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Eco-friendliness and fashion perceptual attributes of fashion brands: An analysis of consumers’ perceptions based on twitter data mining
سازگاری با محیط زیست و ویژگی های ادراکی مد برندهای مد: تحلیلی از درک مصرف کنندگان براساس داده کاوی توییتر-2020 This study explores if there is a convergence between the concepts of fashion and eco-friendliness in
consumer perception of a fashion brand.We assume that increased eco-friendly perception will influence
the brand image positively, with this impact being much higher for luxury than for high and fast fashion
brands. The hypotheses are tested using data collected from Twitter. We analyzed the fashion clothing
brands with the highest number of followers on the Socialbakers list and applied a novel social network
mining methodology that allows measuring the relationship between each brand and two perceptual
attributes (fashion and eco-friendliness). The method is based on attribute exemplarsdthat is, Twitter
accounts that represent a perceptual attribute. Our exemplars catalyze social media conversations on
fashion (identified in our research by the keywords “fashion,” “glamour,” and “style”) and ecofriendliness
(keywords “environment” and “ethical business”). Based on social network analysis theory,
we computed a similarity function between the followers of the exemplars and those of the brand.
The results suggest that there is a correlation between the fashion and the eco-friendliness perceptual
attributes of a brand; however, this correlation is far stronger for luxury brands than for high and fast
fashion brands. The difference in the correlations confirms the recent tendency of fashion luxury brand
to increasingly consider treating environmental issues as part of their core business and not just as added
value to the brand’s offer. Keywords: Fashion brands | Twitter | Consumer perception | Environment | Ethical business | Brand image | Big data |
مقاله انگلیسی |
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Identifying and monitoring the development trends of emerging technologies using patent analysis and Twitter data mining_ The case of perovskite solar cell technology
شناسایی و نظارت بر روند توسعه فن آوری های نوظهور با استفاده از آنالیز ثبت اختراع و داده کاوی توییتر: مورد فناوری سلول خورشیدی پروسکایت-2019 Monitoring the emergence of emerging technologies helps managers and decision makers to identify development
trends in emerging technologies is crucial for government research and development (R&D), strategic
planning, social investment, and enterprise practices. Researchers usually use academic papers and patent data
to identify and monitoring the trends of emerging technologies from a technological perspective, but they rarely
make use of social media data (e.g., such as Twitter data) related to emerging technologies. Analysis of this social
media data is of great significance to understand the emergence of emerging technologies and gain insight into
development trends. Therefore, this paper proposes a framework that uses patent analysis and Twitter data
mining to monitoring the emergence of emerging technologies and identify changing trends of these emerging
technologies. The perovskite solar cell technology is selected as a case study. In this case, we used patent analysis
to monitoring the evolutionary path of perovskite solar cell technology. We applied Twitter data mining to
analyze Twitter users sense of, response to, and expectations for this perovskite solar cell technology. We also
identified the professional types of Twitter users and examined changes in their topics of interest over time to
track the emergence of perovskite solar cell technology. We analyzed a comparison of the results of patent
analysis and Twitter data mining to identify development trends of perovskite solar cell technology. This paper
contributes to our understanding of how technologies emerge and develop, as well as the technology forecasting
and foresight methodology, and will be of interest to solar photovoltaic technology R&D experts. Keywords: Emerging technologies | Technology trends | Patent analysis | Twitter data mining | Technologies emerge | Perovskite solar cell technology |
مقاله انگلیسی |
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Development of a national-scale real-time Twitter data mining pipeline for social geodata on the potential impacts of flooding on communities
توسعه یک خط لوله داده کاوی داده های توییتر در زمان واقعی در مقیاس ملی برای ژئو داده های اجتماعی در مورد اثرات احتمالی سیل بر جوامع-2019 Social media, particularly Twitter, is increasingly used to improve resilience during extreme weather events/
emergency management situations, including floods: by communicating potential risks and their impacts, and
informing agencies and responders. In this paper, we developed a prototype national-scale Twitter data mining
pipeline for improved stakeholder situational awareness during flooding events across Great Britain, by retrieving
relevant social geodata, grounded in environmental data sources (flood warnings and river levels). With
potential users we identified and addressed three research questions to develop this application, whose components
constitute a modular architecture for real-time dashboards. First, polling national flood warning and
river level Web data sources to obtain at-risk locations. Secondly, real-time retrieval of geotagged tweets,
proximate to at-risk areas. Thirdly, filtering flood-relevant tweets with natural language processing and machine
learning libraries, using word embeddings of tweets. We demonstrated the national-scale social geodata pipeline
using over 420,000 georeferenced tweets obtained between 20 and 29th June 2016. Keywords: Flood management | Twitter | Volunteered geographic information | Natural language processing | Word embeddings | Social geodata |
مقاله انگلیسی |