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  4. Understanding Innovations and Conventions and Their Diffusion Process in Online Social Media

Understanding Innovations and Conventions and Their Diffusion Process in Online Social Media

File(s)
Rotabi_cornellgrad_0058F_10652.pdf (2.73 MB)
Permanent Link(s)
https://doi.org/10.7298/X4XS5SM2
https://hdl.handle.net/1813/59045
Collections
Cornell Theses and Dissertations
Author
Rotabi, Rahmtin
Abstract

This thesis investigates innovations, trends, conventions and practices in online social media. Tackling these problems will give more insight into how their users use these online platforms with the hope that the results can be generalized to the offline world. Every major step in human history was accompanied by an innovation, from the time that mankind invented and mastered the production of fire, to the invention of the World Wide Web. The societal process of adopting innovations has been a case that has fascinated many researchers throughout the past century. Prior to the existence of online social networks, economists and sociologists were able to study these phenomena on small groups of people through microeconomics and microsociology. However, the data gathered from these online communities help us to take one step further, initiating studies on macroeconomic and macrosociologal problems—in addition to the previous two areas. Work in this thesis sheds light on the properties of both innovators and laggards, the expansion and adaptation of innovation, competition among innovations with the same purpose, and the eventual crowding out of competitor innovations in the target society. Lastly, we look at the bigger picture by studying the entire diffusion process as a whole, abstracting out a great deal of details. This offers a view on why every single idea, content, product, etc., fails to go viral.

Date Issued
2017-12-30
Keywords
Cascades
•
Conventions
•
Data Mining
•
Innovations
•
Spread of Influence
•
Computer science
•
Social Media
Committee Chair
Kleinberg, Jon M.
Committee Member
Kleinberg, Robert David
Lee, Lillian Jane
Ghosh, Arpita
Degree Discipline
Computer Science
Degree Name
Ph. D., Computer Science
Degree Level
Doctor of Philosophy
Rights
Attribution 4.0 International
Rights URI
https://creativecommons.org/licenses/by/4.0/
Type
dissertation or thesis

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