Machine Learning and Applications on Social Media Data

مرکز و کتابخانه مطالعات اسلامی به زبان های اروپایی

منو

" Machine Learning and Applications on Social Media Data "
Kalyanam, Janani Lanckriet, Gert

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	905714
Doc. No	:	TL6545w71z
Main Entry	:	Kalyanam, Janani
Title & Author	:	Machine Learning and Applications on Social Media Data\ Kalyanam, JananiLanckriet, Gert
College	:	UC San Diego
Date	:	2017
student score	:	2017
Abstract	:	The emergence of social media and advances in mobile technology and internethas resulted in constant connectivity across users enabling them to post, share, and engage with content published on the web. Studying and learning from such data aboutusers, and their engagement with content can give insights into the current and emerging trends in society. However, studying social media data comes with its own set ofunique challenges. Social media data is highly unstructured because the content is notcurated to adhere to any formal structure. This makes the process of analyzing the datachallenging. Each message published on social media has Social media data is alsohighly volatile since huge volumes of data is generated every second. In this thesis, wepropose machine learning based algorithms and methodologies to accommodate thesechallenges; and apply the algorithms to solve problems in domains of public health andjournalism.Chapter 1 proposes a new framework to combine the text and user engagementdata to detect trends from social networks.Chapter 2 studies social media data to predict the impact of news events. Thechatter on social media surrounding news events is accurately quantified, and is foundto be the most distinguishing feature between high-impact and low-impact events.Chapter 3 uses topic modeling to discover attitudes and trends about drug abuse.
Added Entry	:	Lanckriet, Gert
Added Entry	:	UC San Diego