Date of Award

4-2017

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Engineering and Sciences

First Advisor

Ronaldo Menezes

Second Advisor

Ryan Stansifer

Third Advisor

Pat Bond

Fourth Advisor

Lisa Steelman

Abstract

Online social networks (e.g., Twitter and Facebook) play a vital role in the spreading of information in today’s world. Interestingly, the spread of information is enabled by the existence of an underlying connectivity of the users. One factor influencing the online connectivity, which only recently has been receiving attention, is the language used by the user in his or her activities. The understanding of information propagation from the perspective of languages is of particular interest because we live in a world with a very diverse set of languages. Using Network Science approaches, we demonstrate that Twitter users have a strong preference to connect to people who use their own language, but more importantly, we found that this preference is stronger than the tendency to connect to people with a similar popularity level (i.e., the traditional notion of homophily). The connecting patterns between users of different languages vary considerably and such patterns shed light on the similarity of languages from a user-preference point of view. Furthermore, we unveil the “Twitter Language Network”, a connected system of many different languages, and we analyze several of its interesting characteristics. In addition, we demonstrate that the position of languages in the Twitter Language Network correlates with the social and development indicator of the language users. This dissertation presents an investigation of the language structure of Twitter, giving a better understanding of the connectivity of users in the context of their languages.

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