rvenkat + influence   24

[1803.08491] Influence of fake news in Twitter during the 2016 US presidential election
We investigate the influence of fake and traditional, fact-based, news outlets on Twitter during the 2016 US presidential election. Using a comprehensive dataset of 171 million tweets covering the five months preceding election day, we identify 30 million tweets, sent by 2.2 million users, which are classified as spreading fake and extremely biased news, based on a list of news outlets curated from independent fact-checking organizations, and traditional news from right to left. We find that 29% of these tweets disseminate fake or extremely biased news. We fully characterize the networks of users spreading fake and traditional news and find the most influential users. Contrary to traditional news, where influencers are mainly journalists or news outlets with verified Twitter accounts, e.g. @FoxNews and @CNN, the majority of fake news influencers have unverified or deleted accounts. In particular, accounts with seemingly deceiving profiles are found among the top fake and extremely biased influencers. We find that the three top influencers spreading (i.e. re-tweeting) fake news websites are @PrisonPlanet, @RealAlexJones and @zerohedge and re-tweeting extremely bias news websites are @realDonaldTrump, @DailyCaller and @BreitbartNews. To understand how fake news influenced Twitter opinion during the presidential election, we perform a Granger-causality test between the time series of activity of influencers and the supporters of each presidential candidate: Trump and Clinton. Two different news spreading mechanisms are revealed: (i) The influencers spreading traditional center and left leaning news largely determine (Granger-cause) the opinion of the Clinton supporters. (ii) Remarkably, this causality is reversed for the fake news: the opinion of Trump supporters largely Granger-causes the dynamics of influencers spreading fake and extremely biased news.

Other work
https://arxiv.org/abs/1707.01594
https://www.nature.com/articles/nphys1746

-- Absence of citations to other careful analysis available make me not trust these papers.
social_networks  influence  causal_inference  i_remain_skeptical 
march 2018 by rvenkat
Social Influence and Reciprocity in Online Gift Giving – Facebook Research
Giving gifts is a fundamental part of human relationships that is being affected by technology. The Internet enables people to give at the last minute and over long distances, and to observe friends giving and receiving gifts. How online gift giving spreads in social networks is therefore important to understand. We examine 1.5 million gift exchanges on Facebook and show that receiving a gift causes individuals to be 56% more likely to give a gift in the future. Additional surveys show that online gift giving was more socially acceptable to those who learned about it by observing friends’ participation instead of a non-social encouragement. Most receivers pay the gift forward instead of reciprocating directly online, although surveys revealed additional instances of direct reciprocity, where the initial gifting occurred offline. Thus, social influence promotes the spread of online gifting, which both complements and substitutes for offline gifting.

-- 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
[1708.04575] Information flow reveals prediction limits in online social activity
Modern society depends on the flow of information over online social networks, and popular social platforms now generate significant behavioral data. Yet it remains unclear what fundamental limits may exist when using these data to predict the activities and interests of individuals. Here we apply tools from information theory to estimate the predictive information content of the writings of Twitter users and to what extent that information flows between users. Distinct temporal and social effects are visible in the information flow, and these estimates provide a fundamental bound on the predictive accuracy achievable with these data. Due to the social flow of information, we estimate that approximately 95% of the potential predictive accuracy attainable for an individual is available within the social ties of that individual only, without requiring the individual's data.

https://github.com/bagrow

--According to them, they use a version of Granger causality to measure information flow. But they never address the critiques of transfer entropies and information flows from this paper
https://arxiv.org/abs/1512.06479

The last tag applies because they do not discuss limitations of their approach, especially from Crutchfield et al point of view.
social_networks  influence  information  information_theory  networks  i_remain_skeptical 
march 2018 by rvenkat
Psychological targeting as an effective approach to digital mass persuasion
People are exposed to persuasive communication across many different contexts: Governments, companies, and political parties use persuasive appeals to encourage people to eat healthier, purchase a particular product, or vote for a specific candidate. Laboratory studies show that such persuasive appeals are more effective in influencing behavior when they are tailored to individuals’ unique psychological characteristics. However, the investigation of large-scale psychological persuasion in the real world has been hindered by the questionnaire-based nature of psychological assessment. Recent research, however, shows that people’s psychological characteristics can be accurately predicted from their digital footprints, such as their Facebook Likes or Tweets. Capitalizing on this form of psychological assessment from digital footprints, we test the effects of psychological persuasion on people’s actual behavior in an ecologically valid setting. In three field experiments that reached over 3.5 million individuals with psychologically tailored advertising, we find that matching the content of persuasive appeals to individuals’ psychological characteristics significantly altered their behavior as measured by clicks and purchases. Persuasive appeals that were matched to people’s extraversion or openness-to-experience level resulted in up to 40% more clicks and up to 50% more purchases than their mismatching or unpersonalized counterparts. Our findings suggest that the application of psychological targeting makes it possible to influence the behavior of large groups of people by tailoring persuasive appeals to the psychological needs of the target audiences. We discuss both the potential benefits of this method for helping individuals make better decisions and the potential pitfalls related to manipulation and privacy.

--meta-studies of political canvasing and prejudice reduction all suggest otherwise; so has replications of priming studies. Unless there are other mechanisms at work, these results seem untrustworthy.
big_five  intervention  social_media  influence  social_psychology  i_remain_skeptical  via:nyhan 
november 2017 by rvenkat
Change behaviors by changing perception of normal | Stanford News
-- obviously, one study does not anything but the idea of beliefs about norms and its dynamics are interesting. Political science applications?
norms  social_psychology  influence  social_behavior  dmce  teaching  models_of_behavior 
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
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
Is the Internet Causing Political Polarization? Evidence from Demographics
We combine nine previously proposed measures to construct an index of political polarization among US adults. We find that the growth in polarization in recent years is largest for the demographic groups least likely to use the internet and social media. For example, our overall index and eight of the nine individual measures show greater increases for those older than 75 than for those aged 18–39. These facts argue against the hypothesis that the internet is a primary driver of rising political polarization.

-- a really misleading version of the article here
http://www.vox.com/policy-and-politics/2017/4/12/15259438/social-media-political-polarization
democracy  media_studies  causal_inference  influence  polarization  political_science  us_politics  i_remain_skeptical 
april 2017 by rvenkat
Peer Effects on the United States Supreme Court by Richard Holden, Michael Keane, Matthew Lilley :: SSRN
Using data on essentially every US Supreme Court decision since 1946, we estimate a model of peer effects on the Court. We consider both the impact of justice ideology and justice votes on the votes of their peers. To identify these peer effects we use two instruments. The first is based on the composition of the Court, determined by which justices sit on which cases due to recusals or health reasons for not sitting. The second utilizes the fact that many justices previously sat on Federal Circuit Courts and are empirically much more likely to affirm decisions from their “home” court. We find large peer effects. Replacing a single justice with one who votes in a conservative direction 10 percentage points more frequently increases the probability that each other justice votes conservative by 1.63 percentage points. In terms of votes, a 10 percentage point increase in the probability that a single justice votes conservative leads to a 1.1 percentage increase in the probability that each other justice votes conservative. Finally, a single justice becoming 10% more likely to vote conservative increases the share of cases with a conservative outcome by 3.6 percentage points – excluding the direct effect of that justice – and reduces the share with a liberal outcome by 3.2 percentage points. In general, the indirect effect of a justice’s vote on the outcome through the votes of their peers is typically several times larger than the direct mechanical effect of the justice’s own vote.

-- a dilute version here
https://www.nytimes.com/2017/04/04/upshot/how-gorsuchs-influence-could-be-bigger-than-his-single-vote.html

-- a related paper here
http://www.princeton.edu/~jkastell/AA_Panel_Effects/kastellec_racial_diversity_final.pdf
empirical_legal_studies  us_supreme_court  influence  homophily  groups  judgment_decision-making  dmce  teaching  political_science  political_economy  collective_cognition  causal_inference  via:wolfers 
april 2017 by rvenkat
Secrets and Misperceptions: The Creation of Self-Fulfilling Illusions | Sociological Science
This study examines who hears what secrets, comparing two similar secrets — one which is highly stigmatized and one which is less so. Using a unique survey representative of American adults and intake forms from a medical clinic, I document marked differences in who hears these secrets. People who are sympathetic to the stigmatizing secret are more likely to hear of it than those who may react negatively. This is a consequence not just of people selectively disclosing their own secrets but selectively sharing others’ as well. As a result, people in the same social network will be exposed to and influenced by different information about those they know and hence experience that network differently. When people effectively exist in networks tailored by others to not offend then the information they hear tends to be that of which they already approve. Were they to hear secrets they disapprove of then their attitudes might change but they are less likely to hear those secrets. As such, the patterns of secret-hearing contribute to a stasis in public opinion.

--her dissertation (Secrets and Social Influence) here
http://escholarship.org/uc/item/1hf7s08s
sociology  influence  public_opinion  opinion_dynamics  social_networks  norms  information_diffusion  people 
january 2017 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
Social Influences on Policy Preferences by Meirav Furth-Matzkin, Cass R. Sunstein :: SSRN
Social norms have been used to nudge people toward specified outcomes in various domains. But can people be nudged to support, or to reject, proposed government policies? How do people’s views change when they learn that the majority approves of a particular policy, or that the majority opposes it? To answer these questions, we conducted a series of experiments. We find that in important contexts, learning about the majority’s opinion causes a significant shift toward support for or opposition to particular policies. At the same time, we find that when people’s views are fixed and firm, they are unlikely to conform to the majority’s view and that they might even show reactance. We show this pattern of results with respect to people’s support for or opposition to governmental policies in a wide range of substantive areas — and also to the use of paternalistic tools, such as nudges or bans

-- are there IPS models that already model this?
social_behavior  influence  norms  democracy  collective_cognition  interating_particle_system  collective_dynamics  decision_making  policy  via:sunstein 
december 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
Dissecting the Spirit of Gezi: Influence vs. Selection in the Occupy Gezi Movement. by Ceren Budak, Duncan J. Watts :: SSRN
Do social movements actively shape the opinions and attitudes of participants by bringing together diverse groups of activists who subsequently influence one another? Ethnographic studies of the 2013 Gezi uprising in Turkey suggest that the answer is "yes," pointing to the solidarity exhibited by protesters who previously identified with political groups that were traditionally indifferent, or even hostile, to one another. We argue, however, that two mechanisms with rather different implications could have generated this observed outcome: "influence," referring to a spontaneous change in attitudes caused by interacting with other movement participants; and "selection," meaning that the individuals who participated in the movement were more likely to be supportive of other groups prior to the event itself. We tease out the relative importance of these two mechanisms in the observed solidarity in Gezi uprising by constructing a panel of over 30,000 Twitter users and analyzing their support for the main Turkish opposition parties before, during, and after the movement. We find that although individuals did change their support over time in significant ways, becoming in general more supportive of the other opposition parties, those who participated in the movement were also significantly more supportive of the other opposition parties all along. Together these findings suggest that both influence and selection were important to generating the observed solidarity among opposition supporters, but that selection was the more important mechanism. We also suggest that while our substantive results are specific to the Gezi uprising our method of ex-post panel construction could be useful to studies of social movements and mass opinion change more generally. In contrast with traditional panel studies, which must be designed and implemented prior to the event of interest occurring, ours can be designed ex-post, and hence can be used to study events such as uprisings that are unanticipated by researchers or are inaccessible for other reasons. We conclude that although social media platforms such as Twitter suffer from a number of well-known limitations, their "always on" nature combined with their widespread availability offer an important source of public opinion data to students of social change.

--Looks very interesting.
influence  selection  twitter  teaching  networks  social_networks 
june 2015 by rvenkat
[1506.05903] Detecting Real-World Influence Through Twitter
In this paper, we investigate the issue of detecting the real-life influence of people based on their Twitter account. We propose an overview of common Twitter features used to characterize such accounts and their activity, and show that these are inefficient in this context. In particular, retweets and followers numbers, and Klout score are not relevant to our analysis. We thus propose several Machine Learning approaches based on Natural Language Processing and Social Network Analysis to label Twitter users as Influencers or not. We also rank them according to a predicted influence level. Our proposals are evaluated over the CLEF RepLab 2014 dataset, and outmatch state-of-the-art ranking methods.

--Are they proposing a model of influence?
influence  twitter  data 
june 2015 by rvenkat
[1506.06294] Adaptive Influence Maximization in Dynamic Social Networks
For the purpose of propagating information and ideas through a social network, a seeding strategy aims to find a small set of seed users that are able to maximize the spread of the influence, which is termed as influence maximization problem. Despite a large number of works have studied this problem, the existing seeding strategies are limited to the static social networks. In fact, due to the high speed data transmission and the large population of participants, the diffusion processes in real-world social networks have many aspects of uncertainness. Unfortunately, as shown in the experiments, in such cases the state-of-art seeding strategies are pessimistic as they fails to trace the dynamic changes in a social network. In this paper, we study the strategies selecting seed users in an adaptive manner. We first formally model the Dynamic Independent Cascade model and introduce the concept of adaptive seeding strategy. Then based on the proposed model, we show that a simple greedy adaptive seeding strategy finds an effective solution with a provable performance guarantee. Besides the greedy algorithm an efficient heuristic algorithm is provided in order to meet practical requirements. Extensive experiments have been performed on both the real-world networks and synthetic power-law networks. The results herein demonstrate the superiority of the adaptive seeding strategies to other standard methods.

--The font and latex style sheets suggest that it is destined for an IEEE publication.
dynamics  influence  information_diffusion  networks 
june 2015 by rvenkat
A Cluster Process Representation of a Self-Exciting Process on JSTOR
The original theoretical paper on Hawkes process. They use this in a variety of settings, especially in opinion dynamics, where I first encountered them (T. Liniger. Multivariate Hawkes Processes. PhD thesis, ETHZ, 2009.) I wonder if these models are taken seriously by sociologists.

Corrections
I encountered these processes elsewhere!
Hawkes processes are sometimes used as models of ion channels. Colquhoun D, Hawkes AG have written a series of papers on the topic.
stochastic_process  gpp  influence  information_diffusion  opinion_dynamics 
june 2015 by rvenkat
[1506.05474] Modeling Opinion Dynamics in Diffusion Networks
Social media and social networking sites have become a global pinboard for exposition and discussion of news, topics, and ideas, where social media users increasingly form their opinion about a particular topic by learning information about it from her peers. In this context, whenever a user posts a message about a topic, we observe a noisy estimate of her current opinion about it but the influence the user may have on other users' opinions is hidden. In this paper, we introduce a probabilistic modeling framework of opinion dynamics, which allows the underlying opinion of a user to be modulated by those expressed by her neighbors over time. We then identify a set of conditions under which users' opinions converge to a steady state, find a linear relation between the initial opinions and the opinions in the steady state, and develop an efficient estimation method to fit the parameters of the model from historical fine-grained opinion and information diffusion event data. Experiments on data gathered from Twitter, Reddit and Amazon show that our model provides a good fit to the data and more accurate predictions than alternatives.
influence  information_diffusion  social_networks 
june 2015 by rvenkat
[1506.01589] Identifying Demand Effects in a Large Network of Product Categories
Planning marketing mix strategies requires retailers to understand within- as well as cross-category demand effects. Most retailers carry products in a large variety of categories, leading to a high number of such demand effects to be estimated. At the same time, we do not expect cross-category effects between all categories. This paper outlines a methodology to estimate a parsimonious product category network without prior constraints on its structure. To do so, sparse estimation of the Vector AutoRegressive Market Response Model is presented. We find that cross-category effects go beyond substitutes and complements, and that categories have asymmetric roles in the product category network. Destination categories are most influential for other product categories, while convenience and occasional categories are most responsive. Routine categories are moderately influential and moderately responsive.

-- Need to read it more before assigning it to students.
marketing  intervention  influence  teaching  networks 
june 2015 by rvenkat
Agnieszka Rusinowska
Works at the interface of Social Networks, Economics and Political Science
influence  opinion_formation  social_networks 
june 2015 by rvenkat

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