Unsolicited messages affects virtually every popular social media website, and spammers have become increasingly proficient at bypassing conventional filters, prompting a stronger effort to develop new methods. First, we build an independent model using features that capture the cases where spam is obvious. Second, a relational model is built, taking advantage of the interconnected
nature of users and their comments. By feeding our initial predictions from the independent model into the relational model, we can propagate and jointly infer the labels of all comments at the same time. This allows us to capture the obfuscated spam comments missed by the independent model that are only found by looking at the relational structure of the social network. The results from our experiments shows that models utilizing the underlying structure of the social network are more effective at detecting spam than ones that do not.
This thesis includes previously published coauthored material.
| Identifer | oai:union.ndltd.org:uoregon.edu/oai:scholarsbank.uoregon.edu:1794/22625 |
| Date | 06 September 2017 |
| Creators | Brophy, Jonathan |
| Contributors | Lowd, Daniel |
| Publisher | University of Oregon |
| Source Sets | University of Oregon |
| Language | en_US |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Rights | All Rights Reserved. |
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