The effect of toxicity on COVID-19 news network formation in political subcommunities on Reddit: An affiliation network approach     

《International Journal of Information Management》2021

Political polarization remains perhaps the “greatest barrier” to effective COVID-19 pandemic mitigation measures in the United States. Social media has been implicated in fueling this polarization. In this paper, we uncover the network of COVID-19 related news sources shared to 30 politically biased and 2 neutral subcommunities on Reddit. We find, using exponential random graph modeling, that news sources associated with highly toxic – “rude, disrespectful” – content are more likely to be shared across political subreddits. We also find homophily according to toxicity levels in the network of online news sources. Our findings suggest that news sources associated with high toxicity are rewarded with prominent positions in the resultant network. The toxicity in COVID-19 discussions may fuel political polarization by denigrating ideological opponents and politicizing responses to the COVID-19 pandemic, all to the detriment of mitigation measures. Public health practitioners should monitor toxicity in public online discussions to familiarize themselves with emerging political arguments that threaten adherence to public health crises management. We also recommend, based on our findings, that social media platforms algorithmically promote neutral and scientific news sources to reduce toxic discussion in subcommunities and encourage compliance with public health recommendations in the fight against COVID-19.  相似文献   

RGAN-EL: A GAN and ensemble learning-based hybrid approach for imbalanced data classification     

《Information processing & management》2023,60(2):103235

Imbalanced sample distribution is usually the main reason for the performance degradation of machine learning algorithms. Based on this, this study proposes a hybrid framework (RGAN-EL) combining generative adversarial networks and ensemble learning method to improve the classification performance of imbalanced data. Firstly, we propose a training sample selection strategy based on roulette wheel selection method to make GAN pay more attention to the class overlapping area when fitting the sample distribution. Secondly, we design two kinds of generator training loss, and propose a noise sample filtering method to improve the quality of generated samples. Then, minority class samples are oversampled using the improved RGAN to obtain a balanced training sample set. Finally, combined with the ensemble learning strategy, the final training and prediction are carried out. We conducted experiments on 41 real imbalanced data sets using two evaluation indexes: F1-score and AUC. Specifically, we compare RGAN-EL with six typical ensemble learning; RGAN is compared with three typical GAN models. The experimental results show that RGAN-EL is significantly better than the other six ensemble learning methods, and RGAN is greatly improved compared with three classical GAN models.  相似文献   

Creating value from Social Big Data: Implications for Smart Tourism Destinations     

Pasquale Del Vecchio  Gioconda Mele  Valentina Ndou  Giustina Secundo 《Information processing & management》2018,54(5):847-860

This paper aims to demonstrate how the huge amount of Social Big Data available from tourists can nurture the value creation process for a Smart Tourism Destination. Applying a multiple-case study analysis, the paper explores a set of regional tourist experiences related to a Southern European region and destination, to derive patterns and opportunities of value creation generated by Big Data in tourism. Findings present and discuss evidence in terms of improving decision-making, creating marketing strategies with more personalized offerings, transparency and trust in dialogue with customers and stakeholders, and emergence of new business models. Finally, implications are presented for researchers and practitioners interested in the managerial exploitation of Big Data in the context of information-intensive industries and mainly in Tourism.  相似文献   

Discrete Choice Modeling in Education: An Innovative Method to Assess Teaching Practices     

Vasilis Grammatikopoulos  Athanasios Gregoriadis  Michalis Linardakis 《Educational Measurement》2019,38(3):46-54

Self‐reported measures are an easy, time‐efficient, and low‐cost way to collect research data compared to other assessment methods. They are, however, characterized by several limitations regarding the quality and the clarity of the data they produce, especially when they are implemented in education. The main purpose of the current study was to use a method called discrete choice modeling (DCM) in education, in order to develop a self‐reported instrument that will reduce the bias for socially desirable responses and to assess teachers’ practices in physical activity. DCM method has the ability to overcome the respondents’ tendency to answer in a socially desirable way. A nationally representative sample of 531 Greek early educators participated in this study and were administered a self‐assessment questionnaire based on DCM. Results showed that the DCM based newly developed instrument manages to assess effectively educators’ practices and provided evidence of the applicability of the method in education. Further implications and future recommendations regarding the effective application of DCM in education are discussed.  相似文献   

Remaining in Control with an Illusion of Interactivity: The Paternalistic Side of Data Journalism*     

Ester Appelgren 《Journalism Practice》2019,13(8):956-960

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MARC to BIBFRAME: Converting the PCC to Linked Data     

Jacquie Samples  Ian Bigelow 《Cataloging & classification quarterly》2020,58(3-4):403-417

Abstract

The Program for Cooperative Cataloging (PCC) has formal relationships with the Library of Congress (LC), Share-VDE, and Linked Data for Production Phase 2 (LD4P2) for work on Bibliographic Framework (BIBFRAME), and PCC institutions have been very active in the exploration of MARC to BIBFRAME conversion processes. This article will review the involvement of PCC in the development of BIBFRAME and examine the work of LC, Share-VDE, and LD4P2 on MARC to BIBFRAME conversion. It will conclude with a discussion of areas for further exploration by the PCC leading up to the creation of PCC conversion specifications and PCC BIBFRAME data.  相似文献   

Using an interactive platform to recognize the intersection of social and spatial inequalities     

Lisa Poling  Travis Weiland 《Teaching Statistics》2020,42(3):108-116

With the creation of interactive tasks that allow students to explore spatial ways of knowing in conjunction with their other ways of knowing the world, we create a space where students can make sense of information as they organize these new ideas into their already existing schema. Through the use of a Common Online Data Analysis Platform (CODAP) and data from Public Use Microdata Areas (PUMA), students can explore the communities in which they live and work, critically examining opportunities and challenges within a defined space.  相似文献   

Cross-company customer churn prediction in telecommunication: A comparison of data transformation methods     

《International Journal of Information Management》2019

Cross-Company Churn Prediction (CCCP) is a domain of research where one company (target) is lacking enough data and can use data from another company (source) to predict customer churn successfully. To support CCCP, the cross-company data is usually transformed to a set of similar normal distribution of target company data prior to building a CCCP model. However, it is still unclear which data transformation method is most effective in CCCP. Also, the impact of data transformation methods on CCCP model performance using different classifiers have not been comprehensively explored in the telecommunication sector. In this study, we devised a model for CCCP using data transformation methods (i.e., log, z-score, rank and box-cox) and presented not only an extensive comparison to validate the impact of these transformation methods in CCCP, but also evaluated the performance of underlying baseline classifiers (i.e., Naive Bayes (NB), K-Nearest Neighbour (KNN), Gradient Boosted Tree (GBT), Single Rule Induction (SRI) and Deep learner Neural net (DP)) for customer churn prediction in telecommunication sector using the above mentioned data transformation methods. We performed experiments on publicly available datasets related to the telecommunication sector. The results demonstrated that most of the data transformation methods (e.g., log, rank, and box-cox) improve the performance of CCCP significantly. However, the Z-Score data transformation method could not achieve better results as compared to the rest of the data transformation methods in this study. Moreover, it is also investigated that the CCCP model based on NB outperform on transformed data and DP, KNN and GBT performed on the average, while SRI classifier did not show significant results in term of the commonly used evaluation measures (i.e., probability of detection, probability of false alarm, area under the curve and g-mean).  相似文献   

唐诗知识图谱的构建及其智能知识服务设计   总被引：2，自引：0，他引：2  

周莉娜  洪亮  高子阳 《图书情报工作》2019,63(2):24-33

[目的/意义]立足于当前大数据环境下的唐诗知识服务需求，以大规模唐诗数据为基础构建唐诗知识图谱并提供智能知识服务，推动人工智能环境下唐诗知识管理和知识服务方式的创新。[方法/过程]本文在对领域知识服务需求调研的基础上，设计领域知识服务驱动的唐诗本体模型，然后利用从Web上爬取的多源异构数据，采用知识抽取、知识融合、知识推理等技术自动构建唐诗知识图谱，统一表示和组织唐诗领域数据，实现对大规模唐诗数据的语义化处理。[结果/结论]本文设计基于唐诗知识图谱的智能知识服务平台KnowPoetry，提供唐诗领域的知识探索、时空轨迹、语义查询等智能化知识服务，推动人工智能环境下唐诗数字人文研究方法的创新转型。  相似文献   

Ongoing child welfare services: Understanding the relationship of worker and organizational characteristics to service provision     

《Child abuse & neglect》2018

Ongoing child welfare services are put in place after completion of the initial maltreatment investigation when there is a perceived need to mitigate the risk of future harm. The knowledge of how clinical, worker, and organizational characteristics interact with this decision to provide ongoing child welfare services is not well integrated in the research literature. Using secondary data from the Canadian Incidence Study of Reported Child Abuse and Neglect-2008, this study’s primary objective is to understand the relationship of clinical, worker, and organizational characteristics to the decision to transfer a case to ongoing child welfare services and their relative contribution to the transfer decision in Canada. Findings indicate that several clinical level variables are associated with families receiving ongoing services. Additionally, organizational factors, such as type of services offered by the organization and the number of employee support programs available to workers, significantly predicted the decision to transfer a case to ongoing services. While no worker factors, such as education, amount of training, experience, or caseload, were associated with ongoing service receipt, the intraclass correlation coefficient of the final three-level parsimonious model indicated substantial clustering at the worker level. Results indicate that Canadian child welfare workers make decisions differently based on factors not available in the current study and that what would be deemed as important worker characteristics do not necessarily predict this outcome. Findings and implications for future research are discussed.  相似文献