Full metadata
Title
An information diffusion approach to detecting emotional contagion in online social networks
Description
Internet sites that support user-generated content, so-called Web 2.0, have become part of the fabric of everyday life in technologically advanced nations. Users collectively spend billions of hours consuming and creating content on social networking sites, weblogs (blogs), and various other types of sites in the United States and around the world. Given the fundamentally emotional nature of humans and the amount of emotional content that appears in Web 2.0 content, it is important to understand how such websites can affect the emotions of users. This work attempts to determine whether emotion spreads through an online social network (OSN). To this end, a method is devised that employs a model based on a general threshold diffusion model as a classifier to predict the propagation of emotion between users and their friends in an OSN by way of mood-labeled blog entries. The model generalizes existing information diffusion models in that the state machine representation of a node is generalized from being binary to having n-states in order to support n class labels necessary to model emotional contagion. In the absence of ground truth, the prediction accuracy of the model is benchmarked with a baseline method that predicts the majority label of a user's emotion label distribution. The model significantly outperforms the baseline method in terms of prediction accuracy. The experimental results make a strong case for the existence of emotional contagion in OSNs in spite of possible alternative arguments such confounding influence and homophily, since these alternatives are likely to have negligible effect in a large dataset or simply do not apply to the domain of human emotions. A hybrid manual/automated method to map mood-labeled blog entries to a set of emotion labels is also presented, which enables the application of the model to a large set (approximately 900K) of blog entries from LiveJournal.
Date Created
2011
Contributors
- Cole, William David, M.S (Author)
- Liu, Huan (Thesis advisor)
- Sarjoughian, Hessam S. (Committee member)
- Candan, Kasim S (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
vi, 51 p. : col. ill
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.9470
Statement of Responsibility
William David Cole
Description Source
Viewed on Jan. 6, 2012
Level of coding
full
Note
thesis
Partial requirement for: M.S., Arizona State University, 2011
bibliography
Includes bibliographical references (p. 46-51)
Field of study: Computer science
System Created
- 2011-08-12 05:12:38
System Modified
- 2021-08-30 01:50:59
- 3 years 2 months ago
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