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Using machine learning techniques to...

Bora, Prachi.

Using machine learning techniques to predict the education level of Twitter users.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Using machine learning techniques to predict the education level of Twitter users.
Author:	Bora, Prachi.
Description:	67 p.
Notes:	Source: Masters Abstracts International, Volume: 55-02.
Notes:	Adviser: Tim Oates.
Contained By:	Masters Abstracts International55-02(E).
Subject:	Computer science.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1603712
ISBN:	9781339234298

Using machine learning techniques to predict the education level of Twitter users.
Bora, Prachi.

Using machine learning techniques to predict the education level of Twitter users. - 67 p.

Source: Masters Abstracts International, Volume: 55-02.

Thesis (M.S.)--University of Maryland, Baltimore County, 2015.

Over the past two decades, social media has been widely used to connect, socialize, and share content across the world. Platforms such as Twitter, Facebook, LinkedIn, and Google+ produce enormous amounts of data by and about users. This data can be used to predict hidden or latent attributes of a user like gender, age group, political inclination, regional origin, and many others, with applications in targeted advertising, recommendations, and personalization. While there is significant prior work on trying to predict attributes like age and gender, our work focuses on predicting the educational level of users from their Twitter feed. Specifically, we try to determine if a user is in high school or has (or is working on) a Ph.D. We use a machine learning approach to predict the educational level of Twitter users by looking at their tweet stream using Support Vector Machines, Perceptron, Logistic Regression, k-Nearest Neighbors, and Naive Bayes. We extract features from each class that clearly describe the characteristics of tweets from members of the classes. We also attempt to discover the abstract topics that occur in this dataset using Latent Dirichlet Allocation. These user attributes provide a very useful source of information for a variety of applications like advertising, search, friend recommendation, computational social science and many more.

ISBN: 9781339234298Subjects--Topical Terms:

199325
Computer science.

Using machine learning techniques to predict the education level of Twitter users.
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