2026-09-18T20:13:26Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/18285 2023-07-10 col_2142_10761 col_2142_5131 com_2142_10755 com_2142_234 com_2142_5130

Karahalios, Karrie G. Karahalios, Karrie G. Bailey, Brian P. Zhai, ChengXiang Sandvig, Christian E. Terveen, Loren Grudin, Jonathan Gilbert, Eric E. 2011-01-14T22:44:48Z 2011-01-14T22:44:48Z 2011-01-14T22:44:48Z 2010-12 Relationships make social media social. But, not all relationships are created equal. We have colleagues with whom we correspond intensely, but not deeply; we have childhood friends we consider close, even if we fell out of touch. Social media, however, treats everybody the same: someone is either a completely trusted friend or a total stranger, with little or nothing in between. In reality, relationships fall everywhere along this spectrum, a topic social science has investigated for decades under the name tie strength, a term for the strength of a relationship between two people. Despite many compelling findings along this line of research, social media does not incorporate tie strength or its lessons. Neither does most research on large-scale social phenomena. In social network analyses, a link either exists or not. Relationships have few properties of their own. Simply put, we do not understand a basic property of relationships expressed online. This dissertation addresses this problem, merging the theories behind tie strength with the data from social media. I show how to reconstruct tie strength from digital traces in online social media, and how to apply it as a tool in design and analysis. Specifically, this dissertation makes three contributions. First, it offers a rich, high-accuracy and general way to reconstruct tie strength from digital traces, traces like recency and a message’s emotional content. For example, the model can split users into strong and weak ties with nearly 89% accuracy. I argue that it also offers us a chance to rethink many of social media’s most fundamental design elements. Next, I showcase an example of how we can redesign social media using tie strength: a Twitter application open to anyone on the internet which puts tie strength at the heart of its design. Through this application, called We Meddle, I show that the tie strength model generalizes to a new online community, and that it can solve real people’s practical problems with social media. Finally, I demonstrate that modeling tie strength is an important new tool for analyzing large-scale social phenomena. Specifically, I show that real-life diffusion in online networks depends on tie strength (i.e., it depends on social relationships). As a body of work, diffusion studies make a big simplifying assumption: simple stochastic rules govern person-to-person transmission. How does a disease spread? With constant probability. How does a chain letter diffuse? As a branching process. I present a case where this simplifying assumption does not hold. The results challenge the macroscopic diffusion properties in today’s literature, and they hint at a nest of complexity below a placid stochastic surface. It may be fair to see this dissertation as linking the online to the offline; that is, it connects the traces we leave in social media to how we feel about relationships in real life. Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2010-10-01T15:08:58Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 2 Gilbert_Eric.zip: 14663757 bytes, checksum: bca070ed6af54b032d538372f43bf1b9 (MD5) Gilbert_Eric.pdf: 13869243 bytes, checksum: e42479e49a27583574703005c8de9559 (MD5) Made available in DSpace on 2011-01-14T22:44:48Z (GMT). No. of bitstreams: 3 Gilbert_Eric.pdf: 13821459 bytes, checksum: b8bc79c564cf776fc4bdc4029aff7654 (MD5) license.txt: 4062 bytes, checksum: e2d301beaa20dcd857350d2a17b0ace0 (MD5) Gilbert_Eric.zip: 14663757 bytes, checksum: bca070ed6af54b032d538372f43bf1b9 (MD5) http://hdl.handle.net/2142/18285 en Copyright 2010 Eric Edmund Gilbert tie strength social media social networks, computer-mediated communication collapsed contexts streams diffusion Computing tie strength Computer Science 1434 Computer Science 0112 University of Illinois at Urbana-Champaign Dissertation Ph.D. PHD:Computer Science -UIUC 10KS0112PHD