Social media data analytics for the NSW construction industry : a study on Twitter

The primary aim of this dissertation is to explore the social interaction and relationship of people within the NSW construction industry through social media data analytics. The research objective is to perform social media data analytics through Twitter and explore the social interactions between different stakeholders in the construction industry to understand the real-world situations better. The data analytics was performed on Twitter tweets, retweets, and hashtags that were collected from four clusters on construction stakeholders in NSW, namely construction workers, companies, media, and union. Tweets, retweets, and hashtags that were collected from four clusters on construction stakeholders in NSW, namely construction workers, companies, media, and unions. The thesis seeks to perform social media data analytics in order to explore and investigate the social interactions and links between the different stakeholders that are present in the construction industry. Investigating these interactions will help reveal a multitude of other related social aspects about the stakeholders, e.g., their genuine attitudes about the construction industry and how they feel being involved in this field of work. In order to facilitate this research, a social media data analytics study was carried out to find out the links and associations that are present between the construction workers, companies, unions, and media group entities. Five types of analyses were performed, namely sentiment analysis, link analysis, topic modelling, geo-location analysis, and timeline analysis. The results indicated that there are minimal social interactions between the construction workers and the other three clusters (i.e., companies, unions, and the media). The main reason that has been attributed to this observation is the way workers operate in a rather informal and casual manner. The construction companies, unions, and the media define their behavior in a much more formal and corporate attitude, hence they tend to relate to one another more than they do with workers. A number of counteractive approaches may be enforced in an effort to restore healthy social relations between workers and the other three clusters. For example, the company management teams should endeavor to develop stronger interactions with the workers and improve the working conditions, in overall

Web-Based Interactive Social Media Visual Analytics

Real-time social media platforms enable quick information broadcasting and response during disasters and emergencies. Analyzing the massive amount of generated data to understand the human behavior requires data collection and acquisition, parsing, filtering, augmentation, processing, and representation. Visual analytics approaches allow decision makers to observe trends and abnormalities, correlate them with other variables and gain invaluable insight into these situations. In this paper, we propose a set of visual analytic tools for analyzing and understanding real-time social media data in times of crisis and emergency situations. First, we model the degree of risk of individuals’ movement based on evacuation zones and post-event damaged areas. Identified movement patterns are extracted using clustering algorithms and represented in a visual and interactive manner. We use Twitter data posted in New York City during Hurricane Sandy in 2012 to demonstrate the efficacy of our approach. Second, we extend the Social Media Analytics and Reporting Toolkit (SMART) to supporting the spatial clustering analysis and temporal visualization. Our work would help first responders enhance awareness and understand human behavior in times of emergency, improving future events’ times of response and the ability to predict the human reaction. Our findings prove that today’s high-resolution geo-located social media platforms can enable new types of human behavior analysis and comprehension, helping decision makers take advantage of social media

A Location Analytics Method for the Utilisation of Geotagged Photos in Travel Marketing Decision-Making

Location analytics offers statistical analysis of any geo- or spatial data concerning user location. Such analytics can produce useful insights into the attractions of interest to travellers or visitation patterns of a demographic group. Based on these insights, strategic decision-making by travel marketing agents, such as travel package design, may be improved. In this paper, we develop and evaluate an original method of location analytics to analyse travellers' social media data for improving managerial decision support. The method proposes an architectural framework that combines emerging pattern data mining techniques with image processing to identify and process appropriate data content. The design artefact is evaluated through a focus group and a detailed case study of Australian outbound travellers. The proposed method is generic, and can be applied to other specific locations or demographics to provide analytical outcomes useful for strategic decision support

A Location Analytics Method for the Utilisation of Geotagged Photos in Travel Marketing Decision-Making

© 2019 World Scientific Publishing Co. Location analytics offers statistical analysis of any geo-or spatial data concerning user location. Such analytics can produce useful insights into the attractions of interest to travellers or visitation patterns of a demographic group. Based on these insights, strategic decision-making by travel marketing agents, such as travel package design, may be improved. In this paper, we develop and evaluate an original method of location analytics to analyse travellers\u27 social media data for improving managerial decision support. The method proposes an architectural framework that combines emerging pattern data mining techniques with image processing to identify and process appropriate data content. The design artefact is evaluated through a focus group and a detailed case study of Australian outbound travellers. The proposed method is generic, and can be applied to other specific locations or demographics to provide analytical outcomes useful for strategic decision support

Improving Big Data Visual Analytics with Interactive Virtual Reality

For decades, the growth and volume of digital data collection has made it challenging to digest large volumes of information and extract underlying structure. Coined 'Big Data', massive amounts of information has quite often been gathered inconsistently (e.g from many sources, of various forms, at different rates, etc.). These factors impede the practices of not only processing data, but also analyzing and displaying it in an efficient manner to the user. Many efforts have been completed in the data mining and visual analytics community to create effective ways to further improve analysis and achieve the knowledge desired for better understanding. Our approach for improved big data visual analytics is two-fold, focusing on both visualization and interaction. Given geo-tagged information, we are exploring the benefits of visualizing datasets in the original geospatial domain by utilizing a virtual reality platform. After running proven analytics on the data, we intend to represent the information in a more realistic 3D setting, where analysts can achieve an enhanced situational awareness and rely on familiar perceptions to draw in-depth conclusions on the dataset. In addition, developing a human-computer interface that responds to natural user actions and inputs creates a more intuitive environment. Tasks can be performed to manipulate the dataset and allow users to dive deeper upon request, adhering to desired demands and intentions. Due to the volume and popularity of social media, we developed a 3D tool visualizing Twitter on MIT's campus for analysis. Utilizing emerging technologies of today to create a fully immersive tool that promotes visualization and interaction can help ease the process of understanding and representing big data.Comment: 6 pages, 8 figures, 2015 IEEE High Performance Extreme Computing Conference (HPEC '15); corrected typo

Using Flickr to identify and connect tourism Points of Interest: The case of Lisbon, Porto and Faro

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsUnderstanding the movement of tourists helps not only the management of cities but also to enhance the most attractive places. The growth of people in social media allows us to have greater access to information about user preferences, reviews, and shared moments. Information can be used to study tourist activity. Here, it is used geo-tagged photographs from the social media platform Flickr, to identify the locations of tourists’ Points of Interest in Lisbon, Porto and Faro and quantify their relationship from the user’s co-occurrence in the identified points. The results show that, using standard clustering methods, it is possible to identify likely candidate Points of Interest. The association of the Points of Interest from users’ social media activity (i.e., posting of photos) results in a non-trivial network that breaks geographical proximity. It was found that, in all the cities under study, historical places (such as churches and cathedrals), viewpoints and beaches are captured

Using big data for customer centric marketing

This chapter deliberates on “big data” and provides a short overview of business intelligence and emerging analytics. It underlines the importance of data for customer-centricity in marketing. This contribution contends that businesses ought to engage in marketing automation tools and apply them to create relevant, targeted customer experiences. Today’s business increasingly rely on digital media and mobile technologies as on-demand, real-time marketing has become more personalised than ever. Therefore, companies and brands are striving to nurture fruitful and long lasting relationships with customers. In a nutshell, this chapter explains why companies should recognise the value of data analysis and mobile applications as tools that drive consumer insights and engagement. It suggests that a strategic approach to big data could drive consumer preferences and may also help to improve the organisational performance.peer-reviewe

The Evidence Hub: harnessing the collective intelligence of communities to build evidence-based knowledge

Conventional document and discussion websites provide users with no help in assessing the quality or quantity of evidence behind any given idea. Besides, the very meaning of what evidence is may not be unequivocally defined within a community, and may require deep understanding, common ground and debate. An Evidence Hub is a tool to pool the community collective intelligence on what is evidence for an idea. It provides an infrastructure for debating and building evidence-based knowledge and practice. An Evidence Hub is best thought of as a filter onto other websites — a map that distills the most important issues, ideas and evidence from the noise by making clear why ideas and web resources may be worth further investigation. This paper describes the Evidence Hub concept and rationale, the breath of user engagement and the evolution of specific features, derived from our work with different community groups in the healthcare and educational sector