In the past two decades, social media has become one of the most popular sources of large-scale and granular data about people. This has resulted in many academic studies that have used social media data to understand many different phenomena. Some of this research has focused on using social data, including social media data, on identifying different kinds of social ties online and the role these social ties play in various contexts. Over the past decade, many different approaches and models have been built to identify social ties using social media data. These methods have been built using private data and explicit social relationship data of users’ social media platforms. However, in the past few years, it has become nearly impossible to access this kind of social media data due to many different reasons.
In this doctoral research, Jayesh Prakash Gupta carried out three different studies to develop an approach to identifying social ties from publicly available social data. The dissertation also focused on understanding the role of the identified social ties in different contexts like events and crowdfunding. The first two studies were single-case case studies, while the third was an experiment where two different sets of hypotheses were tested using empirical data. All three studies used publicly available social media data related to a specific context.
The first study used a large dataset related to a game developer community on Facebook. The second study used social media data related to a business event from Twitter and Facebook. The third study used large datasets associated with social media data about crowdfunding projects from Twitter and crowdfunding project data from Kickstarter.com. These studies have created an approach for identifying implicit social ties from social data which could be further tested in other new contexts.
The doctoral dissertation of M.Sc. (Tech) Jayesh Prakash Gupta in the field of information and knowledge management titled Identification & Role of Implicit Social Ties from Social Data will be publicly examined in the Faculty of Management and Business at Tampere University at 13:00 on Friday 16 Dec. 2022 in auditorium S2 (SA203) of Sähkötalo, address: Korkeakoulunkatu 3, Tampere. The Opponent will be Dr. Kaisa Still from the University of Oulu, Finland. The custos will be Professor Hannu Kärkkäinen from the Faculty of Management and Business, Tampere University, Finland.
The doctoral dissertation is available online.
Photo: Shruti Mittal