Special interest tourism is not so special after all: Big data evidence from the 2017 Great American Solar Eclipse
جهانگردی با علاقه ویژه از همه مهم تر نیست: شواهد داده های بزرگ از خورشید گرفتگی بزرگ آمریکایی 2017-2020
This study puts to empirical test a major typology in the tourism literature, mass versus special interest tourism (SIT), as the once-distinctive boundary between the two has become blurry in modern tourism scholarship. We utilize 41,747 geo-located Instagram photos pertaining to the 2017 Great American Solar Eclipse and Big Data analytics to distinguish tourists based on their choice of observational destinations and spatial movement patterns. Two types of tourists are identified: opportunists and hardcore. The motivational profile of those tourists is validated with the external data through hypothesis testing and compared with and contrasted against existing motivation-based tourist typologies. The main conclusion is that large share of tourists involved in what is traditionally understood as SIT activities exhibit behavior and profile characteristic of mass tourists seeking novelty but conscious about risks and comforts. Practical implications regarding the potential of rural and urban destinations for developing SIT tourism are also discussed.
Keywords: Big data | Instagram photos | Social media | Spatial analysis | Special interest tourism | Astro-tourism
The geography of human activity and land use: A big data approach
جغرافیای فعالیت های انسانی و استفاده از زمین: یک رویکرد داده های بزرگ-2020
The application of location-based social media big data in urban contexts offers new and alternative strategies for understanding city liveliness in developing countries where traditional census data are poor. This paper demonstrates how the spatial-temporal distribution of Chinas Tencent social media usage intensities can be effectively used as a proxy for modelling the geographic patterns of human activity at fine scales. Our results suggest that the spatially-temporally contextualized nature of human activity is dependent upon land use mixing characteristics. With billions of social media data being collected in the virtual world, findings of this study suggest that land use policies to delineating the density, orderly or disorderly geographic patterns of human activity are important for city liveliness.
Keywords: Big data | Human activity | Land use
Remote sensing and social sensing for socioeconomic systems: A comparison study between nighttime lights and location-based social media at the 500m spatial resolution
سنجش از دور و سنجش اجتماعی برای سیستمهای اقتصادی اقتصادی: مطالعه مقایسه ای بین چراغ های شب و رسانه های اجتماعی مبتنی بر مکان در وضوح مکانی 500 متر-2020
With the advent of “social sensing” in the Big Data era, location-based social media (LBSM) data are increasingly used to explore anthropogenic activities and their impacts on the environment. This study converts a typical kind of LBSM data, geo-tagged tweets, into raster images at the 500m spatial resolution and compares them with the new generation nighttime lights (NTL) image products, the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) monthly image composites. The results show that the monthly tweet images are significantly correlated with the VIIRS-DNB images at the pixel level. The tweet images have nearly the same ability on estimating electric power consumption and better performance on assessing personal incomes and population than the NTL images. Tweeted areas (i.e. the pixels with at least one posted tweet) are closer to satellite-derived built-up/urban areas than lit areas in NTL imagery, making tweet images an alternative to delimit extents of human activities. Moreover, the monthly tweet images do not show apparent seasonal changes, and the values of tweet images are more stable across different months than VIIRS-DNB monthly image composites. This study explores the potential of LBSM data at relatively fine spatiotemporal resolutions to estimate or map socioeconomic factors as an alternative to NTL images in the United States
Keywords: Nighttime lights imagery | Geo-tagged tweets | Socioeconomic factors | Social sensing
An extensive study on the evolution of context-aware personalized travel recommender systems
یک مطالعه گسترده در مورد تکامل سیستمهای توصیه گر سفر شخصی آگاه از متن-2020
Ever since the beginning of civilization, travel for various causes exists as an essential part of human life so as travel recommendations, though the early form of recommendations were the accrued experiences shared by the community. Modern recommender systems evolved along with the growth of Information Technology and are contributing to all industry and service segments inclusive of travel and tourism. The journey started with generic recommender engines which gave way to personalized recommender systems and further advanced to contextualized personalization with advent of artificial intelligence. Current era is also witnessing a boom in social media usage and the social media big data is acting as a critical input for various analytics with no exception for recommender systems. This paper details about the study conducted on the evolution of travel recommender systems, their features and current set of limitations. We also discuss on the key algorithms being used for classification and recommendation processes and metrics that can be used to evaluate the performance of the algorithms and thereby the recommenders.
Keywords: Recommender system | Personalization | Context aware | Big data | Travel and tourism
Towards a real-time processing framework based on improved distributed recurrent neural network variants with fastText for social big data analytics
به سمت یک چارچوب پردازش در زمان واقعی بر اساس بهبود انواع شبکه عصبی مکرر توزیع شده با fastText برای تجزیه و تحلیل داده های بزرگ اجتماعی-2020
Big data generated by social media stands for a valuable source of information, which offers an excellent opportunity to mine valuable insights. Particularly, User-generated contents such as reviews, recommendations, and users’ behavior data are useful for supporting several marketing activities of many companies. Knowing what users are saying about the products they bought or the services they used through reviews in social media represents a key factor for making decisions. Sentiment analysis is one of the fundamental tasks in Natural Language Processing. Although deep learning for sentiment analysis has achieved great success and allowed several firms to analyze and extract relevant information from their textual data, but as the volume of data grows, a model that runs in a traditional environment cannot be effective, which implies the importance of efficient distributed deep learning models for social Big Data analytics. Besides, it is known that social media analysis is a complex process, which involves a set of complex tasks. Therefore, it is important to address the challenges and issues of social big data analytics and enhance the performance of deep learning techniques in terms of classification accuracy to obtain better decisions. In this paper, we propose an approach for sentiment analysis, which is devoted to adopting fastText with Recurrent neural network variants to represent textual data efficiently. Then, it employs the new representations to perform the classification task. Its main objective is to enhance the performance of well-known Recurrent Neural Network (RNN) variants in terms of classification accuracy and handle large scale data. In addition, we propose a distributed intelligent system for real-time social big data analytics. It is designed to ingest, store, process, index, and visualize the huge amount of information in real-time. The proposed system adopts distributed machine learning with our proposed method for enhancing decision-making processes. Extensive experiments conducted on two benchmark data sets demonstrate that our proposal for sentiment analysis outperforms well-known distributed recurrent neural network variants (i.e., Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), and Gated Recurrent Unit (GRU)). Specifically, we tested the efficiency of our approach using the three different deep learning models. The results show that our proposed approach is able to enhance the performance of the three models. The current work can provide several benefits for researchers and practitioners who want to collect, handle, analyze and visualize several sources of information in real-time. Also, it can contribute to a better understanding of public opinion and user behaviors using our proposed system with the improved variants of the most powerful distributed deep learning and machine learning algorithms. Furthermore, it is able to increase the classification accuracy of several existing works based on RNN models for sentiment analysis.
Keywords: Big data | FastText | Recurrent neural networks | LSTM | BiLSTM | GRU | Natural language processing | Sentiment analysis | Social big data analytics
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
An analytic infrastructure for harvesting big data to enhance supply chain performance
یک زیرساخت تحلیلی برای برداشت داده های بزرگ به منظور افزایش عملکرد زنجیره تأمین-2020
Big data has already received a tremendous amount of attention from managers in every industry, policy and decision makers in governments, and researchers in many different areas. However, the current big data analytics have conspicuous limitations, especially when dealing with information silos. In this pa- per, we synthesise existing researches on big data analytics and propose an integrated infrastructure for breaking down the information silos, in order to enhance supply chain performance. The analytic infras- tructure effectively leverages rich big data sources (i.e. databases, social media, mobile and sensor data) and quantifies the related information using various big data analytics. The information generated can be used to identify a required competence set (which refers to a collection of skills and knowledge used for specific problem solving) and to provide roadmaps to firms and managers in generating actionable supply chain strategies, facilitating collaboration between departments, and generating fact-based opera- tional decisions. We showcase the usefulness of the analytic infrastructure by conducting a case study in a world-leading company that produces sports equipment. The results indicate that it enabled managers: (a) to integrate information silos in big data analytics to serve as inputs for new product ideas; (b) to capture and interrelate different competence sets to provide an integrated perspective of the firm’s op- erations capabilities; and (c) to generate a visual decision path that facilitated decision making regarding how to expand competence sets to support new product development.
Keywords: Decision support systems | Big data | Analytic infrastructure | Competence set | Deduction graph
Machine + Man: A field experiment on the role of discretion in augmenting AI-based lending models
ماشین + انسان: یک آزمایش میدانی در مورد نقش اختیار در افزایش مدل های وام مبتنی بر هوش مصنوعی-2020
We assess the role of human discretion in lending outcomes using a randomized, controlled experiment. The lenders in our sample utilize a third party, machine-generated credit model as an input in their decision. We design a new feature for the credit-scoring platform – the slider feature – which invites lenders to incorporate additional discretion in their decision by adjusting the machine-based recommendation. We compare the loan outcomes for treatment lenders that randomly get the slider, relative to a control group. The treatment group’s adjustments are predictive of forward looking portfolio characteristics – they show larger declines in future portfolio-level credit risk and larger increases in future sales orders, relative to the control group. The effects of our intervention are more pronounced when borrowers do not have social media accounts and in competitive markets. Our study provides insights about the role of human decisions, given the rapid evolution of machine-based lending models.
Keywords: relationship lending | discretion | machine-learning | fintech | artificial intelligence
Business value of big data analytics: A systems-theoretic approach and empirical test
ارزش تجاری تجزیه و تحلیل داده های بزرگ: یک رویکرد سیستم-تئوری و آزمون تجربی-2020
Although big data analytics have been widely considered a key driver of marketing and innovation processes, whether and how big data analytics create business value has not been fully understood and empirically validated at a large scale. Taking social media analytics as an example, this paper is among the first attempts to theoretically explain and empirically test the market performance impact of big data analytics. Drawing on the systems theory, we explain how and why social media analytics create super-additive value through the synergies in functional complementarity between social media diversity for gathering big data from diverse social media channels and big data analytics for analyzing the gathered big data. Furthermore, we deepen our theorizing by considering the difference between small and medium enterprises (SMEs) and large firms in the required integration effort that enables the synergies of social media diversity and big data analytics. In line with this theorizing, we empirically test the synergistic effect of social media diversity and big data analytics by using a recent large-scale survey data set from 18,816 firms in Italy. We find that social media diversity and big data analytics have a positive interaction effect on market performance, which is more salient for SMEs than for large firms.
Keywords: Big data analytics | Social media analytics | Synergies | Business value of information technology | Market performance | Digital innovation
Can twitter analytics predict election outcome? An insight from 2017 Punjab assembly elections
آیا تحلیل های توییتر می توانند نتیجه انتخابات را پیش بینی کنند؟ بینشی از انتخابات مجلس پنجم 2017-2020
Since the beginning of this decade, there has seen an exponential growth in number of internet users using social media, especially Twitter for sharing their views on various topics of common interest like sports, products, politics etc. Due to the active participation of large number of people on Twitter, huge amount of data (i.e. big data) is being generated, which can be put to use (after refining) to analyze real world problems. This paper takes into consideration the Twitter data related to the 2017 Punjab (a state of India) assembly elections and applies different social media analytic techniques on collected tweets to extract and unearth hidden but useful information. In addition to this, we have employed machine learning algorithm to perform polarity analysis and have proposed a new seat forecasting method to accurately predict the number of seats that a political party is likely to win in the elections. Our results confirmed that Indian National Congress was likely to emerge winner and that in fact was the outcome, when results got declared.
Keywords: Analytics | Election prediction | Social media | Natural language processing | Machine learning | Sentiment analysis | Twitter