Publication Date

2015

Document Type

Thesis

Committee Members

Keke Chen (Committee Member), Derek Doran (Committee Member), Krishnaprasad Thirunarayan (Advisor)

Degree Name

Master of Science (MS)

Abstract

We create a robust and general feature set for learning to rank algorithms that rank tweets based on credibility and newsworthiness. In previous works, it has been demonstrated that when the training and testing data are from two distinct time periods, the ranker performs poorly. We improve upon previous work by creating a feature set that does not over fit a particular year or set of topics. This is critical given how people utilize social media changes as time progresses, and the topics discussed vary. In addition, we are constantly gaining new tweet data. Thus, it is important to be able to have a set of features that can perform well across many different topics, and across different years. In our approach, we present a methodology for selecting features based on how they can capture credibility and newsworthiness regardless of year and topic. In order to derive such features, we use the studies done on credibility perception of social media as well as the clues provided in past works in this domain. We also present new features that, to our knowledge, have not been used in previous works in this domain.

Page Count

65

Department or Program

Department of Computer Science

Year Degree Awarded

2015

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