Anatoliy Gruzd & Philip Mai
Social Media Lab, Ted Rogers School of Management, Ryerson University

Lecture Details
Monday, November 19th, 2018
2:30pm
Room 3089, Kenneth C. Rowe Management Building
6100 University Ave

Abstract: Social media data is a rich source of behavioral data that can reveal how we connect and interact with each other online in real time and over time. Once collected, the data can be combined with other types of data to reveal even more sensitive information about the users. Currently, much of the data being collected from social media platforms are being used in ways that are not always transparent to users. In the aftermath of the Facebook’s Cambridge Analytica scandal and other data breaches, issues around data privacy is once again on the public’s mind.

In this talk, we will provide an overview of the work that we do at Ryerson University’s Social Media Lab. We will also share the result of a recently completed study led by Anatoliy Gruzd, Jenna Jacobson and Elizabeth Dubois, examining users’ attitudes towards organizations’ use of public social media data to screen job applicants. The study applied the Communication Privacy Management theory to assess whether and to what extent users’ privacy concerns are influenced by factors such as: culture, gender, motivation, and risk-benefit ratio. The results revealed a nuanced nature of people’s information privacy expectations in the context of hiring practices. The talk will be of interest to privacy, organizational studies and communication scholars as well as everyday social media users who are interested in online privacy.

Biographies:
Anatoliy Gruzd is a Canada Research Chair in Social Media Data Stewardship, Associate Professor at the Ted Rogers School of Management at Ryerson University, and Director of Research at the Social Media Lab. Anatoliy is also a Member of the Royal Society of Canada’s College of New Scholars, Artists and Scientists; a co-editor of a multidisciplinary journal on Big Data and Society; and a founding co-chair of the International Conference on Social Media and Society. His research initiatives explore how social media platforms are changing the ways in which people and organizations communicate, collaborate and disseminate information and how these changes impact the norms and structures of modern society.

Philip Mai is the Director of Business and Communications at Ryerson Social Media Lab at Ted Rogers School of Management and co-founder of the International Conference on Social Media & Society.  In his work at the Social Media Lab, he focuses on knowledge mobilization, information diffusion, business and research partnerships, and the practical application of social media analytics.

Jamal Shahrabi
Amirkabir University of Technology, Iran

Lecture Details
Thursday, September 13th, 2018
1:30pm
Room 5053, Kenneth C. Rowe Management Building
6100 University Ave

Abstract: Big data analytics is the process of examining large and varied data sets to discover hidden patterns, unknown correlations, market trends, customer knowledge and other useful information that can help organizations make more-informed business decisions. All industries now are facing with a large amount of data and complex management issues with a much more competition than before. The most important benefits of big data analytics compare to classical analytical methods are speed and efficiency. Few years ago a business would have gathered data, run traditional analytics and provided information that could be used for future decisions, today that business can identify insights for immediate decisions by smart management systems. Meanwhile all organizations and industries are involving with a broad range of decision making criteria and multiple different internal  goals and targets that make decision taking difficult. In this situation classical analytical methods do not work anymore and Multi Agent Systems (MAS) are needed. Multi Agent Ensemble Learning Systems are used in a variety of domains for making collaborative smart decision support systems by discovering a solution by agents on their own, using learning. The most important part of the problem is how the agents will learn independently and then how they will cooperate to establish the common task.

Biography: Dr. Jamal Shahrabi received his PhD from Dalhousie University. He is a faculty member of Industrial Engineering and Management Systems faculty at Amirkabir University of Technology (AUT). His research interests lie at the intersection of data science (particularly data analysis, artificial intelligence, machine learning & data mining) and Management and Marketing (particularly smart management, operations management, management information systems, business intelligence, customer knowledge discovery, customer relationship management & smart marketing,). His research contributions has been to develop the efficient machine learning and data mining models to solve the real management and marketing problems by designing smart models and systems for smart decision making and smart management. He has been succeed to make a synergy of university and industry to solve the industry management problems and provide the opportunity of involvement of students in real industry issues. He has managed 9 big size industry projects so far. He has published two book chapters, 13 books in Persian, 136 conference papers & 34 outstanding ISI journal papers with 865 Citations So far. Graduating 73 Master and 5 PhD students under his supervision and teaching several different bachelor, master and PhD courses in the field of management and marketing is his honor.