Ossi Karkulahti defends his PhD thesis on Understanding Social Media through Large Volume Measurements

30.9.2019
On Wednesday the 9th of October 2019, M.Sc. Ossi Karkulahti will defend his doctoral thesis on Understanding Social Media through Large Volume Measurements. The thesis is a part of research done in the Department of Computer Science and in the Collaborative Networking research group at the University of Helsinki.

M.Sc. Ossi Karkulahti defends his doctoral thesis Understanding Social Media through Large Volume Measurements on Wednesday the 9th of October 2019 at 12 o'clock noon in the University of Helsinki Porthania building, Auditorium PII (Yliopistonkatu 3, 1st floor). His opponent is Associate Professor Yang Chen (Fudan University, China) and custos Professor Jussi Kangasharju (University of Helsinki). The defence will be held in English.

Understanding Social Media through Large Volume Measurements

The amount of user-generated web content has grown drastically in the past 15 years and many social media services are exceedingly popular nowadays. In this thesis we study social media content creation and consumption through large volume measurements of three prominent social media services, namely Twitter, YouTube, and Wikipedia. Common to the services is that they have millions of users, they are free to use, and the users of the services can both create and consume content.

The motivation behind this thesis is to examine how users create and consume social media content, investigate why social media services are as popular as they are, what drives people to contribute on them, and see if it is possible to model the conduct of the users. We study how various aspects of social media content be that for example its creation and consumption or its popularity can be measured, characterized, and linked to real world occurrences.

We have gathered more than 20 million tweets, metadata of more than 10 million YouTube videos and a complete six-year page view history of 19 different Wikipedia language editions. We show, for example, daily and hourly patterns for the content creation and consumption, content popularity distributions, characteristics of popular content, and user statistics.

We will also compare social media with traditional news services and show the interaction with social media, news, and stock prices. In addition, we combine natural language processing with social media analysis, and discover interesting correlations between news and social media content.

Moreover, we discuss the importance of correct measurement methods and show the effects of different sampling methods using YouTube measurements as an example.

Availability of the dissertation

An electronic version of the doctoral dissertation is available on the e-thesis site of the University of Helsinki at http://urn.fi/URN:ISBN:978-951-51-5509-2.

Printed copies will be available on request from Ossi Karkulahti: ossi.karkulahti@helsinki.fi