Social influence in online environments : models and analysis
Abstract/Contents
- Abstract
- In this thesis we study the effects of social influence on the decisions of individuals in online environments. We focus on two different applications, social media and peer-to-peer lending. We initially propose a model for the evolution of market share in the presence of social influence. We study a simple market in which individuals arrive sequentially and choose one of the available products. Their decision of which product to choose is a stochastic function of the inherent quality of the product and its market share. Using techniques from stochastic approximation theory, we show that market shares converge to an equilibrium. We also derive the market shares at equilibrium in terms of the level of social influence and the inherent fitness of the products. In a special case, when the choice model is a multinomial logit model, we show that inequality in the market increases with social influence and with strong enough social influence, monopoly occurs. These results support the observations made by Salganik et al. [SDW06] in their experimental study of cultural markets. Next, we consider the effects of social influence in an online P2P lending service. Online peer- to-peer (P2P) lending services are a new type of social platform that enable individuals to borrow and lend money directly to each other. In this part of the thesis, we study the dynamics of bidder behavior in a P2P loan auction website, prosper.com. We investigate the change of various attributes of loan request listings over time, such as the interest rate and the number of bids. We observe the effects of social influence during bidding: for most listings, the rate of bids peaks at very similar time points. We explain these phenomena by showing that there are economic and social factors that lenders take into account when deciding to bid on a listing. We also observe that the profits that lenders make are tied with their bidding preferences. Finally, we build a model based on the temporal progression of the bidding, that reliably predicts the success of a loan request listing, as well as whether a loan will be paid back or not.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2011 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Ceyhan, Simla |
|---|---|
| Associated with | Stanford University, Department of Management Science and Engineering |
| Primary advisor | Saberi, Amin |
| Thesis advisor | Saberi, Amin |
| Thesis advisor | Leskovec, Jurij |
| Thesis advisor | Montanari, Andrea |
| Advisor | Leskovec, Jurij |
| Advisor | Montanari, Andrea |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Simla Ceyhan. |
|---|---|
| Note | Submitted to the Department of Management Science and Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2011. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2011 by Simla Ceyhan
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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