A big data approach to examining social bots on Twitter

2019 ◽  
Vol 33 (4) ◽  
pp. 369-379 ◽  
Author(s):  
Xia Liu
Keyword(s):  
Big Data ◽  
Fixed Effects ◽  
Latent Dirichlet Allocation ◽  
User Generated Content ◽  
Fixed Effects Model ◽  
Data Set ◽  
Information Distortion ◽  
Content Type ◽  
Related Information ◽  
Brand Reputation

Purpose Social bots are prevalent on social media. Malicious bots can severely distort the true voices of customers. This paper aims to examine social bots in the context of big data of user-generated content. In particular, the author investigates the scope of information distortion for 24 brands across seven industries. Furthermore, the author studies the mechanisms that make social bots viral. Last, approaches to detecting and preventing malicious bots are recommended. Design/methodology/approach A Twitter data set of 29 million tweets was collected. Latent Dirichlet allocation and word cloud were used to visualize unstructured big data of textual content. Sentiment analysis was used to automatically classify 29 million tweets. A fixed-effects model was run on the final panel data. Findings The findings demonstrate that social bots significantly distort brand-related information across all industries and among all brands under study. Moreover, Twitter social bots are significantly more effective at spreading word of mouth. In addition, social bots use volumes and emotions as major effective mechanisms to influence and manipulate the spread of information about brands. Finally, the bot detection approaches are effective at identifying bots. Research limitations/implications As brand companies use social networks to monitor brand reputation and engage customers, it is critical for them to distinguish true consumer opinions from fake ones which are artificially created by social bots. Originality/value This is the first big data examination of social bots in the context of brand-related user-generated content.

Transport infrastructure, accessibility and residential property values: evidence from Hong Kong

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jayantha Wadu Mesthrige ◽  
Tayyab Maqsood
Keyword(s):  
Hong Kong ◽  
Fixed Effects ◽  
Property Values ◽  
Transport Infrastructure ◽  
Residential Property ◽  
Fixed Effects Model ◽  
Data Set ◽  
Content Type ◽  
Residential Properties ◽  
Residential Property Values

PurposeHong Kong, like many other developed cities and countries, invests heavily in transport development. This study investigates whether the speculative benefits of future improvements in accessibility, brought about by impending transport development, will be capitalized into nearby residential property values even prior to the opening of the development.Design/methodology/approachDeviating from the standard hedonic price approach, the present study employed a fixed-effects model with a large data set of residential property transactions in the vicinity of three-stations situated along a newly proposed mass-transit-railway line in Hong Kong.FindingsThe results suggest that the values of residential properties close to stations do reflect the accessibility enhancements to be brought about by transport improvements even before the opening of the line. Results revealed a 6.5% of property value premium after the announcement of construction; and higher up to 6.7% after the operation of the line. This indicates that forthcoming new transport-infrastructure development produces changes in spatial price-gradients for neighbouring residential properties. Findings indicate that potential buyers/investors recognized the positive benefits of the planned transportation development, even before completion of the project, and are ready to pay a premium for those properties close to railway stations, representing clear evidence that residential property prices/values, near stations, reflect anticipated accessibility enhancements brought about by transport improvements.Originality/valueThis study, using a novel approach – a fixed-effects model to capture the speculative benefits of future improvements in transport infrastructure – provides a positive hypothesis that expected benefits of future improvements in accessibility are capitalized into property values.

Which food prices were more vulnerable to the COVID-19 blockade regulations in China? Comparison between perishable and storable vegetables

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yupeng Wang ◽  
Satoru Shimokawa
Keyword(s):  
Spatial Dependence ◽  
Fixed Effects ◽  
Design Methodology ◽  
Food Prices ◽  
Panel Var ◽  
Fixed Effects Model ◽  
Dynamic Panel ◽  
Data Set ◽  
Content Type ◽  
The Impact

PurposeThis paper aims to investigate how differently the COVID-19 blockade regulations influence the prices of perishable and storable foods. The authors focus on the cases of the 2020 blockade at Hubei province and the 2021 blockade at Shijiazhuang city in China, and the authors examine how the blockade influenced the prices of Chinese cabbages (perishable) and potatoes (storable) within and around the blockade area.Design/methodology/approachThe paper employs the fixed effects model, the panel VAR (PVAR) model, and the spatial dynamic panel (SPD) model to estimate the impacts of the blockade on the food prices. It constructs the unique data set of 3-day average prices of Chinese cabbages and potatoes at main wholesale markets in China during the two urban blockade periods from January 1 to April 8 in 2020 and from January 1 to March 1 in 2021.FindingsThe results from the SPD models indicate that the price of Chinese cabbages was more vulnerable and increased by 7.1–9.8% due to the two blockades while the price of potatoes increased by 1.2–6.1%. The blockades also significantly influenced the prices in the areas adjacent to the blockade area. The SPD results demonstrate that the impacts of the blockades would be overestimated if the spatial dependence is not controlled for in the fixed effects model and the PVAR model.Research limitations/implicationsBecause the research focuses on the cases in China, the results may lack generalizability. Further research for other countries is encouraged.Originality/valueThis paper demonstrates the importance of considering food types and spatial dependence in examining the impact of the COVID-19 blockades on food prices.

Child schooling in India: the role of gender

2015 ◽  
Vol 8 (1) ◽  
pp. 93-108 ◽  
Author(s):  
Itismita Mohanty ◽  
ANU RAMMOHAN
Keyword(s):  
Fixed Effects ◽  
Sample Selection ◽  
Family Health ◽  
Large Sample Size ◽  
Fixed Effects Model ◽  
Data Set ◽  
Content Type ◽  
Child Schooling ◽  
Years Of Schooling

Purpose – This paper aims to analyse factors that influence child schooling outcomes in India, specifically the role of gender. Design/methodology/approach – This paper uses data from the nationally representative Indian National Family Health Surveys 1995-1996 and 2005-2006 and estimates Heckman sample selection, cluster fixed-effects and household fixed-effects econometric models. The dependent variables are the child’s enrolment status and conditional on enrolment child’s years of schooling. Findings – This analysis finds statistically significant evidence of male advantage both in schooling enrolment as well as years of schooling. However, using a cluster fixed-effects model, our analysis finds that within a village, conditional on being enrolled, girls spend more years in school relative to boys. Other results show that parental schooling has a positive and statistically significant impact on child schooling. There is statistically significant wealth effect, community effect and regional disparities between states in India. Originality/value – The large sample size and the range of questions available in this data set, allows us to explore the influence of individual, household and village level social, economic and cultural factors on child schooling. The role of gender on child schooling within a village, intrahousehold resource allocation for schooling and regional gender differences in schooling are important issues in India, where education outcomes remain poor for large segments of the population.

Economies of scale in European life insurance

2018 ◽  
Vol 19 (2) ◽  
pp. 190-207 ◽  
Author(s):  
Udo Klotzki ◽  
Alexander Bohnert ◽  
Nadine Gatzert ◽  
Ulrike Vogelgesang
Keyword(s):  
Life Insurance ◽  
Fixed Effects ◽  
Economies Of Scale ◽  
Insurance Markets ◽  
Fixed Effects Model ◽  
Content Type ◽  
Administrative Costs ◽  
Life Insurers ◽  
Acquisition Costs ◽  
The Uk

Purpose Due to the continuing low interest rate environment as well as the increase in acquisition costs, price transparency, cost transparency and competition with banks, the cost of life insurance becomes increasingly important for customers, insurers and shareholders. Against this background, the purpose of this paper is to study the development of insurers’ economies of scale in regard to administrative costs for four of the largest European life insurance markets. Design/methodology/approach The analysis on economies of scale is based on a comprehensive set of 477 life insurers in Germany, Italy, Spain and the UK, yearly data between 2000 and 2014, and regression calculations that are based on 4,855 observations. Findings The results show that economies of scale exist for all considered markets and for most of the considered years. However, the extent of economies of scale varies considerably across countries. Originality/value Overall, the existing academic literature on costs and corresponding economies of scale in life insurance primarily deals with analyses of total costs instead of administrative costs, a single year or a single market. This paper contributes to the existing literature by conducting an analysis of recent market dynamics and economies of scale in regard to administrative costs for the period from 2000 and 2014 for four of the largest European life insurance markets for which the respective data were available (Germany, Italy, Spain and the UK) and 477 life insurers in total. This is done by means of a log-log transformation of premiums and costs and a fixed effects model based on these transformed figures for 4,855 observations. In addition, for each market, the authors analyze the development of administrative costs for a total of 477 insurers.

Mining numerical measure of consumers’ product evaluation expressed in words based on latent Dirichlet allocation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ziang Wang ◽  
Feng Yang
Keyword(s):  
Latent Dirichlet Allocation ◽  
Product Evaluation ◽  
Online Reviews ◽  
Product Evaluations ◽  
Product Attributes ◽  
Data Set ◽  
Content Type ◽  
Face To Face ◽  
Online Retailers ◽  
Dirichlet Allocation

Purpose It has always been a hot topic for online retailers to obtain consumers’ product evaluations from massive online reviews. In the process of online shopping, there is no face-to-face interaction between online retailers and customers. After collecting online reviews left by customers, online retailers are eager to acquire answers to some questions. For example, which product attributes will attract consumers? Or which step brings a better experience to consumers during the process of shopping? This paper aims to associate the latent Dirichlet allocation (LDA) model with the consumers’ attitude and provides a method to calculate the numerical measure of consumers’ product evaluation expressed in each word. Design/methodology/approach First, all possible pairs of reviews are organized as a document to build the corpus. After that, latent topics of the traditional LDA model noted as the standard LDA model, are separated into shared and differential topics. Then, the authors associate the model with consumers’ attitudes toward each review which is distinguished as positive review and non-positive review. The product evaluation reflected in consumers’ binary attitude is expanded to each word that appeared in the corpus. Finally, a variational optimization is introduced to calculate parameters mentioned in the expanded LDA model. Findings The experiment’s result illustrates that the LDA model in the research noted as an expanded LDA model, can successfully assign sufficient probability with words related to products attributes or consumers’ product evaluation. Compared with the standard LDA model, the expanded model intended to assign higher probability with words, which have a higher ranking within each topic. Besides, the expanded model also has higher precision on the prediction set, which shows that breaking down the topics into two categories fits better on the data set than the standard LDA model. The product evaluation of each word is calculated by the expanded model and depicted at the end of the experiment. Originality/value This research provides a new method to calculate consumers’ product evaluation from reviews in the level of words. Words may be used to describe product attributes or consumers’ experiences in reviews. Assigning words with numerical measures can analyze consumers’ products evaluation quantitatively. Besides, words are labeled themselves, they can also be ranked if a numerical measure is given. Online retailers can benefit from the result for label choosing, advertising or product recommendation.

Collaborative matrix factorization mechanism for group recommendation in big data-based library systems

2018 ◽  
Vol 36 (3) ◽  
pp. 458-481 ◽  
Author(s):  
Yezheng Liu ◽  
Lu Yang ◽  
Jianshan Sun ◽  
Yuanchun Jiang ◽  
Jinkun Wang
Keyword(s):  
Big Data ◽  
Matrix Factorization ◽  
Empirical Studies ◽  
Interaction Matrix ◽  
Group Recommendation ◽  
Data Set ◽  
Content Type ◽  
Potential Value ◽  
Membership Retention ◽  
Online Library

Purpose Academic groups are designed specifically for researchers. A group recommendation procedure is essential to support scholars’ research-based social activities. However, group recommendation methods are rarely applied in online libraries and they often suffer from scalability problem in big data context. The purpose of this paper is to facilitate academic group activities in big data-based library systems by recommending satisfying articles for academic groups. Design/methodology/approach The authors propose a collaborative matrix factorization (CoMF) mechanism and implement paralleled CoMF under Hadoop framework. Its rationale is collaboratively decomposing researcher-article interaction matrix and group-article interaction matrix. Furthermore, three extended models of CoMF are proposed. Findings Empirical studies on CiteULike data set demonstrate that CoMF and three variants outperform baseline algorithms in terms of accuracy and robustness. The scalability evaluation of paralleled CoMF shows its potential value in scholarly big data environment. Research limitations/implications The proposed methods fill the gap of group-article recommendation in online libraries domain. The proposed methods have enriched the group recommendation methods by considering the interaction effects between groups and members. The proposed methods are the first attempt to implement group recommendation methods in big data contexts. Practical implications The proposed methods can improve group activity effectiveness and information shareability in academic groups, which are beneficial to membership retention and enhance the service quality of online library systems. Furthermore, the proposed methods are applicable to big data contexts and make library system services more efficient. Social implications The proposed methods have potential value to improve scientific collaboration and research innovation. Originality/value The proposed CoMF method is a novel group recommendation method based on the collaboratively decomposition of researcher-article matrix and group-article matrix. The process indirectly reflects the interaction between groups and members, which accords with actual library environments and provides an interpretable recommendation result.

The elephant in the room of mutual forbearance

2018 ◽  
Vol 11 (2) ◽  
pp. 257-279 ◽  
Author(s):  
Burak Cem Konduk
Keyword(s):  
Airline Industry ◽  
Fixed Effects ◽  
Research Question ◽  
Fixed Effects Model ◽  
Content Type ◽  
High Performing ◽  
The Us ◽  
Mutual Forbearance ◽  
Low Performance ◽  
The Impact

PurposeThe purpose of this paper is to explain how a multi-market firm develops the motivation to forbear from competition.Design/methodology/approachA two-way fixed effects model with Driscoll and Kraay standard errors investigates the research question with panel data collected from the US scheduled passenger airline industry.FindingsThe results demonstrate that although the interaction of multi-market contact with strategic similarity impairs a firm’s forbearance from competition, the same interaction promotes it as firm performance deteriorates, supporting the hypotheses.Research limitations/implicationsPerformance explains not only how forbearance emerges out of coincidental multi-market contact but also reconciles the mixed evidence for the impact of the two-way interaction between multi-market contact and strategic similarity on forbearance.Practical implicationsAntitrust authorities should pay more attention to low performing firms than to high performing firms in their investigations. Also, managers of multi-market firms should identify multi-market rivals with low performance as targets for the initiation of forbearance.Originality/valueThis study revises the mutual forbearance theory to align it with the accumulating empirical evidence that otherwise refutes its assumption and thereby improves theory’s descriptive and predictive power.

Does precarious employment ruin youth health and marriage? Evidence from Egypt using longitudinal data

2020 ◽  
Vol 19 (3) ◽  
pp. 391-406
Author(s):  
Mesbah Fathy Sharaf ◽  
Ahmed Shoukry Rashad
Keyword(s):  
Mental Health ◽  
Longitudinal Data ◽  
Fixed Effects ◽  
Job Insecurity ◽  
Well Being ◽  
Poor Mental Health ◽  
Youth Workers ◽  
Fixed Effects Model ◽  
Precarious Employment ◽  
Content Type

Purpose This study aims to analyze whether precarious employment is associated with youth mental health, self-rated health and happiness in marriage and whether this association differs by sex. Design/methodology/approach This paper uses longitudinal data from the Survey of Young People in Egypt conducted in 2009 and 2014 and estimates a fixed-effects model to control for time-invariant unobserved individual heterogeneity. The analysis is segregated by sex. Findings The results indicate that precarious employment is significantly associated with poor mental health and less happiness in marriage for males and is positively associated with poor self-reported health for females. The adverse impact of precarious work is likely to be mediated through poor working conditions such as low salary, maltreatment at work, job insecurity and harassment from colleagues. Social implications Governmental policies that tackle job precariousness are expected to improve population health and marital welfare. Originality/value Egypt has witnessed a significant increase in the prevalence of precarious employment, particularly among youth, in recent decades, yet the evidence on its effect on the health and well-being of youth workers is sparse. This paper adds to the extant literature by providing new evidence on the social and health repercussions of job precariousness from an understudied region.

scholarly journals The relationship between debt and a firm’s performance: the impact of institutional factors

2019 ◽  
Vol 45 (9) ◽  
pp. 1272-1291 ◽  
Author(s):  
Rosa Forte ◽  
José Miguel Tavares
Keyword(s):  
Fixed Effects ◽  
Negative Relationship ◽  
Research Area ◽  
Institutional Framework ◽  
Market Regulation ◽  
Fixed Effects Model ◽  
Content Type ◽  
Macroeconomic Variables ◽  
The Impact ◽  
The Relationship

Purpose The purpose of this paper is to contribute to the existing literature on the relationship between debt and firms’ performance, by focusing on the influence of the institutional framework on this relationship and on the role of macroeconomic variables in explaining performance. Design/methodology/approach The present work is based on a large sample of 48,840 manufacturing firms from nine European countries covering the 2008–2013 period and uses a fixed effects model. Findings Results show that the impact of debt on a firm’s performance depends on the measure of debt (short-term debt positively affects a firm’s performance, whereas long-term debt presents a negative relationship) and that the institutional framework is indeed affecting the relationship between debt and a firm’s performance: the positive effect of debt on a firm’s performance tends to be higher the greater the “efficiency of the legal system” and the greater the “credit market regulation.” Macroeconomic variables also play a key role in explaining performance. Originality/value Unlike most of the existing studies, which focus only on the relationship between debt and firms’ performance in a single country, the present work uses a sample of firms from nine countries with the purpose of filling a research gap and bringing new empirical evidence to this research area.

scholarly journals The Negative Effect of Smartphone Use on Academic Performance May Be Overestimated: Evidence From a 2-Year Panel Study

2020 ◽  
Vol 31 (11) ◽  
pp. 1351-1362
Author(s):  
Andreas Bjerre-Nielsen ◽  
Asger Andersen ◽  
Kelton Minor ◽  
David Dreyer Lassen
Keyword(s):  
Academic Performance ◽  
Fixed Effects ◽  
Panel Study ◽  
Student Characteristics ◽  
Fixed Effects Model ◽  
Data Set ◽  
Course Characteristics ◽  
Negative Effect ◽  
Smartphone Use ◽  
The Relationship

In this study, we monitored 470 university students’ smartphone usage continuously over 2 years to assess the relationship between in-class smartphone use and academic performance. We used a novel data set in which smartphone use and grades were recorded across multiple courses, allowing us to examine this relationship at the student level and the student-in-course level. In accordance with the existing literature, our results showed that students’ in-class smartphone use was negatively associated with their grades, even when we controlled for a broad range of observed student characteristics. However, the magnitude of the association decreased substantially in a fixed-effects model, which leveraged the panel structure of the data to control for all stable student and course characteristics, including those not observed by researchers. This suggests that the size of the effect of smartphone usage on academic performance has been overestimated in studies that controlled for only observed student characteristics.

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