Measuring Tie Strength in Implicit Social Networks

Given a set of people and a set of events they attend, we address the problem of measuring connectedness or tie strength between each pair of persons given that attendance at mutual events gives an implicit social network between people. We take an axiomatic approach to this problem. Starting from a list of axioms that a measure of tie strength must satisfy, we characterize functions that satisfy all the axioms and show that there is a range of measures that satisfy this characterization. A measure of tie strength induces a ranking on the edges (and on the set of neighbors for every person). We show that for applications where the ranking, and not the absolute value of the tie strength, is the important thing about the measure, the axioms are equivalent to a natural partial order. Also, to settle on a particular measure, we must make a non-obvious decision about extending this partial order to a total order, and that this decision is best left to particular applications. We classify measures found in prior literature according to the axioms that they satisfy. In our experiments, we measure tie strength and the coverage of our axioms in several datasets. Also, for each dataset, we bound the maximum Kendall's Tau divergence (which measures the number of pairwise disagreements between two lists) between all measures that satisfy the axioms using the partial order. This informs us if particular datasets are well behaved where we do not have to worry about which measure to choose, or we have to be careful about the exact choice of measure we make.Comment: 10 page

Computing tie strength

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

An Examination Of Online Social Networks Properties With Tie-Strength

In the past, most researchers focused on the efficacy of tie-strength in various applications for both online and offline social networks. However, how tie-strength can help in the analysis of online social networks was a commonly neglected issue. The massive size and recording properties of online social networks offer the possibility to measure tie-strength objectively. In this study, we examine a social network extracted from a blog network. We then propose a tie-strength measurement and investigate several properties of the network using the tie-strength we defined. We also study how tie-strength plays a role in these properties

- Friends?... Fair enough.

Social network analysis is one of the fields in the social sciences which went through a huge development in the last two decades. With the availability of newer tools and methods, in-depth analysis of huge networks became possible resulting in important results at various fields. Despite this advancement, the strength of a tie – a foundation of this theory – is still a hot topic in SNA. This paper aims to provide another approach to tie strength, which is based on one of the internal properties of agents manifesting in human interactions – fairness. An analytical model of tie strength is introduced focusing on fairness concerns of people towards each other. The model is analyzed and an experimental method is shown to test the model. Also pilot results are introduced.fairness, social network analysis, tie strength

REVISIT THE INFORMATION ADOPTION MODEL BY EXPLORING THE MODERATING ROLE OF TIE STRENGTH: A PERSPECTIVE FROM CONSTRUAL LEVEL THEORY

Previous studies on review information adoption, drawing upon dual process theory, focus on the important roles of two key review-related factors namely argument strength and source credibility, but pay less attention to the social relationships between review sources and recipients. To fill this research gap, based on the construal level theory, we articulate that tie strength moderates the impacts of argument strength and source credibility on content diagnosticity. A survey was conducted to examine the proposed research model and hypotheses and the results showed that the relationship between argument strength and content diagnosticity is stronger when tie strength is weak than when tie strength is strong while the relationship between source credibility is stronger when tie strength is strong than when tie strength is weak. The theoretical and practical implications of the study are also discussed

Time as a limited resource: Communication Strategy in Mobile Phone Networks

We used a large database of 9 billion calls from 20 million mobile users to examine the relationships between aggregated time spent on the phone, personal network size, tie strength and the way in which users distributed their limited time across their network (disparity). Compared to those with smaller networks, those with large networks did not devote proportionally more time to communication and had on average weaker ties (as measured by time spent communicating). Further, there were not substantially different levels of disparity between individuals, in that mobile users tend to distribute their time very unevenly across their network, with a large proportion of calls going to a small number of individuals. Together, these results suggest that there are time constraints which limit tie strength in large personal networks, and that even high levels of mobile communication do not fundamentally alter the disparity of time allocation across networks.Comment: 10 pages, 3 figures. Accepted for publication in Social Network