Viewing Airbnb from Twitter: factors associated with users’ utilization.

Airbnb is a peer-to-peer accommodation website in the sharing economy. Past studies have examined the factors associated with Airbnb utilization from various platforms, but not exclusively from Twitter. A total of 21,097 tweets was collected in a period of two months, and the tweets were qualitatively analyzed with the help of text analysis tools to verify the discourse of discussion. Literature was reviewed for common factors attracting clients to an Airbnb accommodation. Factors were then qualitatively analyzed and compiled using Wmatrix, and the themes that emerged were: Price and status, social interaction and communication, location, reputation, amenities and a pet-friendly environment. This result provides a deeper insight to Airbnb hosts to strategize and add value to their current market situations

Investigating transportation research based on social media analysis: A systematic mapping review

Social media is a pool of users’ thoughts, opinions, surrounding environment, situation and others. This pool can be used as a real-time and feedback data source for many domains such as transportation. It can be used to get instant feedback from commuters; their opinions toward the transportation network and their complaints, in addition to the traffic situation, road conditions, events detection and many others. The problem is in how to utilize social media data to achieve one or more of these targets. A systematic review was conducted in the field of transportation-related research based on social media analysis (TRRSMA) from the years between 2008 and 2018; 74 papers were identified from an initial set of 703 papers extracted from 4 digital libraries. This review will structure the field and give an overview based on the following grounds: activity, keywords, approaches, social media data and platforms and focus of the researches. It will show the trend in the research subjects by countries, in addition to the activity trends, platforms usage trend and others. Further analysis of the most employed approach (Lexicons) and data (text) will be also shown. Finally, challenges and future works are drawn and proposed

Some families of count distributions for modelling zero-inflation and dispersion / Low Yeh Ching

A popular distribution for the modelling of discrete count data is the Poisson distribution. However, count data usually exhibit over dispersion or under dispersion when modelled by a Poisson distribution in empirical modelling. The presence of excess zeros is also closely related to over dispersion. Two new mixed Poisson distributions, namely a three-parameter Poisson-exponentiated Weibull distribution and a fourparameter generalized Sichel distribution is introduced to model over dispersed, zeroinflated and long-tailed count data. Some of the theoretical properties of the distributions are derived and the distributions' characteristics are studied. A Monte Carlo simulation technique is examined and employed to overcome the computational issues arising from the intractability of the probability mass function of some mixed Poisson distributions. For parameter estimation, the simulated annealing global optimization routine and an EM-algorithm type approach for maximum likelihood estimation are studied. Examples are provided to compare the proposed distributions with several other existing mixed Poisson models. Another approach to modelling count data is by examining the relationship between the counts of number of events which has occurred up to a fixed time t and the inter-arrival times between the events in a renewal process. A family of count distributions, which is able to model under- and over dispersion, is presented by considering the inverse Gaussian distribution, the convolution of two gamma distributions and a finite mixture of exponential distributions as the distribution of the inter-arrival times. The probability function of the counts is often complicated thus a method using numerical Laplace transform inversion for computing the probabilities and the renewal function is proposed. Parameter estimation with maximum likelihood estimation is considered with applications of the count distributions to under dispersed and over dispersed count data from the literature

User mode choice behavior in public transportation : a systematic literature review

With the staggering concerns in environmental pollution caused by the transportation industry, researchers have ventured their studies to identify the primary factors that may affect an individuals’ propensity to choose public transportation as more sustainable transportation. Numerous research studies are surrounding this subject; however, no effort has been made to systematically review them for a synthesized analysis. There is also a lack of study in identifying contributing factors that may potentially affect each other to determine the propensity to take public transportation and identify their relationships to each other to provide a guide for future researchers to analyse and consider for future work. With the motivation to tackle the existing research inadequacies, we conducted a study that focuses on using a systematic literature review methodology with validated analyses on existing studies. This study found that demographic factors are the most analysed aspect, followed by transportation, trip-related and environmental. Secondly, our study provides new insight into several contributing factors affecting each other in predicting public transportation ridership. Thirdly, our findings also demonstrate the trends and gaps that occur in different geographical areas. The outcomes of our research present a consolidated view for relevant authorities to cater their strategies tactically according to each geographical area and identify potential opportunities for future research

Rapid production of clinical-grade SARS-CoV-2 specific T cells.

10.1002/acg2.101Adv Cell Gene Ther34e101