and impact-targeted pattern mining. This paper investigates the characteristics and challenges of activity data, and the methodologies and tasks of activity mining based on case-study experience in the area of social security. Activity mining aims to discover high impact activity patterns in huge volumes of unbalanced activity transactions. Activity patterns identified can be used to prevent disastrous events or improve business decision making and processes. We illustrate the above issues and prospects in mining governmental customer contacts data to recover customer debt.
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Martínez Moncaleano, Carlos Javier, i Ofelia Palencia Fajardo. "Modelo de minería de datos para el análisis de la productividad y crecimiento personal en las mujeres emprendedoras: el caso de la Asociación las Rosas". Suma de Negocios 12, nr 26 (29.01.2021): 23–30. http://dx.doi.org/10.14349/sumneg/2021.v12.n26.a3.
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The research objective was to implement appropriate data mining techniques in the analysis of a social enterprise, considering the question: what are the most appropriate classification models to evaluate the productivity and personal growth of the entrepreneurs of Association las Rosas? Considering social learning related to business growth and personal development. A survey based in systematized information on the characterization of the business and personal development of the sample was taken as a basis; The first model made it possible to establish that productivity was determined by aspects such as the amount of debts that finance the undertakings, as well as the type of financing thereof. The second model established that female entrepreneurs perceive their personal growth and development will take place in the long term.
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Rahim, Syamsuri, Hasriani Safitra i Aditya Halim Perdana Kusuma Putra. "Sustainability Report and Financial Performance: Evidence from Mining Companies in Indonesia". International Journal of Energy Economics and Policy 14, nr 1 (15.01.2023): 673–85. http://dx.doi.org/10.32479/ijeep.14994.
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This study aims to determine the effect of disclosure of sustainability reports (SR) on economic performance aspects, environmental performance aspects, and social performance aspects of a company's financial performance by using the ratios of return on equity (ROE), return on sales (ROS), and return on assets (ROA). To mining companies listed on the Indonesia Stock Exchange (IDX). This study uses quantitative and secondary data. The data collection technique is to record data on the financial reports of companies listed on the Indonesia Stock Exchange for the 2018–2022 period. The research population consists of mining companies listed on the Indonesia Stock Exchange, using a purposive sampling technique with a sample of 50 companies. They use multiple linear regression methods with the help of SPSS software as an analysis method. The analysis results show that the economic aspect of the sustainability report (SR) variable has a significant effect on the company's financial performance using the return on asset (ROA) ratio. The SR variable in the economic aspect significantly affects financial performance using the return on equity (ROE) ratio. The economic aspect of the SR variable influences and is significant in financial performance using the return on sales (ROS) ratio. The environmental aspect of the SR variable has no effect and is not significant on financial performance using the ratio of return on assets (ROA). The environmental aspect The SR variable significantly influences financial performance using the return on equity ratio (ROE). Variable SR environmental aspects have no effect and are not significant on financial performance using the return on sales (ROS) ratio. The social aspect of the SR variable influences and is significant in financial performance using the ratio of return on assets (ROA). The social aspect (SR) variable has no effect and is not significant on financial performance using the return on equity ratio (ROE). The social aspect of the SR variable significantly influences financial performance using the return on sales (ROS) ratio.
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Zema, Yani Maulita i Lina Arliana. "PENERAPAN DATA MINING PENGELOMPOKAN PESERTA BPJS KETENAGAKERJAAN BERDASARKAN PROGRAM YANG DIAMBIL MENGGUNAKAN METODE CLUSTERING". Jurnal Sistem Informasi Kaputama (JSIK) 6, nr 2 (15.07.2022): 152–64. http://dx.doi.org/10.59697/jsik.v6i2.166.
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The implementation of the social security program is one of the responsibilities and obligations of the State, to provide socio-economic protection to the community. Indonesia, like other developing countries, develops social security programs based on funded social security, namely social security that is funded by participants and is still limited to working people in the formal sector. BPJS Ketenagakerjaan continues to improve competence in all aspects of service while developing various programs and benefits that can be directly enjoyed by workers and their families. Non-Wage Recipient Workers (BPU) are employees who carry out economic activities or businesses independently to earn income from their activities or business. The problem that hinders the length of data collection for BPJS Employment participants is the process of determining the social security program that will be taken by Non-Wage Recipient (BPU) workers from the program taken by BPJS Ketenagakerjaan participants. owned is very small and only enough for the daily needs of participants. Data Mining is a data mining process in very large amounts of data using statistical, and mathematical methods, and utilizing the latest Artificial Intelligence technology. Data mining in the process of grouping data can use a grouping method, namely the Clustering method. The system is designed with the MATLAB R2014a programming application, after testing with the system, the results obtained are that in group 1 there are 370 data, group 2 there are 359 data and group 3 there are 271 data with a total of 100 data participants.
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Sucharittham, Nanthawadee, Choochart Haruechaiyasak, Hieu Chi Dam i Thanaruk Theeramunkong. "Multidimensional Sentiment Cube Mining for Process Monitoring". Trends in Sciences 19, nr 9 (9.04.2022): 3682. http://dx.doi.org/10.48048/tis.2022.3682.
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Process monitoring is essential for quality improvement because it is necessary to find the answers to which business issues need to be understood. In the era of social media, many critiques concern the business domain, including life insurance, which is one of the significant business sectors in Thailand. To utilize this useful cloud corpus for the business improvement process, we propose a novel methodology for process monitoring using the concept of multidimensional sentiment cube (MDSC) mining to raise usefulness with the business process model notation (BPMN). As the ability of MDC raise unlimited analysis perspectives merge with sentiment analysis (MDSC), this method can provide more sets of data for association rules mining and meet the needs to be analyzed. The cube analysis scenario, which uses association rules mining results, can reveal a significant hidden issue among aspects and sub-aspects associated under our design with their measurements. The results can be used for monitoring, which presents the customer's sentiment from social media in the real business case and identifying in the real process model.
HIGHLIGHTS
A methodology for process monitoring using the concept of multidimensional sentiment cube (MDSC) mining raise the usefulness of the business process model notation (BPMN) by utilizing the corpus for the business improvement process
This method can provide more sets of data for association rules mining as the ability of MDC raise unlimited analysis perspectives to merge with sentiment analysis (MDSC)
The results can monitor the customer’s sentiment from social media in the real business case and identify in the real process model
GRAPHICAL ABSTRACT
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Mathew, Alexander, Gayatri Moindi, Ketan Bende i Neha Singh. "Credit Card Fraud Prediction System". Journal of Advance Research in Computer Science & Engineering (ISSN: 2456-3552) 2, nr 3 (31.03.2015): 30–35. http://dx.doi.org/10.53555/nncse.v2i3.481.
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Identity crime is common, and pricey, and credit card fraud is a specific case of identity crime. The existing systems of known fraud matching and business rules have restrictions. To remove these negative aspects in real world, this paper proposes a data mining approach: Communal Detection (CD) and Spike Detection (SD). CD finds real social relationships to reduce the suspicion score, and is impervious to fake social relationships. This approach on a fixed set of attributes is whitelist-oriented. SD increases the suspicion score by finding discrepancies in duplicates. These data mining approaches can detect more types of attacks and removes the unnecessary attributes.
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Yang, Z., i Q. Wang. "MINING METHOD OF TRAFFIC IMPACT AREAS OF RAINSTORM EVENT BASED ON SOCIAL MEDIA IN ZHENGZHOU CITY". International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-3/W1-2022 (27.10.2022): 79–86. http://dx.doi.org/10.5194/isprs-archives-xlviii-3-w1-2022-79-2022.
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Abstract. Due to the influence of typhoon "fireworks" on July 20, 2021, there was a rare heavy rainfall in Zhengzhou, Henan Province, China, which caused severe urban waterlogged disasters and casualties. Take it as an example, using Web Crawler technology to obtain Weibo’s (Chinese Twitter) time and space data involved in the rare heavy rainfall in Zhengzhou. Through statistical analysis and spatiotemporal analysis to filter, classify, analyse and manipulate the crawled Weibo’s data, and then study the influence of the extreme rainstorm weather on the traffic areas from two aspects of address points and road networks. At the same time, to verify the effectiveness of the social media-based method for mining the traffic impact areas of the Zhengzhou extreme rainstorm, this experiment compares Weibo data with official data in various aspects according to four categories of waterlogging severity.
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Blus, Pavel I., i Oleg B. Ganin. "Spatial aspects and prerequisites for agglomeration process in old industrial territories of the Western Urals". Ars Administrandi (Искусство управления) 12, nr 4 (2020): 656–77. http://dx.doi.org/10.17072/2218-9173-2020-4-656-677.
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Introduction: the article examines the materials of Gornozavodsk local economic micro-district to identify the processes that shape the classical poly-centered conurbation on this territory. Objectives: analysis of Gornozavodsk conurbation specific features, historical development of this old industrial territory in the Western Urals, as well as the overall social, economic, and infrastructural challenges of future opportunities for the mining and metallurgical area of Prikamye. Methods: comparative and comprehensive analysis of the prerequisites and agglomeration evolution in the old industrial territory of the mining and metallurgical Prikamye, statistic data analysis, analysis of the regulatory documents on the territory spatial development, identification of the strategic potential for Gornozavodsk conurbation. Results: spatial aspect and prerequisites to shape and develop the agglomeration processes in the neighboring municipalities in the mining and metallurgical Prikamye have been identified; strategic priorities for dealing with the traditional challenges in the social and economic development in the old industrial territories have been formulated in terms of their neighboring and associated performance. Conclusions: the current conditions of the uneven spatial development and polarization of the social and economic processes update the need to identify priority “growth points”, to enhance agglomeration aspects of the territorial interaction, to consolidate the efforts by the federal, regional, and municipal authorities and systemic companies in strategic development of the industrial territories in the Western Urals.
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Li, Yang, i Zongli Lv. "Ranking of Urban Brand Influence Based on Social Media Comment Mining". Mathematical Problems in Engineering 2022 (13.07.2022): 1–10. http://dx.doi.org/10.1155/2022/7724020.
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Social media reviews play an increasingly important role in ranking the influence of urban brands. In this study, the public social media review database is mined, and a regression model of urban influence is established. Firstly, this study introduces text mining and data collection. Comment data are collected from static websites and dynamic websites, and ICTCLAS word segmentation tool is used to preprocess the comment data. The algorithm of urban influence level is established, and finally the regression model of urban brand influence is established. A database of 10000 city-related reviews was used in the experiment. The gender thesaurus is established to further improve the accuracy of the experimental results. The feasibility of the model is verified from two aspects: expectation calculation and standardization. This paper analyzes the composition of citizens who comment on social media and the proportion of comment content and puts forward some suggestions to enhance the influence of the city. Finally, it summarizes the popular comments in different months in December and obtains the force points to enhance the influence of the city in different time periods from the structure.
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Hu, Duan, Benxiong Huang, Lai Tu i Shu Chen. "Understanding Social Characteristic from Spatial Proximity in Mobile Social Network". International Journal of Computers Communications & Control 10, nr 4 (1.08.2015): 539. http://dx.doi.org/10.15837/ijccc.2015.4.1991.
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Over the past decades, cities as gathering places of millions of people rapidly evolved in all aspects of population, society, and environments. As one recent trend, location-based social networking applications on mobile devices are becoming increasingly popular. Such mobile devices also become data repositories of massive human activities. Compared with sensing applications in traditional sensor network, Social sensing application in mobile social network, as in which all individuals are regarded as numerous sensors, would result in the fusion of mobile, social and sensor data. In particular, it has been observed that the fusion of these data can be a very powerful tool for series mining purposes. A clear knowledge about the interaction between individual mobility and social networks is essential for improving the existing individual activity model in this paper. We first propose a new measurement called geographic community for clustering spatial proximity in mobile social networks. A novel approach for detecting these geographic communities in mobile social networks has been proposed. Through developing a spatial proximity matrix, an improved symmetric nonnegative matrix factorization method (SNMF) is used to detect geographic communities in mobile social networks. By a real dataset containing thousands of mobile phone users in a provincial capital of China, the correlation between geographic community and common social properties of users have been tested. While exploring shared individual movement patterns, we propose a hybrid approach that utilizes spatial proximity and social proximity of individuals for mining network structure in mobile social networks. Several experimental results have been shown to verify the feasibility of this proposed hybrid approach based on the MIT dataset.
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Li, Xianghai. "Research on the Application of Data Mining Technology in College Students’ Mental Health Education in the Network Age". Security and Communication Networks 2022 (21.03.2022): 1–8. http://dx.doi.org/10.1155/2022/4449066.
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As the marcher of life in the times, the network has penetrated into all fields of life and played an immeasurable role. As a specific contemporary group, college students have a weak sense of self-protection and are very vulnerable to adverse factors. Some colleges and universities are trying to explore various psychological intervention modes in order to conduct psychological counselling and prediction more objectively. Network mental education means that, on the premise of using social networks, educators use psychological science methods to exert a positive impact on all aspects of students' mental health education, so as to promote the development of contemporary mental health of college students under social networks and cultivate the correct mentality of using social networks. It is combined with the established college students' rational correlation analysis system. Based on the collected data and basic information of college students' rational evaluation, the improved mining algorithm is used, and some rules and characteristics of college students' psychological related factors are analyzed from the results, which provides a new idea for college students' rational health education. Aiming at realizing the embedded data mining technology in the psychological management system, this paper expounds in detail the design and implementation of the data mining technology module suitable for the psychological management system and discusses the factors affecting students' mental health.
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Zhang, Shuyue, i Chao Duan. "Clustering Optimization Algorithm for Data Mining Based on Artificial Intelligence Neural Network". Wireless Communications and Mobile Computing 2022 (17.02.2022): 1–16. http://dx.doi.org/10.1155/2022/1304951.
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Social production and life have become increasingly prominent. Cluster analysis is the basis for further processing of the data. The concept of data mining and the application of neural networks in data mining are introduced. According to the related technology of data mining, this article introduces in detail the two-layer perceptron, backpropagation (BP) neural network, RBF radial basis function network for processing classification problems, and self-organizing map (SOM) self-organizing neural network for unsupervised clustering problems. According to the characteristics of self-adaptive and self-organizing capabilities of these algorithms, we learn and design and implement data mining clustering optimization algorithms. In this paper, the neural network-based data mining process consists of three stages: data preparation, rule extraction, and rule evaluation. This paper studies the teaching-type and decomposition-type rule extraction algorithms. After analyzing the BP decomposition-type algorithm, the correlation method is used to calculate the correlation of the input and output neurons. After sorting by the degree of correlation, the RBF neural network is used for node selection. This can greatly reduce the number of input nodes of the neural network, simplify the network structure, reduce the number of recursive splits of the subnet, and improve calculation efficiency. Taking the model as an example, the training error is calculated through data mining technology and clustering algorithm. Data mining clustering optimization algorithm mainly improves the popular neural network from two aspects: finer model design and model pruning, and simulates model complexity, computational complexity, and errors through simulation experiments. The rate is measured, and finally, the simulation experiment is performed. The results show that the proposed algorithm for differential distributed data mining has higher accuracy and stronger convergence ability and overcomes the shortcomings and shortcomings of several original genetic algorithm optimization neural network data mining models; it can effectively improve the searchability and search accuracy of the algorithm and improve the efficiency of data mining. Accuracy and accuracy have a wide range of applications.
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Ou, Zhiyuan, Bingqing Wang, Bin Meng, Changsheng Shi i Dongsheng Zhan. "Research on Resident Behavioral Activities Based on Social Media Data: A Case Study of Four Typical Communities in Beijing". Information 15, nr 7 (5.07.2024): 392. http://dx.doi.org/10.3390/info15070392.
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With the support of big data mining techniques, utilizing social media data containing location information and rich semantic text information can construct large-scale daily activity OD flows for urban populations, providing new data resources and research perspectives for studying urban spatiotemporal structures. This paper employs the ST-DBSCAN algorithm to identify the residential locations of Weibo users in four communities and then uses the BERT model for activity-type classification of Weibo texts. Combined with the TF-IDF method, the results are analyzed from three aspects: temporal features, spatial features, and semantic features. The research findings indicate: ① Spatially, residents’ daily activities are mainly centered around their residential locations, but there are significant differences in the radius and direction of activity among residents of different communities; ② In the temporal dimension, the activity intensities of residents from different communities exhibit uniformity during different time periods on weekdays and weekends; ③ Based on semantic analysis, the differences in activities and venue choices among residents of different communities are deeply influenced by the comprehensive characteristics of the communities. This study explores methods for OD information mining based on social media data, which is of great significance for expanding the mining methods of residents’ spatiotemporal behavior characteristics and enriching research on the configuration of public service facilities based on community residents’ activity spaces and facility demands.
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Li, Yan. "Comprehensive Benefit Evaluation of Cemented Paste Backfill in the Mining Industry". Advances in Civil Engineering 2021 (10.03.2021): 1–9. http://dx.doi.org/10.1155/2021/6646671.
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With the increasing scrutiny on tailings management and requirements of the ground subsidence control, both the academia and industry are highly advocating the utilization of cemented paste backfill (CPB) in the mining industry. Literature review has shown that there are some studies conducted to evaluate the ecological environment, economic development, and social impact of mining. However, a comprehensive study about the economic benefits, resource benefits, environmental benefits, and social benefits of CPB is still lacking. This study aims to present a comprehensive benefit index system for CPB from four aspects, i.e., economy, resource, environment, and society. Questionnaires from experts and the mining industry using CPB technology were collected, and structural equation modeling (SEM) was used for data analysis. The relationships between economy, resource, environment, and society were analyzed. The results show that resource benefits have the greatest impact on the comprehensive benefit of CPB in the mining industry, followed by environmental, economic, and social benefits. According to relevance ranking, resource benefits are positively correlated with social, environmental, and economic benefits, whereas environmental, resource, and social benefits have correlation with economic benefits in turn. Therefore, resource, environmental, economic, and social benefits in order directly affect the comprehensive benefit of CPB on the basis of the influencing degree. Moreover, resource, environmental, and social benefits indirectly affect the comprehensive benefit of CPB. The research findings of this study would help the mining industry popularize the CPB technology and promote the sustainability and cleaner production of the mining industry.
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Tanrısevdi, Abdullah, Gözde Öztürk i Ahmet Cumhur Öztürk. "A supervised data mining approach for predicting comment card ratings". International Journal of Contemporary Hospitality Management 34, nr 5 (15.03.2022): 1823–53. http://dx.doi.org/10.1108/ijchm-05-2021-0675.
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Purpose The purpose of this study is to develop a review rating prediction method based on a supervised text mining approach for unrated customer reviews. Design/methodology/approach Using 2,851 hotel comment card (HCC) reviews, this paper manually labeled positive and negative comments with seven aspects (dining, cleanliness, service, entertainment, price, public, room) that emerged from the content of said reviews. After text preprocessing (tokenization, eliminating punctuation, stemming, etc.), two classifier models were created for predicting the reviews’ sentiments and aspects. Thus, an aggregate rating scale was generated using these two classifier models to determine overall rating values. Findings A new algorithm, Comment Rate (CRate), based on supervised learning, is proposed. The results are compared with another review-rating algorithm called location based social matrix factorization (LBSMF) to check the consistency of the proposed algorithm. It is seen that the proposed algorithm can predict the sentiments better than LBSMF. The performance evaluation is performed on a real data set, and the results indicate that the CRate algorithm truly predicts the overall rating with ratio 80.27%. In addition, the CRate algorithm can generate an overall rating prediction scale for hotel management to automatically analyze customer reviews and understand the sentiment thereof. Research limitations/implications The review data were only collected from a resort hotel during a limited period. Therefore, this paper cannot explore the effect of independent variables on the dependent variable in context of larger period. Practical implications This paper provides a novel overall rating prediction technique allowing hotel management to improve their operations. With this feature, hotel management can evaluate guest feedback through HCCs more effectively and quickly. In this way, the hotel management will be able to identify those service areas that need to be developed faster and more effectively. In addition, this review rating prediction approach can be applied to customer reviews posted via online platforms for detecting review and rating reliability. Originality/value Manually analyzing textual information is time-consuming and can lead to measurement errors. Therefore, the primary contribution of this study is that although comment cards do not have rating values, the proposed CRate algorithm can predict the overall rating and understand the sentiment of the reviews in question.
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Orola, Anni, Anna Härri, Jarkko Levänen, Ville Uusitalo i Stig Irving Olsen. "Assessing WELBY Social Life Cycle Assessment Approach through Cobalt Mining Case Study". Sustainability 14, nr 18 (19.09.2022): 11732. http://dx.doi.org/10.3390/su141811732.
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The interconnected nature of social, environmental, and economic sustainability aspects must be considered in decision-making to achieve strong sustainability. Social life cycle assessment (S-LCA) has been developed to better include social sustainability aspects into life cycle thinking. However, many of the current S-LCA impact assessment approaches have been developed only on a theoretical level, and thus more case studies are needed. We assess the challenges and opportunities of the S-LCA approach through a case study on cobalt mining in the Democratic Republic of the Congo. Data for the case study were collected from scientific literature, reports, newspaper articles, and interview material. The applicability and possible strengths and weaknesses of the WELBY approach for the case were interpreted. The results showed that applying the WELBY approach in practice is possible, even though there is a lack of existing case studies. However, there are several challenges that must be addressed before the approach can be more widely used. The main challenge with the WELBY approach is the overestimation of impacts when adding multiple impact categories, as is recommended in the S-LCA guidelines. More case-specific severity weights should be developed to address this challenge. Moreover, the interpretation of the results from the perspective of informal work should be executed carefully. Even though the WELBY approach is promising, more methodological development is still needed to build a more ethical and reliable S-LCA methodology.
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Sagita, Darwis, Burhanudin Mujtaba i Rahmi Winangsih. "Big Data Analysis Of Banten Community Information Consumption After The Decline of The Covid 19 Case". LONTAR: Jurnal Ilmu Komunikasi 11, nr 2 (30.12.2023): 137–48. http://dx.doi.org/10.30656/lontar.v11i2.2090.
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This research explains the information consumption of Banten people after the decline of the covid 19 case. What information themes are communicated by the community about Banten on social media. This research was conducted with a qualitative approach by discussing the results of data mining searches (data retrieval on social media through applications), while the application used is NodeXL. The following are the results of this study; among the five aspects of information that researchers examined the data (health, education, economy, tourism, and politics), health information was the least communicated (both one-way and two-way) by the community. Meanwhile, politics and tourism are the most communicated aspects of social media. It can be said that the communication behavior of the community about Banten Province on YouTube social media is generally not dominated by two-way or more communication models. Feedback only occurs on certain information themes. The theme of political information and tourism is the most two-way or more. Meanwhile, health information is the least discussed and does not generate much feedback. It can be concluded that health information is no longer a public concern after the decline in covid 19 cases.
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Ciesłik, Tobiasz, i Justyna Górniak-Zimroz. "Analysis of environmental-social changes in the surrounding area of KWB Turow in the historical context". E3S Web of Conferences 29 (2018): 00028. http://dx.doi.org/10.1051/e3sconf/20182900028.
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Opencast mining of large-area lignite deposits impacts the environment, and the health and life of people living in the vicinity of the conducted mining activity. Therefore, the attempt was made to develop a methodology for identification of environmental and social changes in the Bogatynia municipality (south-western Poland), resulting from functioning of Turow lignite mine within its area. During the study of changes occurring over the years, the development of mining pit was noticed, as well as the transformations of this area and impact of the mining plant on the selected elements of environment and surrounding areas. Analogue and digital data were used for the preparation of cartographic compilations, the usefulness of which was analyzed in accordance with the guidelines contained in the standard [1]. The conducted cartographic studies allowed to learn the history of the mine together with identification of changes taking place in the municipality Bogatynia. The obtained results show the form and condition of the objects in the analyzed year, allowing for the interpretation of changes that occurred in the surrounding areas of the Turow mine. Due to the conducted activity of the mine and Turow power plant, both negative and positive aspects were noted in connection with the carrying out of mining activity in the Bogatynia municipality.
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Ambarak, Faisal, Burhan Niode i Shirley Y. V. I. Goni. "Impacts of Gold Mining on the Social, Cultural, and Economic Structures of Kotabunan Village, East Bolaang Mongondow Regency". Journal La Bisecoman 5, nr 1 (15.01.2024): 50–59. http://dx.doi.org/10.37899/journallabisecoman.v5i1.1025.
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One of the natural riches that our country has is gold. In managing wealth in a small way, many problems are faced. The problems that are most in the spotlight are related to the social life of communities around mining areas. This research was conducted to analyze the existence of gold mining companies in the social life of the community in Kotabunan Village, Kotabunan District, East Bolaang Mongondow Regency. The research design used is qualitative. Data was collected through interviews, observation and searching for documents related to the problem object. The research findings show that the existence of a gold mining company in Kotabunan Village, Kotabunan District, East Bolaang Mongondow Regency has brought about changes in people's lives, both positive and negative impacts which can be seen in the aspects of Social Structure, Cultural Structure and Economic Structure. Changes in Cultural Structure can be seen in changes in cultural structure. Changes in economic structure have greatly influenced people's lives, especially with the existence of corporate social responsibility (CSR) from gold mining companies, enabling people through training, basic food assistance, and changing professions as miners or managing businesses.
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Kumar Atmakur, Vijay, i Dr P.Siva Kumar. "A prototype analysis of machine learning methodologies for sentiment analysis of social networks". International Journal of Engineering & Technology 7, nr 2.7 (18.03.2018): 963. http://dx.doi.org/10.14419/ijet.v7i2.7.11436.
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In present day’s social networking technologies are increased because of different user’s communication with each others. There are different types of networks are available in present situations like face book, twitter and LinkedIn. These are the valuable resources for data mining applications because of prevalence presents of different user’s information present in outside environment. Sentiment analysis is the process that defines attitudes, views, emotions and opinions from text, database sources and tweets. Sentiment analysis involves to categorize data based on different opinions like positive and negative or neutral reference classes. In this paper, we analyze different machine learning approaches to define sentiment analysis on social networks. This paper describes comparative analysis of existing machine learning approaches to classify text and other reference classes to evaluate different metric representations. And also this paper describes different machine learning methodologies like Naïve Bayesian, Entropy max and support vector machine (SVM) research on social network data streams. And also discuss major innovations to evaluate different procedures and challenges of analysis of sentiment or opinion mining aspects in present social networks.
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Saçak, Begüm, Aras Bozkurt i Ellen Wagner. "Learning Design versus Instructional Design: A Bibliometric Study through Data Visualization Approaches". Education Sciences 12, nr 11 (27.10.2022): 752. http://dx.doi.org/10.3390/educsci12110752.
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The terms instructional design and learning design have been widely used to describe a discipline concerned with improving the process of teaching and learning. However, though both terms are interchangeably used and share a common vision, both terms are used to encompass different aspects of the learning and teaching. In order to better understand the evolution, map intersections and differences of these terms, and identify emerging themes, using text mining and social network analysis approaches, a triangulated bibliometric study was carried out to analyze a total of 514 publications (326 for instructional design and 157 for learning design) indexed in the Scopus database using text mining and social network analysis. Our first round of analysis revealed four broad themes for instructional design: Theory-driven approaches; technology-informed designs; instructional design for higher education; and assessment and evaluation. A second round of analysis for learning design identified four major themes: Design thinking and user experience-driven approaches; online learning informed designs and online environments; analytical approaches for assessment and evaluation; and engagement-based learning design. The study concludes that while instructional design is about developing, assessing, and evaluating instruction, learning design is more about learner engagement and experience, which can be assessed and enhanced by analytical and technological approaches.
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Kaushal, Chetna, i Deepika Koundal. "Recent trends in big data using hadoop". International Journal of Informatics and Communication Technology (IJ-ICT) 8, nr 1 (1.04.2019): 39. http://dx.doi.org/10.11591/ijict.v8i1.pp39-49.
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<span>Big data refers to huge set of data which is very common these days due to the increase of internet utilities. Data generated from social media is a very common example for the same. This paper depicts the summary on big data and ways in which it has been utilized in all aspects. Data mining is radically a mode of deriving the indispensable knowledge from extensively vast fractions of data which is quite challenging to be interpreted by conventional methods. The paper mainly focuses on the issues related to the clustering techniques in big data. For the classification purpose of the big data, the existing classification algorithms are concisely acknowledged and after that, k-nearest neighbor algorithm is discreetly chosen among them and described along with an example. </span>
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Laulita, Nasar Buntu. "The impact of sustainable supplier selection to supplier performance in mining industry: ethical culture as moderating variable". BISMA (Bisnis dan Manajemen) 14, nr 1 (30.10.2021): 63–73. http://dx.doi.org/10.26740/bisma.v14n1.p63-73.
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Sustainable supplier selection during tender is one of the critical factors in the supply chain management to maintain sustainable procurement in an organisation. Many factors contribute to the success of sustainable supplier selection practices, and ethical culture is one of the factors because it would influence supplier selection. This study aims to determine the effect of implementing sustainable supplier selection on supplier performance by moderating the effect of ethical culture in the mining industry. This type of research is explanatory research with hypothesis testing of 104 respondents by distributing questionnaires. The data analysis is conducted by Structural Equation Model (SEM). The research shows that the construct of sustainable supplier selection with economic, social, and environmental aspects as dimensions has a direct and significant impact on supplier performance in the mining industry. This research also shows that ethical culture has a significant moderating effect in the relationship between sustainable supplier selection and supplier performance. The managerial implication of this research is providing guideline for decision-makers to implement sustainable supplier selection by considering economic, social, and environmental aspects while maintaining an ethical culture as a part of work professionalism to maintain sustainable performance in the mining industry.
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Haryadi, Dwi, Ibrahim Ibrahim i Darwance Darwance. "Environmental Law Awareness as Social Capital Strategic in Unconventional Tin Mining Activities in the Bangka Belitung Islands". Society 10, nr 2 (30.12.2022): 665–80. http://dx.doi.org/10.33019/society.v10i2.455.
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Since ancient times, the Bangka Belitung Islands have been known as one of the world’s largest tin producers. Mining has taken place massively since tin is no longer a strategic commodity, marked by the issuance of several policies that grant permits to anyone to mine tin. Mining, which was originally mostly carried out on land, over time and needed in the economic aspect, has also been carried out at sea. As a result, mining, mostly carried out without permits, impacts environmental damage and other legal and social aspects. In fact, from a regulatory standpoint, the government has issued laws and regulations that serve as references in environmental management, including its relation to the mining sector. This study aims to determine awareness of environmental law in unconventional tin mining activities in the Bangka Belitung Islands. Judging from its type, this research is analytical descriptive research, describing an object through which the data obtained is processed and analyzed to conclude. The research was conducted in all regencies/municipalities in the Bangka Belitung Islands. From the research that has been done, the result is that even though they know, the fact is that most of the mining is carried out without permits, plus there has never been, and there has been no socialization regarding tin mining permits. In addition, most of them also know that their mining activities damage the environment and admit that mining activities damage the habitat of living things. This means that, based on the theories and concepts used, miners are more towards ecocentrism because they make nature an object, not ecocentrism, which pays attention to environmental sustainability.
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Naumova, Vera Viktorovna. "V International Conference «Information Technologies in Earth Sciences and Applications for Geology, Mining And Economy. Ites&Mp-2019»". Russian Digital Libraries Journal 23, nr 6 (8.07.2020): 1279–300. http://dx.doi.org/10.26907/1562-5419-2020-23-6-1279-1300.
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The materials presented at the Conference describe the results of recent years in the following areas: Open access to scientific data and knowledge in Earth Sciences; Data peculiarities in Earth Sciences: new concepts and methods, tools for their collection, integration and processing in different information systems, including systems with intensive use of data; Data mining and mathematical simulation of natural processes in Earth Sciences. Evolution of classical GIS-applications in Earth Sciences; Application to Critical Raw Materials (CRM); social aspects of mining (e.g., the Social Licence to Operate [SLO]); predictive mapping and applications to exploration, landuse and search for extensions of known deposits; Intelligent data analysis, elicitation of facts and knowledge from scientific publications. Thesauruses, ontologies and conceptual modeling. Semantic WEB, linked data. Services. Content semantic structuring. Applications for geosciences, e.g., Ontology-based Dynamic Decision Graphs for Expert systems and decision-aid tools; Application of methods and technologies of the remote sensing in Earth Sciences: from satellites to unmanned aerial vehicles; Information technologies for demonstration and popularization of scientific achievements in Earth Sciences; Applications: environmental risks including mining wastes, natural hazards, water resource management, etc.
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Sbragaglia, Valerio, Ricardo A. Correia, Salvatore Coco i Robert Arlinghaus. "Data mining on YouTube reveals fisher group-specific harvesting patterns and social engagement in recreational anglers and spearfishers". ICES Journal of Marine Science 77, nr 6 (14.06.2019): 2234–44. http://dx.doi.org/10.1093/icesjms/fsz100.
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Abstract We applied data mining on YouTube videos to better understand recreational fisheries targeting common dentex (Dentex dentex), an iconic species of Mediterranean fisheries. In Italy alone, from 2010 to 2016 spearfishers posted 1051 videos compared to 692 videos posted by anglers. The upload pattern of spearfishing videos followed a seasonal pattern with peaks in July, a trend not found for anglers. The average mass of the fish declared in angling videos (6.4 kg) was significantly larger than the one in spearfishing videos (4.5 kg). Videos posted by spearfishers received significantly more likes and comments than those posted by anglers. Content analysis suggested that the differences in engagement can be related to appreciation of successful spearfishers necessitating relevant personal qualities for catching D. dentex. We also found that the mass of the fish positively predicted social engagement as well as the degree of positive evaluation only in spearfishing videos. This could be caused by the generally smaller odds of catching large D. dentex by spearfishing. Our case study demonstrates that data mining on YouTube can be a powerful tool to provide complementary data on controversial and data-poor aspects of recreational fisheries and contribute to understanding the social dimensions of recreational fishers.
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Janubová, Barbora. "Green Extractivism in Lithium Triangle". Slovak Journal of International Relations 21, nr 2 (15.09.2023): 109–34. http://dx.doi.org/10.53465/sjir.1339-2751.2023.2.109-134.
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The aim of this paper is to investigate the relationship between lithium mining and the environmental-social aspects of mining in the countries of the lithium triangle by analysing scientific research works and available statistical data, and applying economic theory to green extractivism. We investigate whether the countries of the lithium triangle meet the criteria of the theory of green extractivism. In the context of the theory of green extractivism, we include Bolivian and Argentine regions as sacrifice zones while Chile is relatively successfully building renewable energy sources. In all three countries, we detected the socio-environmental impacts of lithium mining, the most serious problem being the right to water and the threat to the poorest regions.