با سلام خدمت کاربران عزیز، به اطلاع می رساند ترجمه مقالاتی که سال انتشار آن ها زیر 2008 می باشد رایگان بوده و میتوانید با وارد شدن در صفحه جزییات مقاله به رایگان ترجمه را دانلود نمایید.
Investigating consumer preferences on product designs by analyzing opinions from social networks using evidential reasoning
بررسی ترجیحات مصرف کننده در طرح های محصول با تجزیه و تحلیل نظرات شبکه های اجتماعی با استفاده از استدلال مشهود-2020
The rapid growth of e-commerce and social networking sites has created various challenges for the extraction of user-generated content (UGC). In the era of big data, customer opinions from social media are utilized for investigating consumer preferences to support product redesigns. Opinion mining, including the various automatic text classification algorithms using sentiment analysis is a capable tool to deal with a large amount of comments on the social networking sites. In which, sentiment analysis is used to determine the contextual polarity within a comment by searching sentimental words. However, the inconsistency on choosing the sentiment words leads to the inaccurate interpretation of the opinion strength of sentiment words. An approach to summarize the UGC from social networking media using fuzzy and ER without the need to review all the comments is proposed in this paper. The inaccuracy on determination of the polarity of sentiment words and corresponding opinion strengths is rectified by fuzzy approximation and ER. The result is presented in ranking therefore the effort for result interpretation significantly reduced. The incorporation of sentiment analysis with ER to analyze the UGC for product designs is a new attempt in investigating consumer preferences. The proposed approach is shown to be handy, sufficient, and cost effective for the product design and re-design, particularly in the preliminary stage. This project can be further extended by employing alternative fuzzy approximate techniques in the fuzzy-ER approach to support the sentiment analysis to enhance the accuracy of sentiment values for determining the distribution assessments of ER.
Keywords: Opinion mining | Sentiment analysis | Evidential reasoning | Consumer preferences | Product design
Digital social capital and performance of initial coin offerings
سرمایه اجتماعی دیجیتال و عملکرد ارائه سکه های اولیه-2020
The Initial Coin Offering (ICO) has emerged as an original way for companies to leverage funding. In this study, we analyze 537 companies that chose the ICO model in 2017 and investigate how their digital social capital is related to the rank of the ICO based on the market capitalization. The goal of this work is to better understand the role of digital social capital in ICO success. Multiple facets of digital presence are analyzed, such as website, ICO activity on social networks like Twitter, the community built and the activeness of the community. We apply an exploratory factor analysis to leverage the main factors that can be used as latent variables, and build an original research model. Structural equation modelling is used for model evaluation and hypothesis testing. Curvilinear analyses allow us to obtain a finer vision of our results. We also verify the robustness of our results in predicting the ICO rank further in time. Our results suggest that social capital is indicative of the ICO performance. The website audience is found to be the most predictive. However, the audience and the centrality of an ICO in the community seems less important than the activeness of an ICO and of the related community on social networks.
Keywords: Initial coin offering | Cryptocurrency | Digital presence | Public interest | Social networks | Website | Crowdfunding | Ewom | Marketing | Visibility
Collaborative relationship discovery in BIM project delivery: A social network analysis approach
کشف رابطه همکاری در تحویل پروژه BIM: یک رویکرد تحلیل شبکه های اجتماعی-2020
A deeper understanding of collaboration among various stakeholders is imperative towards the success of the Building Information Modelling (BIM) enabled project delivery. A systematic research framework is proposed to properly analyze dynamic changes in stakeholders and their relationships by deploying social network analysis (SNA), assisting in stakeholder management. First, the work breakdown structure (WBS) is performed to organize a BIM project (i.e., rail project) into 4 levels: project name, stage, process, and activity. Second, different stakeholders are clarified based on the activity level over the whole project using interviews and surveys. Third, a project-level social network containing both the project ontology and stakeholders is established, from which cooperation information is then precisely extracted and stakeholder-level social networks with only stakeholders being nodes are created. Finally, SNA is carried out at both network- and node- levels. The results show that: (i) It is feasible to mine the collaboration information between different stakeholders through a project-level social network, (ii) The most active and central actor in each stage is continually changing, and the degree centrality and betweenness centrality are polarized between the stakeholders, (iii) There is a trend for a central stakeholder to be active and a broker at the same time, such as architect (#3), BIM coordinator (#4), BIM modeler (#6), civil & structural engineer (#9), and mechanical & electrical engineer (#18), and (iv) BIM coordinator (#4) should be given more attention, especially from the tender stage on. The research can contribute to both theoretical and practical development: (a) A new novel approach that could make the dynamic collaboration characteristics explicit was proposed with SNA, (b) A good knowledge of the dynamic collaboration attributes among different stakeholders in BIM-based rail projects could encourage a better project outcome
Keywords: BIM | Social network analysis | Stakeholder management | Rail projects | Relationship discovery
Managing minority opinions in micro-grid planning by a social network analysis-based large scale group decision making method with hesitant fuzzy linguistic information
مدیریت نظرات اقلیت ها در برنامه ریزی خرد شبکه ای با استفاده از روش تصمیم گیری گروهی مقیاس بزرگ مبتنی بر تحلیل شبکه های اجتماعی با اطلاعات زبانی فازی مردد-2020
The growth of global electricity demand has put forward higher requirements for power distribution networks. The high cost of the large-scale power system and the voice for the use of renewable energy impel the birth of the micro-grid which plays a complementary role in the power generation of large-scale power system. The construction of micro-grid planning is complex and many stakeholders’ opinions should be considered for a comprehensive evaluation. Furthermore, the development of social big data techniques, such as e-marketplace and e-democracy, makes experts have social relationships among them. This study aims to develop a consensus model to manage minority opinions for largescale group decision making with social network analysis for micro-grid planning. To deal with the vague and uncertain features in complex micro-grid planning problems, experts are supposed to use hesitant fuzzy linguistic term sets to express their opinions. A social network analysis-based clustering method is introduced to classify experts. Besides, in a large-scale group decision making problem, the opinions of experts should be fully considered, especially the minority opinions. This model considers the minority opinions in a micro-grid planning problem and provides an approach to manage these opinions. Finally, we use an illustrative example concerning the micro-grid planning decision making in Ali district in Tibet to demonstrate the effectiveness and practicability of the proposed model.
Keywords: Micro-grid planning | Large-scale group decision making | Social network analysis | Minority opinions | Hesitant fuzzy linguistic term sets | Consensus
A social-semantic recommender system for advertisements
یک سیستم پیشنهادی اجتماعی معنایی برای تبلیغات-2020
Social applications foster the involvement of end users in Web content creation, as a result of which a new source of vast amounts of data about users and their likes and dislikes has become available. Having access to users’ contributions to social sites and gaining insights into the consumers’ needs is of the utmost importance for marketing decision making in general, and to advertisement recommendation in particular. By analyzing this information, advertisement recommendation systems can attain a better understanding of the users’ interests and preferences, thus allowing these solutions to provide more precise ad suggestions. However, in addition to the already complex challenges that hamper the performance of recommender systems (i.e., data sparsity, cold-start, diversity, accuracy and scalability), new issues that should be considered have also emerged from the need to deal with heterogeneous data gathered from disparate sources. The technologies surrounding Linked Data and the Semantic Web have proved effective for knowledge management and data integration. In this work, an ontology-based advertisement recommendation system that leverages the data produced by users in social networking sites is proposed, and this approach is substantiated by a shared ontology model with which to represent both users’ profiles and the content of advertisements. Both users and advertisement are represented by means of vectors generated using natural language processing techniques, which collect ontological entities from textual content. The ad recommender framework has been extensively validated in a simulated environment, obtaining an aggregated f-measure of 79.2% and a Mean Average Precision at 3 (MAP@3) of 85.6%.
Keywords:Knowledge-based systems | Recommender systems | Natural language processing | Advertising | Social network services
Towards a distributed local-search approach for partitioning large-scale social networks
به سمت یک جستجوی محلی توزیع شده برای پارتیشن بندی شبکه های اجتماعی در مقیاس بزرگ-2020
Large-scale social graph data poses significant challenges for social analytic tools to mon- itor and analyze social networks. A feasible solution is to parallelize the computation and leverage distributed graph computing frameworks to process such big data. However, it is nontrivial to partition social graphs into multiple parts so that they can be computed on distributed platforms. In this paper, we propose a distributed local search algorithm, named dLS, which enables quality and efficient partition of large-scale social graphs. With the vertex-centric computing model, dLS can achieve massive parallelism. We employ a distributed graph coloring strategy to differentiate neighbor nodes and avoid interference during the parallel execution of each vertex. We convert the original graph into a small graph, Quotient Network , and obtain local search solution from processing the Quotient Net- work , thus further improving the partition quality and efficiency of dLS. We have evaluated the performance of dLS experimentally using real-life and synthetic social graphs, and the results show that dLS outperforms two state-of-the-art algorithms in terms of partition quality and efficiency.
Keywords: Graph partitioning | Social network | Local search algorithm
Privacy Preservation for Social Networks Sequential Publishing
حفظ حریم خصوصی برای انتشارات متوالی شبکه های اجتماعی-2020
The proliferation of social networks allowed creating a big quantity of data about users and their relationships. Such data contain much private information. Therefore, anonymization is required before publishing the data for data mining purposes (scientific research, marketing, decision support, etc). Most of the anonymization works in social networks focus on publishing one instance while not considering the need for anonymizing sequential releases. However, many cases show that sequential releases may infer private information even though individual instances are anonymized. This paper studies the privacy issues of sequential releases and proposes a privacy preserving solution for this case. The proposed solution ensures three privacy requirements (users’ privacy, groups’ privacy and edges’ privacy), and it considers the case where many users and groups may share the same profiles. Some experiments over some complex queries show that the utility of the released data is better preserved than other solutions, with regard to the privacy of users, groups and edges.
Keywords: Privacy preserving | Social networks | Anonymization | Sequential releases
Tax evasion on a social network
فرار مالیاتی در یک شبکه اجتماعی-2020
We relate tax evasion behavior to a substantial literature on social comparison in judge- ments. Taxpayers engage in tax evasion as a means to boost their expected consumption relative to others in their social network. The unique Nash equilibrium of the model re- lates optimal evasion to a (Bonacich) measure of network centrality: more central taxpay- ers evade more. Given that tax authorities are now investing heavily in big-data tools that aim to construct social networks, we investigate the value of acquiring network informa- tion. We do this using networks that allow for celebrity taxpayers, whose consumption is seen widely, and who are systematically of higher wealth. We show that there are pro- nounced returns to the initial acquisition of network information, especially in the pres- ence of celebrity taxpayers.
Keywords: Tax evasion | Social networks | Network centrality | Optimal auditing | Social comparison | Relative consumption
Tweeting the United Nations Climate Change Conference in Paris (COP21): An analysis of a social network and factors determining the network influence
توییت کنفرانس تغییرات اقلیمی سازمان ملل متحد در پاریس (COP21): تحلیلی از یک شبکه اجتماعی و عوامل تعیین کننده تأثیر شبکه-2020
To understand the Twitter network of an environmental and political event and to extend the network theory of social capital, we first performed a network analysis of the English tweets during the first 10 days of the United Nations’ Conference of the Parties in Paris in 2015. Accounts for nonprofit and government agencies were more likely to be influential in the Twitter network and be retweeted, whereas individual accounts were more likely to retweet others. Based on a quota sample of 133 Twitter accounts and using both manual and machine coding, we further found that the number of followers (but not the size of following) and the common-goal frame (i.e., mitigation/adaptation) positively predicted an account’s influence in the Twitter network, whereas the conflict frame negatively predicted an account’s network influence
Keywords: Big data | Climate change | COP21 | Framing | Social capital | Social network analysis
Do Chinese hospital services constitute an oligopoly? Evidence of the rich-club phenomenon in a patient referral network
آیا خدمات بیمارستان چینی یک الیگپولی است؟ شواهدی از پدیده باشگاه ثروتمند در یک شبکه مراجعه کننده به بیمار-2020
Research on medical practice that uses big data has attracted considerable attention recently. In this paper, we focused on a large set of patient referral data gathered in Fujian province, China, between 2009 and 2011. We built a directed weighted patient referral network. By using four metrics from network science, namely, the power-law distribution, global rich-club coefficient, local richclub coefficient, and assortativity coefficient, we identified a significant rich-club phenomenon in this network. In addition, the community detection was also carried out to find the relationship between rich members and non-rich members. The findings indicate an oligopoly in which Class-III hospitals occupy an overwhelmingly dominant position over the competition. Also, the characteristic ‘significant regional clustering’ was inferred from the results.
Keywords: Healthcare system | Social network analysis | Hospital | China