عنوان

Machine Learning and Applications on Social Media Data

شماره

TL6545w71z

زبان متن نوشتاري يا گفتاري و مانند آن

انگلیسی

عنوان اصلي

Machine Learning and Applications on Social Media Data

نام عام مواد

[Thesis]

نام نخستين پديدآور

Kalyanam, Janani

نام ساير پديدآوران

Lanckriet, Gert

نام ناشر، پخش کننده و غيره

UC San Diego

تاریخ نشرو بخش و غیره

2017

کسي که مدرک را اعطا کرده

UC San Diego

امتياز متن

2017

متن يادداشت

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.

مستند نام اشخاص تاييد نشده

Kalyanam, Janani

مستند نام اشخاص تاييد نشده

Lanckriet, Gert

مستند نام تنالگان تاييد نشده

UC San Diego

نام الکترونيکي

فرمت انتشار

p

نوع ماده

[Thesis]

کد کاربرگه

276903

سطح دسترسي

a

تكميل شده

Y