Thesis Defense

Students and faculty are encouraged to attend the student presentation. Graduate students are required to present their theses as part of their program requirements. We hope to see you there. 

Name:  Lia Nogueira de Moura

Event:  Thesis Presentation 

When:  Monday, November 2nd, 2020, 3:00 - 4:30 p.m. (CST)

Where:  Zoom:

Join Zoom Meeting
https://txstate.zoom.us/j/98586093947?pwd=L1E3TVIwMWVCWklxamIrTUVuc0c0Zz09

Meeting ID: 985 8609 3947
Passcode: 78666


Advisor:  Dr. Jelena Tesic

Title: SOCIAL NETWORK ANALYTICS AT SCALE: GRAPH-BASED ANALYSIS OF TWITTER COMMUNITIES AND TRENDS

Abstract:

Twitter influence on society and communication has motivated research work in the past decade. A large percentage of existing research focuses on specific Twitter dataset bound by time, location, topic, hashtag, and the analysis of the content of tweet messages of said datasets, and their influence on fields of business, education, geography, health, linguistics, social sciences, and public governing. Researchers have attempted to answer a variety of questions, e.g. "What topics are being discussed in Twitter dataset?",  "What communities are formed within the set of users?", "Which users are at the center of a particular discussion?", "How are users reacting to real-time events?", and more important, "How can we combine and refine existing data science techniques that can be used in other Twitter research related work?". There has been very few attempts to address the scale and design of end-to-end data processing and analysis pipeline at scale. This body of work offers one solution for a scalable way to gather, discover, analyze, and summarize joint sentiment of Twitter trends (topics, hashtags), and communities (groups of users that are bound by connection, topic, time period, or possibly location /language/interest) in larger subspace of twitter-verse. Topic discovery is improved by contextual construction network and tweet aggregation. The work offers an overarching pathway on how to construct an end-to-end data science pipeline for meaningful analysis of Twitter datasets at scale, namely data management, graph network construction, clustering, topic modeling, and graph data compression for meaningful visualization. We evaluate the data science package and different methods for graph construction and tweet data processing on over 12 million tweets over six different Twitter datasets. 

Deadline: Nov. 12, 2020, midnight