social_influence   125

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[1611.06713] Is Gun Violence Contagious?
"Existing theories of gun violence predict stable spatial concentrations and contagious diffusion of gun violence into surrounding areas. Recent empirical studies have reported confirmatory evidence of such spatiotemporal diffusion of gun violence. However, existing tests cannot readily distinguish spatiotemporal clustering from spatiotemporal diffusion. This leaves as an open question whether gun violence actually is contagious or merely clusters in space and time. Compounding this problem, gun violence is subject to considerable measurement error with many nonfatal shootings going unreported to police. Using point process data from an acoustical gunshot locator system and a combination of Bayesian spatiotemporal point process modeling and space/time interaction tests, this paper demonstrates that contemporary urban gun violence does diffuse, but only slightly, suggesting that a disease model for infectious spread of gun violence is a poor fit for the geographically stable and temporally stochastic process observed."
in_NB  crime  spatio-temporal_statistics  point_processes  social_influence  flaxman.seth 
22 days ago by cshalizi
Social Learning Strategies: Bridge-Building between Fields: Trends in Cognitive Sciences
"While social learning is widespread, indiscriminate copying of others is rarely beneficial. Theory suggests that individuals should be selective in what, when, and whom they copy, by following ‘social learning strategies’ (SLSs). The SLS concept has stimulated extensive experimental work, integrated theory, and empirical findings, and created impetus to the social learning and cultural evolution fields. However, the SLS concept needs updating to accommodate recent findings that individuals switch between strategies flexibly, that multiple strategies are deployed simultaneously, and that there is no one-to-one correspondence between psychological heuristics deployed and resulting population-level patterns. The field would also benefit from the simultaneous study of mechanism and function. SLSs provide a useful vehicle for bridge-building between cognitive psychology, neuroscience, and evolutionary biology."
to:NB  social_learning  cultural_transmission  cultural_evolution  cognitive_science  social_influence  re:do-institutions-evolve 
4 weeks ago by cshalizi
Niezink , Snijders : Co-evolution of social networks and continuous actor attributes
"Social networks and the attributes of the actors in these networks are not static; they may develop interdependently over time. The stochastic actor-oriented model allows for statistical inference on the mechanisms driving this co-evolution process. In earlier versions of this model, dynamic actor attributes are assumed to be measured on an ordinal categorical scale. We present an extension of the stochastic actor-oriented model that does away with this restriction using a stochastic differential equation to model the evolution of continuous actor attributes. We estimate the parameters by a procedure based on the method of moments. The proposed method is applied to study the dynamics of a friendship network among the students at an Australian high school. In particular, we model the relationship between friendship and obesity, focusing on body mass index as a continuous co-evolving attribute."
to:NB  social_networks  network_data_analysis  social_influence  stochastic_differential_equations  statistics  have_read  heard_the_talk  to_teach:baby-nets  niezink.nynke  snijders.tom 
11 weeks ago by cshalizi
Infectious Disease Modeling of Social Contagion in Networks
"It has recently been suggested that certain, particular types of latent homophily, in which an unobservable trait influences both which friends one chooses and current and future behavior, may be impossible to distinguish from contagion in observational studies and hence may bias estimates of contagion and homophily [50]. The circumstances under which this is likely to be a serious source of bias (e.g., whether people, empirically, behave in these sorts of ways), and what (if anything) might be done about it (absent experimental data of the kind that some new networks studies are providing [22]) merits further study. Observational data invariably pose problems for causal inference, and require one set of assumptions or another to analyze; the plausibility of these assumptions (even of standard ones that are widely used) warrants constant review.
"The SISa model as presented here assumes that all individuals have the same probability of changing state (though not everyone will actually change state within their lifetime). It is clearly possible, however, that there is heterogeneity between individuals in these rates. We do not have sufficient data on obesity in the Framingham dataset to explore this issue, which would require observing numerous transitions between states for each individual. Exploring individual differences in acquisition rate empirically is a very interesting topic for future research, as is extending the theoretical framework we introduce to take into account individual differences."

--- For "suggested", read "proved"; the second paragraph amounts to saying "Let's just agree to ignore this".
to:NB  contagion  epidemic_models  social_influence  re:homophily_and_confounding  christakis.nicholas  via:rvenkat  have_skimmed 
12 weeks ago by cshalizi
Behavioral Communities and the Atomic Structure of Networks by Matthew O. Jackson, Evan Storms :: SSRN
"We develop a theory of 'behavioral communities' and the 'atomic structure' of networks. We define atoms to be groups of agents whose behaviors always match each other in a set of coordination games played on the network. This provides a microfoundation for a method of detecting communities in social and economic networks. We provide theoretical results characterizing such behavior-based communities and atomic structures and discussing their properties in large random networks. We also provide an algorithm for identifying behavioral communities. We discuss applications including: a method of estimating underlying preferences by observing behavioral conventions in data, and optimally seeding diffusion processes when there are peer interactions and homophily. We illustrate the techniques with applications to high school friendship networks and rural village networks."
to:NB  to_read  diffusion_of_innovations  community_discovery  networks  network_data_analysis  social_influence  jackson.matthew_o.  re:do-institutions-evolve  via:rvenkat 
march 2018 by cshalizi
How Not to Cover Mass Shootings - WSJ
"The often sensationalistic media attention given to perpetrators is central to why massacres are happening more."
have_read  track_down_references  crime  social_influence  to:NB 
january 2018 by cshalizi
Social network fragmentation and community health
"Community health interventions often seek to intentionally destroy paths between individuals to prevent the spread of infectious diseases. Immunizing individuals through direct vaccination or the provision of health education prevents pathogen transmission and the propagation of misinformation concerning medical treatments. However, it remains an open question whether network-based strategies should be used in place of conventional field approaches to target individuals for medical treatment in low-income countries. We collected complete friendship and health advice networks in 17 rural villages of Mayuge District, Uganda. Here we show that acquaintance algorithms, i.e., selecting neighbors of randomly selected nodes, were systematically more efficient in fragmenting all networks than targeting well-established community roles, i.e., health workers, village government members, and schoolteachers. Additionally, community roles were not good proxy indicators of physical proximity to other households or connections to many sick people. We also show that acquaintance algorithms were effective in offsetting potential noncompliance with deworming treatments for 16,357 individuals during mass drug administration (MDA). Health advice networks were destroyed more easily than friendship networks. Only an average of 32% of nodes were removed from health advice networks to reduce the percentage of nodes at risk for refusing treatment in MDA to below 25%. Treatment compliance of at least 75% is needed in MDA to control human morbidity attributable to parasitic worms and progress toward elimination. Our findings point toward the potential use of network-based approaches as an alternative to role-based strategies for targeting individuals in rural health interventions."
to:NB  social_networks  epidemiology_of_representations  social_influence  networks  re:do-institutions-evolve 
september 2017 by cshalizi
Reach and speed of judgment propagation in the laboratory
"In recent years, a large body of research has demonstrated that judgments and behaviors can propagate from person to person. Phenomena as diverse as political mobilization, health practices, altruism, and emotional states exhibit similar dynamics of social contagion. The precise mechanisms of judgment propagation are not well understood, however, because it is difficult to control for confounding factors such as homophily or dynamic network structures. We introduce an experimental design that renders possible the stringent study of judgment propagation. In this design, experimental chains of individuals can revise their initial judgment in a visual perception task after observing a predecessor’s judgment. The positioning of a very good performer at the top of a chain created a performance gap, which triggered waves of judgment propagation down the chain. We evaluated the dynamics of judgment propagation experimentally. Despite strong social influence within pairs of individuals, the reach of judgment propagation across a chain rarely exceeded a social distance of three to four degrees of separation. Furthermore, computer simulations showed that the speed of judgment propagation decayed exponentially with the social distance from the source. We show that information distortion and the overweighting of other people’s errors are two individual-level mechanisms hindering judgment propagation at the scale of the chain. Our results contribute to the understanding of social-contagion processes, and our experimental method offers numerous new opportunities to study judgment propagation in the laboratory."
to:NB  social_influence  experimental_psychology  experimental_sociology  re:homophily_and_confounding 
august 2017 by cshalizi
The Economic Consequences of Social-Network Structure
"We survey the literature on the economic consequences of the structure of social networks. We develop a taxonomy of "macro" and "micro" characteristics of social-interaction networks and discuss both the theoretical and empirical findings concerning the role of those characteristics in determining learning, diffusion, decisions, and resulting behaviors. We also discuss the challenges of accounting for the endogeneity of networks in assessing the relationship between the patterns of interactions and behaviors."
to:NB  economics  social_networks  social_influence  re:do-institutions-evolve  jackson.matthew_o. 
august 2017 by cshalizi
Murder by Structure: Dominance Relations and the Social Structure of Gang Homicide: American Journal of Sociology: Vol 115, No 1
"Most sociological theories consider murder an outcome of the differential distribution of individual, neighborhood, or social characteristics. And while such studies explain variation in aggregate homicide rates, they do not explain the social order of murder, that is, who kills whom, when, where, and for what reason. This article argues that gang murder is best understood not by searching for its individual determinants but by examining the social networks of action and reaction that create it. In short, the social structure of gang murder is defined by the manner in which social networks are constructed and by people's placement in them. The author uses a network approach and incident‐level homicide records to recreate and analyze the structure of gang murders in Chicago. Findings demonstrate that individual murders between gangs create an institutionalized network of group conflict, net of any individual's participation or motive. Within this network, murders spread through an epidemic‐like process of social contagion as gangs evaluate the highly visible actions of others in their local networks and negotiate dominance considerations that arise during violent incidents."

--- Uses the same old methods for detecting contagion as Christakis-Fowler; perhaps more plausible here?
to:NB  have_read  social_networks  violence  contagion  social_influence  sociology  re:network_differences  honor  re:homophily_and_confounding 
august 2017 by cshalizi
[1706.08440] Challenges to estimating contagion effects from observational data
"A growing body of literature attempts to learn about contagion using observational (i.e. non-experimental) data collected from a single social network. While the conclusions of these studies may be correct, the methods rely on assumptions that are likely--and sometimes guaranteed to be--false, and therefore the evidence for the conclusions is often weaker than it seems. Developing methods that do not need to rely on implausible assumptions is an incredibly challenging and important open problem in statistics. Appropriate methods don't (yet!) exist, so researchers hoping to learn about contagion from observational social network data are sometimes faced with a dilemma: they can abandon their research program, or they can use inappropriate methods. This chapter will focus on the challenges and the open problems and will not weigh in on that dilemma, except to mention here that the most responsible way to use any statistical method, especially when it is well-known that the assumptions on which it rests do not hold, is with a healthy dose of skepticism, with honest acknowledgment and deep understanding of the limitations, and with copious caveats about how to interpret the results."
to:NB  have_read  ogburn.elizabeth  contagion  homophily  social_influence  social_networks  causal_inference  statistics  re:homophily_and_confounding 
july 2017 by cshalizi
Estimating peer effects in networks with peer encouragement designs
"Peer effects, in which the behavior of an individual is affected by the behavior of their peers, are central to social science. Because peer effects are often confounded with homophily and common external causes, recent work has used randomized experiments to estimate effects of specific peer behaviors. These experiments have often relied on the experimenter being able to randomly modulate mechanisms by which peer behavior is transmitted to a focal individual. We describe experimental designs that instead randomly assign individuals’ peers to encouragements to behaviors that directly affect those individuals. We illustrate this method with a large peer encouragement design on Facebook for estimating the effects of receiving feedback from peers on posts shared by focal individuals. We find evidence for substantial effects of receiving marginal feedback on multiple behaviors, including giving feedback to others and continued posting. These findings provide experimental evidence for the role of behaviors directed at specific individuals in the adoption and continued use of communication technologies. In comparison, observational estimates differ substantially, both underestimating and overestimating effects, suggesting that researchers and policy makers should be cautious in relying on them."
to:NB  experimental_design  experimental_sociology  social_influence  social_networks  causal_inference  statistics  eckles.dean  bakshy.eytan  to_read  re:do_not_adjust_your_receiver 
july 2016 by cshalizi
[1606.09082] Formation of homophily in academic performance: students prefer to change their friends rather than performance
"Homophily, the tendency of individuals to associate with others who share similar traits, has been identified as a major driving force in the formation and evolution of social ties. In many cases, it is not clear if homophily is the result of a socialization process, where individuals change their traits according to the dominance of that trait in their local social networks, or if it results from a selection process, in which individuals reshape their social networks so that their traits match those in the new environment. Here we demonstrate the detailed temporal formation of strong homophily in academic achievements of high school and university students. We analyze a unique dataset that contains information about the detailed time evolution of a friendship network of 6,000 students across 42 months. Combining the evolving social network data with the time series of the academic performance (GPA) of individual students, we show that academic homophily is a result of selection: students prefer to gradually reorganize their social networks according to their performance levels, rather than adapting their performance to the level of their local group. We find no signs for a pull effect, where a social environment of good performers motivates bad students to improve their performance. We are able to understand the underlying dynamics of grades and networks with a simple model. The lack of a social pull effect in classical educational settings could have important implications for the understanding of the observed persistence of segregation, inequality and social immobility in societies."

--- On a quick skim, they do not actually address the confounding problem (and I wonder if they have actually read their reference [41]).
to:NB  to_read  social_networks  homophily  social_influence  education  re:homophily_and_confounding  to_be_shot_after_a_fair_trial 
june 2016 by cshalizi
PLOS ONE: Trickle-Down Preferences: Preferential Conformity to High Status Peers in Fashion Choices
On first skim, they don't really seem to consider that women who move from low to high status locations are probably _already different_ from those who don't...
I can't believe I'm writing this, but this might really be a job for propensity-score matching.
to:NB  to_be_shot_after_a_fair_trial  social_influence  economics  shoes  re:homophily_and_confounding  to_teach:undergrad-ADA 
may 2016 by cshalizi
Identifying Formal and Informal Influence in Technology Adoption with Network Externalities
"Firms introducing network technologies (whose benefits depend on who installs the technology) need to understand which user characteristics confer the greatest network benefits on other potential adopters. To examine which adopter characteristics matter, I use the introduction of a video-messaging technology in an investment bank. I use data on its 2,118 employees, their adoption decisions, and their 2.4 million subsequent calls. The video-messaging technology can also be used to watch TV. Exogenous shocks to the benefits of watching TV are used to identify the causal (network) externality of one individual user's adoption on others' adoption decisions. I allow this network externality to vary in size with a variety of measures of informal and formal influence. I find that adoption by either managers or workers in “boundary spanner” positions has a large impact on the adoption decisions of employees who wish to communicate with them. Adoption by ordinary workers has a negligible impact. This suggests that firms should target those who derive their informal influence from occupying key boundary-spanning positions in communication networks, in addition to those with sources of formal influence, when launching a new network technology."
to:NB  causal_inference  instrumental_variables  diffusion_of_innovations  statistics  social_influence  social_networks  re:homophily_and_confounding  to_be_shot_after_a_fair_trial  via:mcfowland 
march 2016 by cshalizi
[1601.00992] Models, Methods and Network Topology: Experimental Design for the Study of Interference
"How should a network experiment be designed to achieve high statistical power? Experimental treatments on networks may spread. Randomizing assignment of treatment to nodes enhances learning about the counterfactual causal effects of a social network experiment and also requires new methodology (Aronow and Samii, 2013; Bowers et al., 2013; Toulis and Kao, 2013, ex.). In this paper we show that the way in which a treatment propagates across a social network affects the statistical power of an experimental design. As such, prior information regarding treatment propagation should be incorporated into the experimental design. Our findings run against standard advice in circumstances where units are presumed to be independent: information about treatment effects is not maximized when we assign half the units to treatment and half to control. We also show that statistical power depends on the extent to which the network degree of nodes is correlated with treatment assignment probability. We recommend that researchers think carefully about the underlying treatment propagation model motivating their study in designing an experiment on a network."
to:NB  to_read  network_data_analysis  social_influence  experimental_design  statistics 
february 2016 by cshalizi
On the Directionality Test of Peer Effects in Social Networks
"One interesting idea in social network analysis is the directionality test that utilizes the directions of social ties to help identify peer effects. The null hypothesis of the test is that if contextual factors are the only force that affects peer outcomes, the estimated peer effects should not differ, if the directions of social ties are reversed. In this article, I statistically formalize this test and investigate its properties under various scenarios. In particular, I point out the validity of the test is contingent on the presence of peer selection, sampling error, and simultaneity bias. I also outline several methods that can help provide causal estimates of peer effects in social networks."

- Last tag applies mostly to the last sentence.
to:NB  to_read  network_data_analysis  causal_inference  social_networks  homophily  social_influence  contagion  re:homophily_and_confounding  to_be_shot_after_a_fair_trial 
may 2015 by cshalizi
Ogburn , VanderWeele : Causal Diagrams for Interference
"The term “interference” has been used to describe any setting in which one subject’s exposure may affect another subject’s outcome. We use causal diagrams to distinguish among three causal mechanisms that give rise to interference. The first causal mechanism by which interference can operate is a direct causal effect of one individual’s treatment on another individual’s outcome; we call this direct interference. Interference by contagion is present when one individual’s outcome may affect the outcomes of other individuals with whom he comes into contact. Then giving treatment to the first individual could have an indirect effect on others through the treated individual’s outcome. The third pathway by which interference may operate is allocational interference. Treatment in this case allocates individuals to groups; through interactions within a group, individuals may affect one another’s outcomes in any number of ways. In many settings, more than one type of interference will be present simultaneously. The causal effects of interest differ according to which types of interference are present, as do the conditions under which causal effects are identifiable. Using causal diagrams for interference, we describe these differences, give criteria for the identification of important causal effects, and discuss applications to infectious diseases."
to:NB  social_influence  contagion  graphical_models  causal_inference  statistics  ogburn.elizabeth 
may 2015 by cshalizi

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