User Modeling in Social Media ; Gebruikersmodellen in sociale media

Author:

Farnadi, Golnoosh [claim]

Description:

The era of Social Media-as we know it today-started around the early 2000s. Social media enable a form of virtual content sharing that is fundamentally different than before. Social media content is no longer created and published by specific individuals, but instead is continuously modified by all users in a collaborative fashion. Nowadays, users around the world are taking advantage of social media as one of their key components of communication, and they rely on social media for news and information. The large number of social media users provides a unique opportunity for researchers to explore user modeling. There exist many applications across a wide array of fields such as marketing, law enforcement, and targeted advertising, which benefit from reliable approaches of user modeling. The traditional approaches of gathering data from users to build a user model by directly asking them to fill out questionnaires is time-consuming and impractical for online users. Users are continuously generating content about themselves, their lifestyle, likes/dislikes, and preferences on social media platforms. This user-generated content (UGC) and social ties among users and the platform itself contain a rich amount of data about users. In this thesis, we address user modeling by processing user data available on social media platforms. We leverage both UGC and user social relational content to automatically infer user attributes, such as age, gender and personality traits. The thesis has five main contributions. First, to model social media users based on their UGC, we propose a comparative analysis of state-of-the-art computational personality recognition machine learning methods on a varied set of social media benchmark datasets. In addition to UGC, we model users given their social relational content such as their friendship connections. Our second contribution is a novel graph mining technique that dynamically adapts to the underlying characteristics of the connections of the network to infer the profile of users in ...

Contributors:

De Cock, Martine ; Moens, Marie-Francine; U0012108; ; De Cock, Martine;

Year of Publication:

2017-06-27

Document Type:

Thesis ; TH ; doctoral_thesis ; 467974;Thesis ; [Doctoral and postdoctoral thesis]

Language:

DDC:

004 Data processing & computer science (computed)

Rights:

467974;intranet

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