rvenkat + contagion   61

Complex Spreading Phenomena in Social Systems - Influence and Contagion in Real-World Social Networks | Sune Lehmann | Springer
This text is about spreading of information and influence in complex networks. Although previously considered similar and modeled in parallel approaches, there is now experimental evidence that epidemic and social spreading work in subtly different ways. While previously explored through modeling, there is currently an explosion of work on revealing the mechanisms underlying complex contagion based on big data and data-driven approaches.

This volume consists of four parts. Part 1 is an Introduction, providing an accessible summary of the state of the art. Part 2 provides an overview of the central theoretical developments in the field. Part 3 describes the empirical work on observing spreading processes in real-world networks. Finally, Part 4 goes into detail with recent and exciting new developments: dedicated studies designed to measure specific aspects of the spreading processes, often using randomized control trials to isolate the network effect from confounders, such as homophily.

Each contribution is authored by leading experts in the field. This volume, though based on technical selections of the most important results on complex spreading, remains quite accessible to the newly interested. The main benefit to the reader is that the topics are carefully structured to take the novice to the level of expert on the topic of social spreading processes. This book will be of great importance to a wide field: from researchers in physics, computer science, and sociology to professionals in public policy and public health.

https://socialcontagionbook.github.io/
networks  epidemics  contagion  social_networks  teaching
14 days ago by rvenkat
[1803.10637] Objective measures for sentinel surveillance in network epidemiology
The problem of optimizing sentinel surveillance in networks is to find the nodes where an emerging disease outbreak can be discovered early or reliably. Whether the emphasis should be on early or reliable detection depends on the scenario in question. We investigate three objective measures quantifying the performance of nodes in sentinel surveillance the time to detection or extinction, the time to detection, and the frequency of detection. As a basis for the comparison, we use the susceptible-infectious-recovered model (SIR) on static and temporal networks of human contacts. We show that, for some regions of parameter space, the three objective measures can rank the nodes very differently. As opposed to other problems in network epidemiology, we find rather similar results for the static and temporal networks. Furthermore, we do not find one network structure that predicts the objective measures---that depends both on the data set and the SIR parameter .
networks  epidemics  contagion  signal_processing  temporal_networks
11 weeks ago by rvenkat
Infectious Disease Modeling of Social Contagion in Networks
Many behavioral phenomena have been found to spread interpersonally through social networks, in a manner similar to infectious diseases. An important difference between social contagion and traditional infectious diseases, however, is that behavioral phenomena can be acquired by non-social mechanisms as well as through social transmission. We introduce a novel theoretical framework for studying these phenomena (the SISa model) by adapting a classic disease model to include the possibility for ‘automatic’ (or ‘spontaneous’) non-social infection. We provide an example of the use of this framework by examining the spread of obesity in the Framingham Heart Study Network. The interaction assumptions of the model are validated using longitudinal network transmission data. We find that the current rate of becoming obese is 2 per year and increases by 0.5 percentage points for each obese social contact. The rate of recovering from obesity is 4 per year, and does not depend on the number of non-obese contacts. The model predicts a long-term obesity prevalence of approximately 42, and can be used to evaluate the effect of different interventions on steady-state obesity. Model predictions quantitatively reproduce the actual historical time course for the prevalence of obesity. We find that since the 1970s, the rate of recovery from obesity has remained relatively constant, while the rates of both spontaneous infection and transmission have steadily increased over time. This suggests that the obesity epidemic may be driven by increasing rates of becoming obese, both spontaneously and transmissively, rather than by decreasing rates of losing weight. A key feature of the SISa model is its ability to characterize the relative importance of social transmission by quantitatively comparing rates of spontaneous versus contagious infection. It provides a theoretical framework for studying the interpersonal spread of any state that may also arise spontaneously, such as emotions, behaviors, health states, ideas or diseases with reservoirs.

-----------------------

"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".
social_networks  contagion  homophily  simulation  epidemics  networks  teaching
12 weeks ago by rvenkat
SocioPatterns.org
--more datasets for students, in case they are interested in dynamics, especially epidemics on networks. Includes data on temporal networks.
data_sets  contagion  epidemiology  social_networks  networks  teaching
12 weeks ago by rvenkat

-- something about facebook experiments and the magnitude of intervention effects make me suspicious. Maybe, I'm to reading too many of Gelman's posts.
norms  influence  contagion  online_experiments  observational_studies  social_networks  networks  teaching  i_remain_skeptical
march 2018 by rvenkat
Contagion on Networks 2017
-- in case I find computer savvy human geographers and epidemiologists in my class.
contagion  epidemiology  networks  teaching
march 2018 by rvenkat
The science of fake news | Science
The rise of fake news highlights the erosion of long-standing institutional bulwarks against misinformation in the internet age. Concern over the problem is global. However, much remains unknown regarding the vulnerabilities of individuals, institutions, and society to manipulations by malicious actors. A new system of safeguards is needed. Below, we discuss extant social and computer science research regarding belief in fake news and the mechanisms by which it spreads. Fake news has a long history, but we focus on unanswered scientific questions raised by the proliferation of its most recent, politically oriented incarnation. Beyond selected references in the text, suggested further reading can be found in the supplementary materials.
review  report  misinformation  disinformation  contagion  journalism  news_media  networks  dmce  teaching
march 2018 by rvenkat
The spread of true and false news online | Science
We investigated the differential diffusion of all of the verified true and false news stories distributed on Twitter from 2006 to 2017. The data comprise ~126,000 stories tweeted by ~3 million people more than 4.5 million times. We classified news as true or false using information from six independent fact-checking organizations that exhibited 95 to 98% agreement on the classifications. Falsehood diffused significantly farther, faster, deeper, and more broadly than the truth in all categories of information, and the effects were more pronounced for false political news than for false news about terrorism, natural disasters, science, urban legends, or financial information. We found that false news was more novel than true news, which suggests that people were more likely to share novel information. Whereas false stories inspired fear, disgust, and surprise in replies, true stories inspired anticipation, sadness, joy, and trust. Contrary to conventional wisdom, robots accelerated the spread of true and false news at the same rate, implying that false news spreads more than the truth because humans, not robots, are more likely to spread it.
misinformation  contagion  social_media  social_networks  networks  teaching  via:henryfarrell
march 2018 by rvenkat
Centola, D.: How Behavior Spreads: The Science of Complex Contagions (Hardcover and eBook) | Princeton University Press
New social movements, technologies, and public-health initiatives often struggle to take off, yet many diseases disperse rapidly without issue. Can the lessons learned from the viral diffusion of diseases be used to improve the spread of beneficial behaviors and innovations? In How Behavior Spreads, Damon Centola presents over a decade of original research examining how changes in societal behavior--in voting, health, technology, and finance—occur and the ways social networks can be used to influence how they propagate. Centola's startling findings show that the same conditions accelerating the viral expansion of an epidemic unexpectedly inhibit the spread of behaviors.

While it is commonly believed that "weak ties"—long-distance connections linking acquaintances—lead to the quicker spread of behaviors, in fact the exact opposite holds true. Centola demonstrates how the most well-known, intuitive ideas about social networks have caused past diffusion efforts to fail, and how such efforts might succeed in the future. Pioneering the use of Web-based methods to understand how changes in people's social networks alter their behaviors, Centola illustrates the ways in which these insights can be applied to solve countless problems of organizational change, cultural evolution, and social innovation. His findings offer important lessons for public health workers, entrepreneurs, and activists looking to harness networks for social change.

Practical and informative, How Behavior Spreads is a must-read for anyone interested in how the theory of social networks can transform our world.

-- While the ideas are simple and neat, his *complex* contagion is not *complex* enough to revolutionize networked interventions. A review article would have sufficed. Some of the mechanisms observed in spread of misinformation and disinformation suggest a completely different model.
book  networks  contagion  i_remain_skeptical
march 2018 by rvenkat
Diffusion in Networks and the Virtue of Burstiness by Mohammad Akbarpour, Matthew O. Jackson :: SSRN
Whether an idea, information, disease, or innovation diffuses throughout a society depends not only on the structure of the network of interactions, but also on the timing of those interactions. Recent studies have shown that diffusion can fail on a network in which people are only active in “bursts”, active for a while and then silent for a while, but diffusion could succeed on the same network if people were active in a more random Poisson manner. Those studies generally consider models in which nodes are active according to the same random timing process and then ask which timing is optimal. In reality, people differ widely in their activity patterns – some are bursty and others are not. We model diffusion on networks in which agents differ in their activity patterns. We show that bursty behavior does not always hurt the diffusion, and in fact having some (but not all) of the population be bursty significantly helps diffusion. We prove that maximizing diffusion requires heterogeneous activity patterns across agents, and the overall maximizing pattern of agents’ activity times does not involve any any Poisson behavior.
networks  contagion  dynamics  stochastic_process  social_networks  diffusion  teaching  matthew.jackson
february 2018 by rvenkat
Mapping the anti-vaccination movement on Facebook: Information, Communication & Society: Vol 0, No 0
Over the past decade, anti-vaccination rhetoric has become part of the mainstream discourse regarding the public health practice of childhood vaccination. These utilise social media to foster online spaces that strengthen and popularise anti-vaccination discourses. In this paper, we examine the characteristics of and the discourses present within six popular anti-vaccination Facebook pages. We examine these large-scale datasets using a range of methods, including social network analysis, gender prediction using historical census data, and generative statistical models for topic analysis (Latent Dirichlet allocation). We find that present-day discourses centre around moral outrage and structural oppression by institutional government and the media, suggesting a strong logic of ‘conspiracy-style’ beliefs and thinking. Furthermore, anti-vaccination pages on Facebook reflect a highly ‘feminised’ movement ‒ the vast majority of participants are women. Although anti-vaccination networks on Facebook are large and global in scope, the comment activity sub-networks appear to be ‘small world’. This suggests that social media may have a role in spreading anti-vaccination ideas and making the movement durable on a global scale.
conspiracy_theories  social_media  social_networks  contagion  sentiment_analysis  topic_model  networks  teaching  via:zeynep
december 2017 by rvenkat
Critical dynamics in population vaccinating behavior
Vaccine refusal can lead to renewed outbreaks of previously eliminated diseases and even delay global eradication. Vaccinating decisions exemplify a complex, coupled system where vaccinating behavior and disease dynamics influence one another. Such systems often exhibit critical phenomena—special dynamics close to a tipping point leading to a new dynamical regime. For instance, critical slowing down (declining rate of recovery from small perturbations) may emerge as a tipping point is approached. Here, we collected and geocoded tweets about measles–mumps–rubella vaccine and classified their sentiment using machine-learning algorithms. We also extracted data on measles-related Google searches. We find critical slowing down in the data at the level of California and the United States in the years before and after the 2014–2015 Disneyland, California measles outbreak. Critical slowing down starts growing appreciably several years before the Disneyland outbreak as vaccine uptake declines and the population approaches the tipping point. However, due to the adaptive nature of coupled behavior–disease systems, the population responds to the outbreak by moving away from the tipping point, causing “critical speeding up” whereby resilience to perturbations increases. A mathematical model of measles transmission and vaccine sentiment predicts the same qualitative patterns in the neighborhood of a tipping point to greatly reduced vaccine uptake and large epidemics. These results support the hypothesis that population vaccinating behavior near the disease elimination threshold is a critical phenomenon. Developing new analytical tools to detect these patterns in digital social data might help us identify populations at heightened risk of widespread vaccine refusal.
epidemiology  sentiment_analysis  contagion  networked_life  ?  dynamics  phase_transition
december 2017 by rvenkat
How 550 Facebook Users Spread Britain First Content To Hundreds Of Thousands Of People
--C for effort!, interesting mostly because of the topic. The analysis as such seems sub-standard given how it has been described. Need better data journalism.
buzzfeed  social_media  misinformation  disinformation  contagion  social_networks  britain  brexit
november 2017 by rvenkat
[1711.05701] Social Complex Contagion in Music Listenership: A Natural Experiment with 1.3 Million Participants
Can live music events generate complex contagion in music streaming? This paper finds evidence in the affirmative, but only for the most popular artists. We generate a novel dataset from Last.fm, a music tracking website, to analyse the listenership history of 1.3 million users over a two-month time horizon. We use daily play counts along with event attendance data to run a regression discontinuity analysis in order to show the causal impact of concert attendance on music listenership among attendees and their friends network. First, we show that attending a music artist's live concert increases that artist's listenership among the attendees of the concert by approximately 1 song per day per attendee (p-value<0.001). Moreover, we show that this effect is contagious and can spread to users who did not attend the event. However, the extent of contagion depends on the type of artist. We only observe contagious increases in listenership for well-established, popular artists (.06 more daily plays per friend of an attendee [p<0.001]), while the effect is absent for emerging stars. We also show that the contagion effect size increases monotonically with the number of friends who have attended the live event.
contagion  homophily  ?  causal_inference  natural_experiment  epidemics  networks  teaching  via:strogatz
november 2017 by rvenkat
--idea of virality may be network dependent
-- an exercise in constructing multilayer networks, perhaps?
-- different networks, different kinds of contagion
-- define meme seeding
-- the article talks about social media analytics companies, peek inside their metrics?

-- Fun facts from the article: _Army of Jesus_ and _Intersectional Feminism_ were fake Russian Instagram accounts
us_politics  social_networks  networked_public_sphere  contagion  networks  teaching
november 2017 by rvenkat
Misinformation and Mass Audiences Edited by Brian G. Southwell, Emily A. Thorson, and Laura Sheble
Lies and inaccurate information are as old as humanity, but never before have they been so easy to spread. Each moment of every day, the Internet and broadcast media purvey misinformation, either deliberately or accidentally, to a mass audience on subjects ranging from politics to consumer goods to science and medicine, among many others. Because misinformation now has the potential to affect behavior on a massive scale, it is urgently important to understand how it works and what can be done to mitigate its harmful effects.

Misinformation and Mass Audiences brings together evidence and ideas from communication research, public health, psychology, political science, environmental studies, and information science to investigate what constitutes misinformation, how it spreads, and how best to counter it. The expert contributors cover such topics as whether and to what extent audiences consciously notice misinformation, the possibilities for audience deception, the ethics of satire in journalism and public affairs programming, the diffusion of rumors, the role of Internet search behavior, and the evolving efforts to counteract misinformation, such as fact-checking programs. The first comprehensive social science volume exploring the prevalence and consequences of, and remedies for, misinformation as a mass communication phenomenon, Misinformation and Mass Audiences will be a crucial resource for students and faculty researching misinformation, policymakers grappling with questions of regulation and prevention, and anyone concerned about this troubling, yet perhaps unavoidable, dimension of current media systems.
book  misinformation  disinformation  media_studies  public_sphere  contagion  social_psychology  journalism  dmce  networks  teaching
november 2017 by rvenkat
Researcher Bias and Influence: How Do Different Sources of Policy Analysis Affect Policy Preferences? by Grant Jacobsen :: SSRN
Analyses of policy options are often unavailable or only available from think tanks that may have political biases. This paper experimentally examines how voters respond to policy analysis and how the response differs when a nonpartisan, liberal, or conservative organization produces the analysis. Partisan organizations are effective at influencing individuals that share their ideology, but individuals collectively are most responsive to analysis produced by nonpartisan organizations. This pattern holds consistently across several areas of policy. The results suggest that increasing the availability of nonpartisan analysis would increase the diffusion of information into the public and reduce political polarization.
elite_opinion  public_opinion  contagion  think_tank  institutions  social_epistemology  cultural_cognition  political_psychology  polarization  democracy  via:nyhan
october 2017 by rvenkat
Social Media and Fake News in the 2016 Election
Following the 2016 US presidential election, many have expressed concern about the effects of false stories ("fake news"), circulated largely through social media. We discuss the economics of fake news and present new data on its consumption prior to the election. Drawing on web browsing data, archives of fact-checking websites, and results from a new online survey, we find: 1) social media was an important but not dominant source of election news, with 14 percent of Americans calling social media their "most important" source; 2) of the known false news stories that appeared in the three months before the election, those favoring Trump were shared a total of 30 million times on Facebook, while those favoring Clinton were shared 8 million times; 3) the average American adult saw on the order of one or perhaps several fake news stories in the months around the election, with just over half of those who recalled seeing them believing them; and 4) people are much more likely to believe stories that favor their preferred candidate, especially if they have ideologically segregated social media networks.
social_media  social_networks  misinformation  disinformation  contagion  microeconomics  market_microstructure  news_media  market_failures  networks  teaching  
october 2017 by rvenkat
Now out of Never: The Element of Surprise in the East European Revolution of 1989 | World Politics | Cambridge Core
Like many major revolutions in history, the East European Revolution of 1989 caught its leaders, participants, victims, and observers by surprise. This paper offers an explanation whose crucial feature is a distinction between private and public preferences. By suppressing their antipathies to the political status quo, the East Europeans misled everyone, including themselves, as to the possibility of a successful uprising. In effect, they conferred on their privately despised governments an aura of invincibility. Under the circumstances, public opposition was poised to grow explosively if ever enough people lost their fear of exposing their private preferences. The currently popular theories of revolution do not make clear why uprisings easily explained in retrospect may not have been anticipated. The theory developed here fills this void. Among its predictions is that political revolutions will inevitably continue to catch the world by surprise.
european_politics  revolutions  20th_century  social_behavior  contagion  homophily  ?  social_psychology  institutions  norms  collective_dynamics  judgment_decision-making  dmce  teaching  timur.kuran
october 2017 by rvenkat
Private Truths, Public Lies — Timur Kuran | Harvard University Press
Preference falsification, according to the economist Timur Kuran, is the act of misrepresenting one’s wants under perceived social pressures. It happens frequently in everyday life, such as when we tell the host of a dinner party that we are enjoying the food when we actually find it bland. In Private Truths, Public Lies, Kuran argues convincingly that the phenomenon not only is ubiquitous but has huge social and political consequences. Drawing on diverse intellectual traditions, including those rooted in economics, psychology, sociology, and political science, Kuran provides a unified theory of how preference falsification shapes collective decisions, orients structural change, sustains social stability, distorts human knowledge, and conceals political possibilities.

A common effect of preference falsification is the preservation of widely disliked structures. Another is the conferment of an aura of stability on structures vulnerable to sudden collapse. When the support of a policy, tradition, or regime is largely contrived, a minor event may activate a bandwagon that generates massive yet unanticipated change.

In distorting public opinion, preference falsification also corrupts public discourse and, hence, human knowledge. So structures held in place by preference falsification may, if the condition lasts long enough, achieve increasingly genuine acceptance. The book demonstrates how human knowledge and social structures co-evolve in complex and imperfectly predictable ways, without any guarantee of social efficiency.

Private Truths, Public Lies uses its theoretical argument to illuminate an array of puzzling social phenomena. They include the unexpected fall of communism, the paucity, until recently, of open opposition to affirmative action in the United States, and the durability of the beliefs that have sustained India’s caste system
book  social_behavior  contagion  homophily  ?  social_psychology  institutions  norms  collective_dynamics  judgment_decision-making  dmce  teaching  timur.kuran
october 2017 by rvenkat
[1709.09636] Randomized experiments to detect and estimate social influence in networks
Estimation of social influence in networks can be substantially biased in observational studies due to homophily and network correlation in exposure to exogenous events. Randomized experiments, in which the researcher intervenes in the social system and uses randomization to determine how to do so, provide a methodology for credibly estimating of causal effects of social behaviors. In addition to addressing questions central to the social sciences, these estimates can form the basis for effective marketing and public policy.
In this review, we discuss the design space of experiments to measure social influence through combinations of interventions and randomizations. We define an experiment as combination of (1) a target population of individuals connected by an observed interaction network, (2) a set of treatments whereby the researcher will intervene in the social system, (3) a randomization strategy which maps individuals or edges to treatments, and (4) a measurement of an outcome of interest after treatment has been assigned. We review experiments that demonstrate potential experimental designs and we evaluate their advantages and tradeoffs for answering different types of causal questions about social influence. We show how randomization also provides a basis for statistical inference when analyzing these experiments.
review  networks  influence  social_networks  homophily  contagion  causal_inference  intervention  experimental_design  teaching
october 2017 by rvenkat
[1305.6156] Estimating Average Causal Effects Under General Interference, with Application to a Social Network Experiment
This paper presents a randomization-based framework for estimating causal effects under interference between units, motivated by challenges that arise in analyzing experiments on social networks. The framework integrates three components: (i) an experimental design that defines the probability distribution of treatment assignments, (ii) a mapping that relates experimental treatment assignments to exposures received by units in the experiment, and (iii) estimands that make use of the experiment to answer questions of substantive interest. We develop the case of estimating average unit-level causal effects from a randomized experiment with interference of arbitrary but known form. The resulting estimators are based on inverse probability weighting. We provide randomization-based variance estimators that account for the complex clustering that can occur when interference is present. We also establish consistency and asymptotic normality under local dependence assumptions. We discuss refinements including covariate-adjusted effect estimators and ratio estimation. We evaluate empirical performance in realistic settings with a naturalistic simulation using social network data from American schools. We then present results from a field experiment on the spread of anti-conflict norms and behavior among school students.
social_networks  influence  contagion  homophily  causal_inference  statistics  methods  networks  teaching
september 2017 by rvenkat
Unleashed
Significant social change often comes from the unleashing of hidden preferences; it also comes from the construction of novel preferences. Under the pressure of social norms, people sometimes falsify their preferences. They do not feel free to say or do as they wish. Once norms are weakened or revised, through private efforts or law, it becomes possible to discover preexisting preferences. Because those preferences existed but were concealed, large-scale movements are both possible and exceedingly difficult to predict; they are often startling. But revisions of norms can also construct rather than uncover preferences. Once norms are altered, again through private efforts or law, people come to hold preferences that they did not hold before. Nothing has been unleashed. These points bear on the rise and fall (and rise again, and fall again) of discrimination on the basis of sex and race (and also religion and ethnicity). They also help illuminate the dynamics of social cascades and the effects of social norms on diverse practices and developments, including smoking, drinking, police brutality, protest activity, veganism, drug use, crime, white nationalism, “ethnification,” considerateness, and the public expression of religious beliefs.
social_behavior  contagion  homophily  ?  social_psychology  institutions  norms  collective_dynamics  cass.sunstein  judgment_decision-making  dmce  teaching
august 2017 by rvenkat
[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.
causal_inference  contagion  homophily  observational_studies  review  social_networks  teaching  ?  via:cshalizi
july 2017 by rvenkat
[1707.07592] The spread of fake news by social bots
he massive spread of fake news has been identified as a major global risk and has been alleged to influence elections and threaten democracies. Communication, cognitive, social, and computer scientists are engaged in efforts to study the complex causes for the viral diffusion of digital misinformation and to develop solutions, while search and social media platforms are beginning to deploy countermeasures. However, to date, these efforts have been mainly informed by anecdotal evidence rather than systematic data. Here we analyze 14 million messages spreading 400 thousand claims on Twitter during and following the 2016 U.S. presidential campaign and election. We find evidence that social bots play a key role in the spread of fake news. Accounts that actively spread misinformation are significantly more likely to be bots. Automated accounts are particularly active in the early spreading phases of viral claims, and tend to target influential users. Humans are vulnerable to this manipulation, retweeting bots who post false news. Successful sources of false and biased claims are heavily supported by social bots. These results suggests that curbing social bots may be an effective strategy for mitigating the spread of online misinformation.
social_media  social_networks  misinformation  disinformation  contagion  networks  teaching
july 2017 by rvenkat
Exercise contagion in a global social network : Nature Communications
We leveraged exogenous variation in weather patterns across geographies to identify social contagion in exercise behaviours across a global social network. We estimated these contagion effects by combining daily global weather data, which creates exogenous variation in running among friends, with data on the network ties and daily exercise patterns of ∼1.1M individuals who ran over 350M km in a global social network over 5 years. Here we show that exercise is socially contagious and that its contagiousness varies with the relative activity of and gender relationships between friends. Less active runners influence more active runners, but not the reverse. Both men and women influence men, while only women influence other women. While the Embeddedness and Structural Diversity theories of social contagion explain the influence effects we observe, the Complex Contagion theory does not. These results suggest interventions that account for social contagion will spread behaviour change more effectively.
contagion  social_networks  influence  causal_inference  via:duncan.watts  networks  teaching
april 2017 by rvenkat
[0710.3256] Statistical physics of social dynamics
Statistical physics has proven to be a very fruitful framework to describe phenomena outside the realm of traditional physics. The last years have witnessed the attempt by physicists to study collective phenomena emerging from the interactions of individuals as elementary units in social structures. Here we review the state of the art by focusing on a wide list of topics ranging from opinion, cultural and language dynamics to crowd behavior, hierarchy formation, human dynamics, social spreading. We highlight the connections between these problems and other, more traditional, topics of statistical physics. We also emphasize the comparison of model results with empirical data from social systems.

-- sociology Phys.Rev E style
social_behavior  opinion_dynamics  statistical_mechanics  contagion  epidemics  interating_particle_system  active_matter  networks  ?  teaching
january 2017 by rvenkat
[1609.00682] Unifying Markov Chain Approach for Disease and Rumor Spreading in Complex Networks
Spreading processes are ubiquitous in natural and artificial systems. They can be studied via a plethora of models, depending on the specific details of the phenomena under study. Disease contagion and rumor spreading are among the most important of these processes due to their practical relevance. However, despite the similarities between them, current models address both spreading dynamics separately. In this paper, we propose a general information spreading model that is based on discrete time Markov chains. The model includes all the transitions that are plausible for both a disease contagion process and rumor propagation. We show that our model not only covers the traditional spreading schemes, but that it also contains some features relevant in social dynamics, such as apathy, forgetting, and lost/recovering of interest. The model is evaluated analytically to obtain the spreading thresholds and the early time dynamical behavior for the contact and reactive processes in several scenarios. Comparison with Monte Carlo simulations shows that the Markov chain formalism is highly accurate while it excels in computational efficiency. We round off our work by showing how the proposed framework can be applied to the study of spreading processes occurring on social networks.

-- there's got to be work that has already discussed this. They cannot be the first. I can't see anything new here.....
epidemics  social_networks  contagion  networks  teaching  via:strogatz
december 2016 by rvenkat
[1408.2701] Epidemic processes in complex networks
In recent years the research community has accumulated overwhelming evidence for the emergence of complex and heterogeneous connectivity patterns in a wide range of biological and sociotechnical systems. The complex properties of real-world networks have a profound impact on the behavior of equilibrium and nonequilibrium phenomena occurring in various systems, and the study of epidemic spreading is central to our understanding of the unfolding of dynamical processes in complex networks. The theoretical analysis of epidemic spreading in heterogeneous networks requires the development of novel analytical frameworks, and it has produced results of conceptual and practical relevance. A coherent and comprehensive review of the vast research activity concerning epidemic processes is presented, detailing the successful theoretical approaches as well as making their limits and assumptions clear. Physicists, mathematicians, epidemiologists, computer, and social scientists share a common interest in studying epidemic spreading and rely on similar models for the description of the diffusion of pathogens, knowledge, and innovation. For this reason, while focusing on the main results and the paradigmatic models in infectious disease modeling, the major results concerning generalized social contagion processes are also presented. Finally, the research activity at the forefront in the study of epidemic spreading in coevolving, coupled, and time-varying networks is reported.
epidemics  contagion  networks  review  teaching  via:strogatz
december 2016 by rvenkat
[1608.09010] Statistical physics of vaccination
Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination - one of the most important preventive measures of modern times - is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research.

-- please let it not be another physicists_think_they_know_how_the_world_works_review
epidemiology  dynamical_system  contagion  influence  review  social_behavior
december 2016 by rvenkat
[1607.06565] Controlling for Latent Homophily in Social Networks through Inferring Latent Locations
"Social influence cannot be identified from purely observational data on social networks, because such influence is generically confounded with latent homophily, i.e., with a node's network partners being informative about the node's attributes and therefore its behavior. We show that {\em if} the network grows according to either a community (stochastic block) model, or a continuous latent space model, then latent homophilous attributes can be consistently estimated from the global pattern of social ties. Moreover, these estimates are informative enough that controlling for them allows for unbiased and consistent estimation of social-influence effects in additive models. For community models, we also provide bounds on the finite-sample bias. These are the first results on the consistent estimation of social-influence effects in the presence of latent homophily, and we discuss the prospects for generalizing them."
via:cshalizi  homophily  contagion  statistics  teaching  networks  social_networks
july 2016 by rvenkat
[1507.08282] Common Knowledge on Networks
Common knowledge of intentions is crucial to basic social tasks ranging from cooperative hunting to oligopoly collusion, riots, revolutions, and the evolution of social norms and human culture. Yet little is known about how common knowledge leaves a trace on the dynamics of a social network. Here we show how an individual's network properties---primarily local clustering and betweenness centrality---provide strong signals of the ability to successfully participate in common knowledge tasks. These signals are distinct from those expected when practices are contagious, or when people use less-sophisticated heuristics that do not yield true coordination. This makes it possible to infer decision rules from observation. We also find that tasks that require common knowledge can yield significant inequalities in success, in contrast to the relative equality that results when practices spread by contagion alone.
collective_cognition  collective_intention  influence  contagion  networks
august 2015 by rvenkat
Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks
Node characteristics and behaviors are often correlated with the structure of social networks over time. While evidence of this type of assortative mixing and temporal clustering of behaviors among linked nodes is used to support claims of peer influence and social contagion in networks, homophily may also explain such evidence. Here we develop a dynamic matched sample estimation framework to distinguish influence and homophily effects in dynamic networks, and we apply this framework to a global instant messaging network of 27.4 million users, using data on the day-by-day adoption of a mobile service application and users' longitudinal behavioral, demographic, and geographic data. We find that previous methods overestimate peer influence in product adoption decisions in this network by 300–700%, and that homophily explains >50% of the perceived behavioral contagion. These findings and methods are essential to both our understanding of the mechanisms that drive contagions in networks and our knowledge of how to propagate or combat them in domains as diverse as epidemiology, marketing, development economics, and public health.

-- Aral has more recent papers on the topic. Some of them have nice overviews that might be helpful for student projects.
via:cshalizi  teaching  contagion  homophily  influence  networks
july 2015 by rvenkat
[1507.04192] Beyond Contagion: Reality Mining Reveals Complex Patterns of Social Influence
Contagion, a concept from epidemiology, has long been used to characterize social influence on people's behavior and affective (emotional) states. While it has revealed many useful insights, it is not clear whether the contagion metaphor is sufficient to fully characterize the complex dynamics of psychological states in a social context. Using wearable sensors that capture daily face-to-face interaction, combined with three daily experience sampling surveys, we collected the most comprehensive data set of personality and emotion dynamics of an entire community of work. From this high-resolution data about actual (rather than self-reported) face-to-face interaction, a complex picture emerges where contagion (that can be seen as adaptation of behavioral responses to the behavior of other people) cannot fully capture the dynamics of transitory states. We found that social influence has two opposing effects on states: \emph{adaptation} effects that go beyond mere contagion, and \emph{complementarity} effects whereby individuals' behaviors tend to complement the behaviors of others. Surprisingly, these effects can exhibit completely different directions depending on the stable personality or emotional dispositions (stable traits) of target individuals. Our findings provide a foundation for richer models of social dynamics, and have implications on organizational engineering and workplace well-being.

-- again Pentland is making claims, I remain skeptical. The only interesting aspect of the study is the well-being indicators. Even there, I am not sure if they are saying things right.
social_behavior  contagion  subjective_well-being  data-streams  data_mining
july 2015 by rvenkat
[1506.00251] Kinetics of Social Contagion
"Diffusion of information, behavioural patterns or innovations follows diverse pathways depending on a number of conditions, including the structure of the underlying social network, the sensitivity to peer pressure and the influence of media. Here we study analytically and by simulations a general model that incorporates threshold mechanism capturing sensitivity to peer pressure, the effect of immune' nodes who never adopt, and a perpetual flow of external information. While any constant, non-zero rate of dynamically-introduced innovators leads to global spreading, the kinetics by which the asymptotic state is approached show rich behaviour. In particular we find that, as a function of the density of immune nodes, there is a transition from fast to slow spreading governed by entirely different mechanisms. This transition happens below the percolation threshold of fragmentation of the network, and has its origin in the competition between cascading behaviour induced by innovators and blocking of adoption due to immune nodes. This change is accompanied by a percolation transition of the induced clusters."
contagion  via:cshalizi  social_networks
july 2015 by rvenkat
[1506.03022] The Majority Illusion in Social Networks
"Social behaviors are often contagious, spreading through a population as individuals imitate the decisions and choices of others. A variety of global phenomena, from innovation adoption to the emergence of social norms and political movements, arise as a result of people following a simple local rule, such as copy what others are doing. However, individuals often lack global knowledge of the behaviors of others and must estimate them from the observations of their friends' behaviors. In some cases, the structure of the underlying social network can dramatically skew an individual's local observations, making a behavior appear far more common locally than it is globally. We trace the origins of this phenomenon, which we call "the majority illusion," to the friendship paradox in social networks. As a result of this paradox, a behavior that is globally rare may be systematically overrepresented in the local neighborhoods of many people, i.e., among their friends. Thus, the "majority illusion" may facilitate the spread of social contagions in networks and also explain why systematic biases in social perceptions, for example, of risky behavior, arise. Using synthetic and real-world networks, we explore how the "majority illusion" depends on network structure and develop a statistical model to calculate its magnitude in a network."
contagion  via:cshalizi  social_networks
july 2015 by rvenkat
[1506.00986] The Impact of Heterogeneous Thresholds on Social Contagion with Multiple Initiators
"The threshold model is a simple but classic model of contagion spreading in complex social systems. To capture the complex nature of social influencing we investigate numerically and analytically the transition in the behavior of threshold-limited cascades in the presence of multiple initiators as the distribution of thresholds is varied between the two extreme cases of identical thresholds and a uniform distribution. We accomplish this by employing a truncated normal distribution of the nodes' thresholds and observe a non-monotonic change in the cascade size as we vary the standard deviation. Further, for a sufficiently large spread in the threshold distribution, the tipping-point behavior of the social influencing process disappears and is replaced by a smooth crossover governed by the size of initiator set. We demonstrate that for a given size of the initiator set, there is a specific variance of the threshold distribution for which an opinion spreads optimally. Furthermore, in the case of synthetic graphs we show that the spread asymptotically becomes independent of the system size, and that global cascades can arise just by the addition of a single node to the initiator set."
contagion  opinion_dynamics  teaching  via:cshalizi  networks
july 2015 by rvenkat

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