Cascading behavior in social networks
- Cascades occur in social networks when information and behavior spreads from person to person. However, as these cascades grow through complex macro- and micro-level processes, their future behavior is difficult to predict. In this thesis, we study the mechanisms through which cascades propagate in networks. Specifically, we present a framework for predicting a cascade's future trajectory, and develop methods that combine data mining and crowdsourcing techniques to explain how behavior spreads from individual to individual. First, we examine the growth and recurrence of information cascades on Facebook. In contrast to prior work that argued that such cascades are unpredictable, we show how the size, structure, and recurrence of a cascade can be predicted, even over long periods of time. Second, we study how behavior cascades by looking at how antisocial behavior such as trolling may spread from person to person. While past literature has characterized such behavior as confined to a vocal, antisocial minority, we instead demonstrate that ordinary people, under the right circumstances, can become trolls, and that such behavior can percolate and escalate through a community. Altogether, this research explores a future where systems can better mediate information sharing and interpersonal interaction, and thus promote the development of prosocial communities.
|Type of resource
|electronic; electronic resource; remote
|1 online resource.
|Stanford University, Computer Science Department.
|Statement of responsibility
|Submitted to the Department of Computer Science.
|Thesis (Ph.D.)--Stanford University, 2017.
- © 2017 by Jus Tin Cheng
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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