Six Degrees of “Who Cares?”

Grannis, Rick

doi:10.1086/649059

Cited by 30 publications

(21 citation statements)

References 26 publications

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“…Though this paper focuses on friendship network data, the logic of the "unfriending" problem applies to estimates of peer effects in any longitudinal network data in which homophily plays a significant role in tie dissolution. For instance, Grannis (2010) Going forward, more research is clearly needed on models of peer influence in observational data. For instance, the actor-based model of Steglich, Snijders and Pearson (2010) attempts to account for many of the concerns described above.…”

Section: Resultsmentioning

confidence: 99%

The “unfriending” problem: The consequences of homophily in friendship retention for causal estimates of social influence

2011

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An increasing number of scholars are using longitudinal social network data to try to obtain estimates of peer or social influence effects. These data may provide additional statistical leverage, but they can introduce new inferential problems. In particular, while the confounding effects of homophily in friendship formation are widely appreciated, homophily in friendship retention may also confound causal estimates of social influence in longitudinal network data. We provide evidence for this claim in a Monte Carlo analysis of the statistical model used by Christakis, Fowler, and their colleagues in numerous articles estimating "contagion" effects in social networks. Our results indicate that homophily in friendship retention induces significant upward bias and decreased coverage levels in the Christakis and Fowler model if there is non-negligible friendship attrition over time.We thank Nicholas Christakis and James Fowler for generously sharing the simulation code adapted in this paper and for helpful comments and clarifications. We are also grateful two anonymous reviewers for helpful suggestions and feedback.

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Section: Resultsmentioning

confidence: 99%

The “unfriending” problem: The consequences of homophily in friendship retention for causal estimates of social influence

2011

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“…Includ-ing stale user-user interactions in the network mistakenly creates an inaccurate portrayal of the current state of the system; this is typically referred to as the "unfriending problem" [26]. Not only will network statistics such as the number of nodes, average degree, maximum degree and proportion of nodes in the giant component be artificially inflated due to superfluous, no-longer-active links [26,33], but the degree distribution will also be distorted. Kwak et al [15] found that the degree distribution for a Twitter follower network deviated from a power law distribution due to an overabundance of high degree nodes resulting from an accumulation of "dead-weight" in the network.…”

Section: Introductionmentioning

confidence: 99%

Twitter reciprocal reply networks exhibit assortativity with respect to happiness

Bliss

Kloumann

Harris

et al. 2012

Journal of Computational Science

124

102

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The advent of social media has provided an extraordinary, if imperfect, big data window into the form and evolution of social networks. Based on nearly 40 million message pairs posted to Twitter between September 2008 and February 2009, we construct and examine the revealed social network structure and dynamics over the time scales of days, weeks, and months. At the level of user behavior, we employ our recently developed hedonometric analysis methods to investigate patterns of sentiment expression. We find users average happiness scores to be positively and significantly correlated with those of users one, two, and three links away. We strengthen our analysis by proposing and using a null model to test the effect of network topology on the assortativity of happiness. We also find evidence that more well connected users write happier status updates, with a transition occurring around Dunbar's number. More generally, our work provides evidence of a social sub-network structure within Twitter and raises several methodological points of interest with regard to social network reconstructions.

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“…(Note 18 Although it is important to note that under no truncation the researcher response is not varied, as there is nothing to try and "fill in." that there are also analytical solutions showing if there is a giant component in the network; Grannis, 2010.) The distance results are more heterogeneous, dependent on both the level of skew in the network and the level of truncation in the survey.…”

Section: Results Part 1: Researcher Does Nothing In Response To Degrementioning

confidence: 99%

Global Network Inference from Ego Network Samples: Testing a Simulation Approach

Smith

2015

The Journal of Mathematical Sociology

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Network sampling poses a radical idea: that it is possible to measure global network structure without the full population coverage assumed in most network studies. Network sampling is only useful, however, if a researcher can produce accurate global network estimates. This article explores the practicality of making network inference, focusing on the approach introduced in Smith (2012). The method uses sampled ego network data and simulation techniques to make inference about the global features of the true, unknown network. The validity check here includes more difficult scenarios than previous tests, including those that go beyond the initial scope conditions of the method. I examine networks with a skewed degree distribution and surveys that limit the number of social ties a respondent can list. For each network/survey combination, I take a random ego network sample, run the simulation method, and compare the results to the true values (using measures of connectivity and cohesion). I also test the method on local measures of network structure. The results, on the whole, are encouraging. The method produces good estimates even in cases where the degree distribution is skewed and the survey is strongly restricted. I also find that is it better to not truncate the survey if possible. If the survey must be restricted, the researcher would do well to infer the missing data, rather than use the raw data naively.

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Six Degrees of “Who Cares?”

Cited by 30 publications

References 26 publications

The “unfriending” problem: The consequences of homophily in friendship retention for causal estimates of social influence

The “unfriending” problem: The consequences of homophily in friendship retention for causal estimates of social influence

Twitter reciprocal reply networks exhibit assortativity with respect to happiness

Global Network Inference from Ego Network Samples: Testing a Simulation Approach

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