The Hidden Truths of Self-Presentation, Self-Disclosure, and Deception on Online Dating Profiles: A Call for Change

The way in which individuals seek romantic partners has changed considerably in the past decades, most notably through online dating sites (ODSs). Despite the possibility of misrepresentation amongst client users, such sites continue to grow in popularity because ODSs provide a large pool from which individuals can select and attract potential partners. While much research has been undertaken on ODSs, little empirical research has examined postsecondary students’ use of ODSs. Therefore, this study sought to investigate why postsecondary students have become involved with and how they present themselves on ODSs. The researcher surveyed 20 postsecondary students and conducted in-depth interviews with 2 participants who use ODSs. Although the limited sample prevented results from being generalized, quantitative and qualitative analyses suggest that participants became involved with ODSs for various purposes, such as seeking long-term relationships and/or marriage partners, or simply exploring or visiting ODSs out of curiosity. Findings indicate that ODS users’ physical appearance and/or “attractiveness” is considered the strongest predictor of relationship success. The study discusses how participants’ self-presentation affects outcomes of ODS usage, particularly when negative self-identification and presentation corresponding to factors such as individuals’ weight and age are taken into account.

Community Detection in Multi-Layer Networks

In the scope of the current thesis we review and analyse networks that are formed by nodes with several attributes. We suppose that different layers of communities are embedded in such networks, besides each of the layers is connected with nodes' attributes. For example, examine one of a variety of online social networks: an user participates in a plurality of different groups/communities – schoolfellows, colleagues, clients, etc. We introduce a detection algorithm for the above-mentioned communities. Normally the result of the detection is the community supplemented just by the most dominant attribute, disregarding others. We propose an algorithm that bypasses dominant communities and detects communities which are formed by other nodes' attributes. We also review formation models of the attributed networks and present a Human Communication Network (HCN) model. We introduce a High School Texting Network (HSTN) and examine our methods for that network.

Dynamics in large scale networks

In this thesis we study the properties of two large dynamic networks, the competition network of advertisers on the Google and Bing search engines and the dynamic network of friend relationships among avatars in the massively multiplayer online game (MMOG) Planetside 2. We are particularly interested in removal patterns in these networks. Our main finding is that in both of these networks the nodes which are most commonly removed are minor near isolated nodes. We also investigate the process of merging of two large networks using data captured during the merger of servers of Planetside 2. We found that the original network structures do not really merge but rather they get gradually replaced by newcomers not associated with the original structures. In the final part of the thesis we investigate the concept of motifs in the Barabási-Albert random graph. We establish some bounds on the number of motifs in this graph.