A 61-million-person experiment in social influence and political mobilization

Pricing a Protest: Forecasting the Dynamics of Civil Unrest Activity in Social Media

Online social media activity can often be a precursor to disruptive events such as protests, strikes, and “occupy” movements. We have observed that such civil unrest can galvanize supporters through social networks and help recruit activists to their cause. Understanding the dynamics of social network cascades and extrapolating their future growth will enable an analyst to detect or forecast major societal events. Existing work has primarily used structural and temporal properties of cascades to predict their future behavior. But factors like societal pressure, alignment of individual interests with broader causes, and perception of expected benefits also affect protest participation in social media. Here we develop an analysis framework using a differential game theoretic approach to characterize the cost of participating in a cascade, and demonstrate how we can combine such cost features with classical properties to forecast the future behavior of cascades. Using data from Twitter, we illustrate the effectiveness of our models on the “Brazilian Spring” and Venezuelan protests that occurred in June 2013 and November 2013, respectively. We demonstrate how our framework captures both qualitative and quantitative aspects of how these uprisings manifest through the lens of tweet volume on Twitter social media.

Multiple effect of social influence on cooperation in interdependent network games

The social influence exists widely in the human society, where individual decision-making process (from congressional election to electronic commerce) may be affected by the attitude and behavior of others belonging to different social networks. Here, we couple the snowdrift (SD) game and the prisoner's dilemma (PD) game on two interdependent networks, where strategies in both games are associated by social influence to mimick the majority rule. More accurately, individuals' strategies updating refers to social learning (based on payoff difference) and above-mentioned social influence (related with environment of interdependent group), which is controlled by social influence strength s. Setting s = 0 decouples the networks and returns the traditional network game; while its increase involves the interactions between networks. By means of numerous Monte Carlo simulations, we find that such a mechanism brings multiple influence to the evolution of cooperation. Small s leads to unequal cooperation level in both games, because social learning is still the main updating rule for most players. Though intermediate and large s guarantees the synchronized evolution of strategy pairs, cooperation finally dies out and reaches a completely dominance in both cases. Interestingly, these observations are attributed to the expansion of cooperation clusters. Our work may provide a new understanding to the emergence of cooperation in intercorrelated social systems.

Effect of Media Usage Selection on Social Mobilization Speed: Facebook vs E-Mail

Social mobilization is a process that enlists a large number of people to achieve a goal within a limited time, especially through the use of social media. There is increasing interest in understanding the factors that affect the speed of social mobilization. Based on the Langley Knights competition data set, we analyzed the differences in mobilization speed between users of Facebook and e-mail. We include other factors that may influence mobilization speed (gender, age, timing, and homophily of information source) in our model as control variables in order to isolate the effect of such factors. We show that, in this experiment, although more people used e-mail to recruit, the mobilization speed of Facebook users was faster than that of those that used e-mail. We were also able to measure and show that the mobilization speed for Facebook users was on average seven times faster compared to e-mail before controlling for other factors. After controlling for other factors, we show that Facebook users were 1.84 times more likely to register compared to e-mail users in the next period if they have not done so at any point in time. This finding could provide useful insights for future social mobilization efforts.

Human Rights Texts: Converting Human Rights Primary Source Documents into Data

We introduce and make publicly available a large corpus of digitized primary source human rights documents which are published annually by monitoring agencies that include Amnesty International, Human Rights Watch, the Lawyers Committee for Human Rights, and the United States Department of State. In addition to the digitized text, we also make available and describe document-term matrices, which are datasets that systematically organize the word counts from each unique document by each unique term within the corpus of human rights documents. To contextualize the importance of this corpus, we describe the development of coding procedures in the human rights community and several existing categorical indicators that have been created by human coding of the human rights documents contained in the corpus. We then discuss how the new human rights corpus and the existing human rights datasets can be used with a variety of statistical analyses and machine learning algorithms to help scholars understand how human rights practices and reporting have evolved over time. We close with a discussion of our plans for dataset maintenance, updating, and availability.

Tracking Time Evolution of Collective Attention Clusters in Twitter: Time Evolving Nonnegative Matrix Factorisation

Micro-blogging services, such as Twitter, offer opportunities to analyse user behaviour. Discovering and distinguishing behavioural patterns in micro-blogging services is valuable. However, it is difficult and challenging to distinguish users, and to track the temporal development of collective attention within distinct user groups in Twitter. In this paper, we formulate this problem as tracking matrices decomposed by Nonnegative Matrix Factorisation for time-sequential matrix data, and propose a novel extension of Nonnegative Matrix Factorisation, which we refer to as Time Evolving Nonnegative Matrix Factorisation (TENMF). In our method, we describe users and words posted in some time interval by a matrix, and use several matrices as time-sequential data. Subsequently, we apply Time Evolving Nonnegative Matrix Factorisation to these time-sequential matrices. TENMF can decompose time-sequential matrices, and can track the connection among decomposed matrices, whereas previous NMF decomposes a matrix into two lower dimension matrices arbitrarily, which might lose the time-sequential connection. Our proposed method has an adequately good performance on artificial data. Moreover, we present several results and insights from experiments using real data from Twitter.

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: adaptation effects that go beyond mere contagion, and 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.

Political psychology

Political psychology is a dynamic field of research that offers a unique blend of approaches and methods in the social and cognitive sciences. Political psychologists explore the interactions between macrolevel political structures and microlevel factors such as decision-making processes, motivations, and perceptions. In this article, we provide a broad overview of the field, beginning with a brief history of political psychology research and a summary of the primary methodological approaches in the field. We then give a more detailed account of research on ideology and social justice, two topics experiencing a resurgence of interest in current political psychology. Finally, we cover research on political persuasion and voting behavior. By summarizing these major areas of political psychology research, we hope to highlight the wide variety of theoretical and methodological approaches of cognitive scientists working at the intersection of psychology and political science. WIREs Cogn Sci 2014, 5:373-385. doi: 10.1002/wcs.1293 For further resources related to this article, please visit the WIREs website.

CONFLICT OF INTEREST

The authors have declared no conflicts of interest for this article.

Trend of Narratives in the Age of Misinformation

Social media enabled a direct path from producer to consumer of contents changing the way users get informed, debate, and shape their worldviews. Such a disintermediation might weaken consensus on social relevant issues in favor of rumors, mistrust, or conspiracy thinking-e.g., chem-trails inducing global warming, the link between vaccines and autism, or the New World Order conspiracy. Previous studies pointed out that consumers of conspiracy-like content are likely to aggregate in homophile clusters-i.e., echo-chambers. Along this path we study, by means of a thorough quantitative analysis, how different topics are consumed inside the conspiracy echo-chamber in the Italian Facebook. Through a semi-automatic topic extraction strategy, we show that the most consumed contents semantically refer to four specific categories: environment, diet, health, and geopolitics. We find similar consumption patterns by comparing users activity (likes and comments) on posts belonging to these different semantic categories. Finally, we model users mobility across the distinct topics finding that the more a user is active, the more he is likely to span on all categories. Once inside a conspiracy narrative users tend to embrace the overall corpus.

The search engine manipulation effect (SEME) and its possible impact on the outcomes of elections

Internet search rankings have a significant impact on consumer choices, mainly because users trust and choose higher-ranked results more than lower-ranked results. Given the apparent power of search rankings, we asked whether they could be manipulated to alter the preferences of undecided voters in democratic elections. Here we report the results of five relevant double-blind, randomized controlled experiments, using a total of 4,556 undecided voters representing diverse demographic characteristics of the voting populations of the United States and India. The fifth experiment is especially notable in that it was conducted with eligible voters throughout India in the midst of India's 2014 Lok Sabha elections just before the final votes were cast. The results of these experiments demonstrate that (i) biased search rankings can shift the voting preferences of undecided voters by 20% or more, (ii) the shift can be much higher in some demographic groups, and (iii) search ranking bias can be masked so that people show no awareness of the manipulation. We call this type of influence, which might be applicable to a variety of attitudes and beliefs, the search engine manipulation effect. Given that many elections are won by small margins, our results suggest that a search engine company has the power to influence the results of a substantial number of elections with impunity. The impact of such manipulations would be especially large in countries dominated by a single search engine company.

Social network targeting to maximise population behaviour change: a cluster randomised controlled trial

BACKGROUND

Information and behaviour can spread through interpersonal ties. By targeting influential individuals, health interventions that harness the distributive properties of social networks could be made more effective and efficient than those that do not. Our aim was to assess which targeting methods produce the greatest cascades or spillover effects and hence maximise population-level behaviour change.

METHODS

In this cluster randomised trial, participants were recruited from villages of the Department of Lempira, Honduras. We blocked villages on the basis of network size, socioeconomic status, and baseline rates of water purification, for delivery of two public health interventions: chlorine for water purification and multivitamins for micronutrient deficiencies. We then randomised villages, separately for each intervention, to one of three targeting methods, introducing the interventions to 5% samples composed of either: randomly selected villagers (n=9 villages for each intervention); villagers with the most social ties (n=9); or nominated friends of random villagers (n=9; the last strategy exploiting the so-called friendship paradox of social networks). Participants and data collectors were not aware of the targeting methods. Primary endpoints were the proportions of available products redeemed by the entire population under each targeting method. This trial is registered with ClinicalTrials.gov, number NCT01672580.

FINDINGS

Between Aug 4, and Aug 14, 2012, 32 villages in rural Honduras (25-541 participants each; total study population of 5773) received public health interventions. For each intervention, nine villages (each with 1-20 initial target individuals) were randomised, using a blocked design, to each of the three targeting methods. In nomination-targeted villages, 951 (74·3%) of 1280 available multivitamin tickets were redeemed compared with 940 (66·2%) of 1420 in randomly targeted villages and 744 (61·0%) of 1220 in indegree-targeted villages. All pairwise differences in redemption rates were significant (p<0·01) after correction for multiple comparisons. Targeting nominated friends increased adoption of the nutritional intervention by 12·2% compared with random targeting (95% CI 6·9-17·9). Targeting the most highly connected individuals, by contrast, produced no greater adoption of either intervention, compared with random targeting.

INTERPRETATION

Introduction of a health intervention to the nominated friends of random individuals can enhance that intervention's diffusion by exploiting intrinsic properties of human social networks. This method has the additional advantage of scalability because it can be implemented without mapping the network. Deployment of certain types of health interventions via network targeting, without increasing the number of individuals targeted or the resources used, could enhance the adoption and efficiency of those interventions, thereby improving population health.

FUNDING

National Institutes of Health, The Bill & Melinda Gates Foundation, Star Family Foundation, and the Canadian Institutes of Health Research.

Quantifying International Travel Flows Using Flickr

Online social media platforms are opening up new opportunities to analyse human behaviour on an unprecedented scale. In some cases, the fast, cheap measurements of human behaviour gained from these platforms may offer an alternative to gathering such measurements using traditional, time consuming and expensive surveys. Here, we use geotagged photographs uploaded to the photo-sharing website Flickr to quantify international travel flows, by extracting the location of users and inferring trajectories to track their movement across time. We find that Flickr based estimates of the number of visitors to the United Kingdom significantly correlate with the official estimates released by the UK Office for National Statistics, for 28 countries for which official estimates are calculated. Our findings underline the potential for indicators of key aspects of human behaviour, such as mobility, to be generated from data attached to the vast volumes of photographs posted online.

Online participation in climate change adaptation: A case study of agricultural adaptation measures in Northern Italy

The new EU strategy on adaptation to climate change suggests flexible and participatory approaches. Face-to-face contact, although it involves time-consuming procedures with a limited audience, has often been considered the most effective participatory approach. In recent years, however, there has been an increase in the visibility of different citizens' initiatives in the online world, which strengthens the possibility of greater citizen agency. This paper investigates whether the Internet can ensure efficient public participation with meaningful engagement in climate change adaptation. In elucidating issues regarding climate change adaptation, we developed an eParticipation framework to explore adaptation capacity of agriculture to climate change in Northern Italy. Farmers were mobilised using a pre-existing online network. First they took part in an online questionnaire for revealing their perceptions of and reactions to the impacts of ongoing changes in agriculture. We used these results to suggest a portfolio of policy measures and to set evaluation criteria. Farmers then evaluated these policy options, using a multi criteria analysis tool with a simple user-friendly interface. Our results showed that eParticipation is efficient: it supports a rapid data collection, while involving high number of participants. Moreover, we demonstrated that the digital divide is decreasingly an obstacle for using online spaces for public engagement. This research does not present eParticipation as a panacea. Rather, eParticipation was implemented with well-established participatory approaches to both validate the results and, consequently, communicate meaningful messages on local agricultural adaptation practices to regional decision-makers. Feedbacks from the regional decision-makers showed their interest in using eParticipation to improve communication with farmers in the future. We expect that, with further Internet proliferation, eParticipation may allow the inclusion of more representative samples, which would contribute to an informed and legitimate decision-making process.

Social proof in the human brain: Electrophysiological signatures of agreement and disagreement with the majority

Perceiving one's deviance from the majority usually instigates conformal adjustments of one's own behavior to that of the group. Using ERPs, we investigated the mechanisms by which agreeing and disagreeing with the majority are differentially represented in the human brain and affect subsequent cognitive processing. Replicating previous findings obtained in a slightly different paradigm, we found that learning about one's disagreement with the majority, as compared to learning about one's agreement with the majority, elicited a mediofrontal feedback negativity. Moreover, an enhanced posterior late positive complex was observed during the processing of agreement as compared to disagreement. Finally, when the to-be-judged faces were viewed for a second time, a stronger posterior P2 was observed for faces on whose judgment one had previously agreed with the majority than for those on which one had disagreed. We thus demonstrate that the brain places particular emphasis on the encoding of the rewarding experience of finding strong social proof for one's judgments. Likewise, having experienced agreement on the judgment of a certain item affects even the later reanalysis of this very item, as previous agreement increases early attention, as reflected in the P2. These findings corroborate and extend previous results and theories on the neurocognitive principles of social influence.

Digital Ecology: Coexistence and Domination among Interacting Networks

The overwhelming success of Web 2.0, within which online social networks are key actors, has induced a paradigm shift in the nature of human interactions. The user-driven character of Web 2.0 services has allowed researchers to quantify large-scale social patterns for the first time. However, the mechanisms that determine the fate of networks at the system level are still poorly understood. For instance, the simultaneous existence of multiple digital services naturally raises questions concerning which conditions these services can coexist under. Analogously to the case of population dynamics, the digital world forms a complex ecosystem of interacting networks. The fitness of each network depends on its capacity to attract and maintain users’ attention, which constitutes a limited resource. In this paper, we introduce an ecological theory of the digital world which exhibits stable coexistence of several networks as well as the dominance of an individual one, in contrast to the competitive exclusion principle. Interestingly, our theory also predicts that the most probable outcome is the coexistence of a moderate number of services, in agreement with empirical observations.

Untangling research and practice: What Facebook’s “emotional contagion” study teaches us

Published in 2014, the Facebook “emotional contagion” study prompted widespread discussions about the ethics of manipulating social media content. By and large, researchers focused on the lack of corporate institutional review boards and informed consent procedures, missing the crux of what upset people about both the study and Facebook’s underlying practices. This essay examines the reactions that unfolded, arguing the public’s growing discomfort with “big data” fueled the anger. To address these concerns, we need to start imagining a socio-technical approach to ethics that does not differentiate between corporate and research practices.

Field evidence of social influence in the expression of political preferences: the case of secessionists flags in Barcelona

Models of social influence have explored the dynamics of social contagion, imitation, and diffusion of different types of traits, opinions, and conducts. However, few behavioral data indicating social influence dynamics have been obtained from direct observation in “natural” social contexts. The present research provides that kind of evidence in the case of the public expression of political preferences in the city of Barcelona, where thousands of citizens supporting the secession of Catalonia from Spain have placed a Catalan flag in their balconies and windows. Here we present two different studies. 1) During July 2013 we registered the number of flags in 26% of the electoral districts in the city of Barcelona. We find that there is a large dispersion in the density of flags in districts with similar density of pro-independence voters. However, by comparing the moving average to the global mean we find that the density of flags tends to be fostered in electoral districts where there is a clear majority of pro-independence vote, while it is inhibited in the opposite cases. We also show that the distribution of flags in the observed districts deviates significantly from that of an equivalent random distribution. 2) During 17 days around Catalonia’s 2013 national holiday we observed the position at balcony resolution of the flags displayed in the facades of a sub-sample of 82 blocks. We compare the ‘clustering index’ of flags on the facades observed each day to thousands of equivalent random distributions. Again we provide evidence that successive hangings of flags are not independent events but that a local influence mechanism is favoring their clustering. We also find that except for the national holiday day the density of flags tends to be fostered in facades located in electoral districts where there is a clear majority of pro-independence vote.

A natural experiment of social network formation and dynamics

Social networks affect many aspects of life, including the spread of diseases, the diffusion of information, the workers' productivity, and consumers' behavior. Little is known, however, about how these networks form and change. Estimating causal effects and mechanisms that drive social network formation and dynamics is challenging because of the complexity of engineering social relations in a controlled environment, endogeneity between network structure and individual characteristics, and the lack of time-resolved data about individuals' behavior. We leverage data from a sample of 1.5 million college students on Facebook, who wrote more than 630 million messages and 590 million posts over 4 years, to design a long-term natural experiment of friendship formation and social dynamics in the aftermath of a natural disaster. The analysis shows that affected individuals are more likely to strengthen interactions, while maintaining the same number of friends as unaffected individuals. Our findings suggest that the formation of social relationships may serve as a coping mechanism to deal with high-stress situations and build resilience in communities.

Friends of friends: are indirect connections in social networks important to animal behaviour?

Friend of a friend relationships, or the indirect connections between people, influence our health, well-being, financial success and reproductive output. As with humans, social behaviours in other animals often occur within a broad interconnected network of social ties. Yet studies of animal social behaviour tend to focus on associations between pairs of individuals. With the increase in popularity of social network analysis, researchers have started to look beyond the dyad to examine the role of indirect connections in animal societies. Here, I provide an overview of the new knowledge that has been uncovered by these studies. I focus on research that has addressed both the causes of social behaviours, i.e. the cognitive and genetic basis of indirect connections, as well as their consequences, i.e. the impact of indirect connections on social cohesion, information transfer, cultural practices and fitness. From these studies, it is apparent that indirect connections play an important role in animal behaviour, although future research is needed to clarify their contribution.

A new source of data for public health surveillance: Facebook likes

Background

Investigation into personal health has become focused on conditions at an increasingly local level, while response rates have declined and complicated the process of collecting data at an individual level. Simultaneously, social media data have exploded in availability and have been shown to correlate with the prevalence of certain health conditions.

Objective

Facebook likes may be a source of digital data that can complement traditional public health surveillance systems and provide data at a local level. We explored the use of Facebook likes as potential predictors of health outcomes and their behavioral determinants.

Methods

We performed principal components and regression analyses to examine the predictive qualities of Facebook likes with regard to mortality, diseases, and lifestyle behaviors in 214 counties across the United States and 61 of 67 counties in Florida. These results were compared with those obtainable from a demographic model. Health data were obtained from both the 2010 and 2011 Behavioral Risk Factor Surveillance System (BRFSS) and mortality data were obtained from the National Vital Statistics System.

Results

Facebook likes added significant value in predicting most examined health outcomes and behaviors even when controlling for age, race, and socioeconomic status, with model fit improvements (adjusted R²) of an average of 58% across models for 13 different health-related metrics over basic sociodemographic models. Small area data were not available in sufficient abundance to test the accuracy of the model in estimating health conditions in less populated markets, but initial analysis using data from Florida showed a strong model fit for obesity data (adjusted R²=.77).

Conclusions

Facebook likes provide estimates for examined health outcomes and health behaviors that are comparable to those obtained from the BRFSS. Online sources may provide more reliable, timely, and cost-effective county-level data than that obtainable from traditional public health surveillance systems as well as serve as an adjunct to those systems.

Quantifying regional differences in the length of Twitter messages

The increasing usage of social media for conversations, together with the availability of its data to researchers, provides an opportunity to study human conversations on a large scale. Twitter, which allows its users to post messages of up to a limit of 140 characters, is one such social media. Previous studies of utterances in books, movies and Twitter have shown that most of these utterances, when transcribed, are much shorter than 140 characters. Furthermore, the median length of Twitter messages was found to vary across US states. Here, we investigate whether the length of Twitter messages varies across different regions in the UK. We find that the median message length, depending on grouping, can differ by up to 2 characters.

E-mail to Promote Colorectal Cancer Screening Within Social Networks: Acceptability and Content

Effective techniques to encourage colorectal cancer screening in underscreened populations have included social support interventions and e-mail reminders from physicians. Personalized e-mail messages to promote colorectal cancer screening within social networks could be even more effective but have not been studied. The authors interviewed 387 e-mail users, aged 42-73 years in Georgia, Hawaii, and Massachusetts. Participants were asked to edit a sample message in which the sender shares a recent colonoscopy experience and urges the recipient to discuss colorectal cancer screening with a doctor. For those reporting willingness to send this message, changes to the message and suggested subject lines were recorded. Edited text was analyzed for content and concordance with original message. The majority of participants (74.4%) were willing to e-mail a modifiable message. Of those willing, 63.5% edited the message. Common edits included deletion (17.7%) or modification (17.4%) of a negatively framed sentence on colon cancer risks and addition or modification of personalizing words (15.6%). Few edits changed the meaning of the message (5.6%), and even fewer introduced factual inaccuracies (1.7%). Modifiable e-mail messages offer a way for screened individuals to promote colorectal cancer screening to social network members. The accuracy and effects of such messages should be further studied.

Field experiment evidence of substantive, attributional, and behavioral persuasion by members of Congress in online town halls

Do leaders persuade? Social scientists have long studied the relationship between elite behavior and mass opinion. However, there is surprisingly little evidence regarding direct persuasion by leaders. Here we show that political leaders can persuade their constituents directly on three dimensions: substantive attitudes regarding policy issues, attributions regarding the leaders' qualities, and subsequent voting behavior. We ran two randomized controlled field experiments testing the causal effects of directly interacting with a sitting politician. Our experiments consist of 20 online town hall meetings with members of Congress conducted in 2006 and 2008. Study 1 examined 19 small meetings with members of the House of Representatives (average 20 participants per town hall). Study 2 examined a large (175 participants) town hall with a senator. In both experiments we find that participating has significant and substantively important causal effects on all three dimensions of persuasion but no such effects on issues that were not discussed extensively in the sessions. Further, persuasion was not driven solely by changes in copartisans' attitudes; the effects were consistent across groups.

Small telescopes: detectability and the evaluation of replication results

This article introduces a new approach for evaluating replication results. It combines effect-size estimation with hypothesis testing, assessing the extent to which the replication results are consistent with an effect size big enough to have been detectable in the original study. The approach is demonstrated by examining replications of three well-known findings. Its benefits include the following: (a) differentiating "unsuccessful" replication attempts (i.e., studies yielding p > .05) that are too noisy from those that actively indicate the effect is undetectably different from zero, (b) "protecting" true findings from underpowered replications, and (c) arriving at intuitively compelling inferences in general and for the revisited replications in particular.

Social diffusion and global drift on networks

We study a mathematical model of social diffusion on a symmetric weighted network where individual nodes' states gradually assimilate to local social norms made by their neighbors' average states. Unlike physical diffusion, this process is not state conservational and thus the global state of the network (i.e., sum of node states) will drift. The asymptotic average node state will be the average of initial node states weighted by their strengths. Here we show that, while the global state is not conserved in this process, the inner product of strength and state vectors is conserved instead, and perfect positive correlation between node states and local averages of their self-neighbor strength ratios always results in upward (or at least neutral) global drift. We also show that the strength assortativity negatively affects the speed of homogenization. Based on these findings, we propose an adaptive link weight adjustment method to achieve the highest upward global drift by increasing the strength-state correlation. The effectiveness of the method was confirmed through numerical simulations and implications for real-world social applications are discussed.

Big other: Surveillance Capitalism and the Prospects of an Information Civilization

This article describes an emergent logic of accumulation in the networked sphere, ‘surveillance capitalism,’ and considers its implications for ‘information civilization.’ The institutionalizing practices and operational assumptions of Google Inc. are the primary lens for this analysis as they are rendered in two recent articles authored by Google Chief Economist Hal Varian. Varian asserts four uses that follow from computer-mediated transactions: data extraction and analysis,’ ‘new contractual forms due to better monitoring,’ ‘personalization and customization, ’ and continuous experiments. ’ An examination of the nature and consequences of these uses sheds light on the implicit logic of surveillance capitalism and the global architecture of computer mediation upon which it depends. This architecture produces a distributed and largely uncontested new expression of power that I christen: Big Other. ’ It is constituted by unexpected and often illegible mechanisms of extraction, commodification, and control that effectively exile persons from their own behavior while producing new markets of behavioral prediction and modification. Surveillance capitalism challenges democratic norms and departs in key ways from the centuries-long evolution of market capitalism.

Why Do Liberals Drink Lattes?

Popular accounts of "lifestyle politics" and "culture wars" suggest that political and ideological divisions extend also to leisure activities, consumption, aesthetic taste, and personal morality. Drawing on a total of 22,572 pairwise correlations from the General Social Survey (1972-2010), the authors provide comprehensive empirical support for the anecdotal accounts. Moreover, most ideological differences in lifestyle cannot be explained by demographic covariates alone. The authors propose a surprisingly simplesolution to the puzzle of lifestyle politics. Computational experiments show how the self-reinforcing dynamics of homophily and influence dramatically amplify even very small elective affinities between lifestyle and ideology, producing a stereotypical world of "latte liberals" and "bird-hunting conservatives" much like the one in which we live.

Science vs conspiracy: collective narratives in the age of misinformation

The large availability of user provided contents on online social media facilitates people aggregation around shared beliefs, interests, worldviews and narratives. In spite of the enthusiastic rhetoric about the so called collective intelligence unsubstantiated rumors and conspiracy theories—e.g., chemtrails, reptilians or the Illuminati—are pervasive in online social networks (OSN). In this work we study, on a sample of 1.2 million of individuals, how information related to very distinct narratives—i.e. main stream scientific and conspiracy news—are consumed and shape communities on Facebook. Our results show that polarized communities emerge around distinct types of contents and usual consumers of conspiracy news result to be more focused and self-contained on their specific contents. To test potential biases induced by the continued exposure to unsubstantiated rumors on users’ content selection, we conclude our analysis measuring how users respond to 4,709 troll information—i.e. parodistic and sarcastic imitation of conspiracy theories. We find that 77.92% of likes and 80.86% of comments are from users usually interacting with conspiracy stories.

Twitter chatter about marijuana

PURPOSE

We sought to examine the sentiment and themes of marijuana-related chatter on Twitter sent by influential Twitter users and to describe the demographics of these Twitter users.

METHODS

We assessed the sentiment and themes of a random sample (n = 7,000) of influential marijuana-related tweets (sent from February 5, 20114, to March 5, 2014). Demographics of the users tweeting about marijuana were inferred using a social media analytics company (Demographics Pro for Twitter).

RESULTS

Most marijuana-related tweets reflected a positive sentiment toward marijuana use, with pro-marijuana tweets outnumbering anti-marijuana tweets by a factor of greater than 15. The most common theme of pro-marijuana tweets included the Tweeter stating that he/she wants/plans to use marijuana, followed by tweeting about frequent/heavy/or regular marijuana use, and that marijuana has health benefits and/or should be legalized. Tweeters of marijuana-related content were younger and a greater proportion was African-American compared with the Twitter average.

CONCLUSIONS

Marijuana Twitter chatter sent by influential Twitter users tends to be pro-marijuana and popular among African-Americans and youth/young adults. Marijuana-related harms may afflict some individuals; therefore, our findings should be used to inform online and offline prevention efforts that work to target individuals who are most at risk for harms associated with marijuana use.

Micro-macro analysis of complex networks

Complex systems have attracted considerable interest because of their wide range of applications, and are often studied via a "classic" approach: study a specific system, find a complex network behind it, and analyze the corresponding properties. This simple methodology has produced a great deal of interesting results, but relies on an often implicit underlying assumption: the level of detail on which the system is observed. However, in many situations, physical or abstract, the level of detail can be one out of many, and might also depend on intrinsic limitations in viewing the data with a different level of abstraction or precision. So, a fundamental question arises: do properties of a network depend on its level of observability, or are they invariant? If there is a dependence, then an apparently correct network modeling could in fact just be a bad approximation of the true behavior of a complex system. In order to answer this question, we propose a novel micro-macro analysis of complex systems that quantitatively describes how the structure of complex networks varies as a function of the detail level. To this extent, we have developed a new telescopic algorithm that abstracts from the local properties of a system and reconstructs the original structure according to a fuzziness level. This way we can study what happens when passing from a fine level of detail ("micro") to a different scale level ("macro"), and analyze the corresponding behavior in this transition, obtaining a deeper spectrum analysis. The obtained results show that many important properties are not universally invariant with respect to the level of detail, but instead strongly depend on the specific level on which a network is observed. Therefore, caution should be taken in every situation where a complex network is considered, if its context allows for different levels of observability.

Social support for physical activity-role of Facebook with and without structured intervention

Despite their widespread use and extensive technical features, little is known about how to use online social networking sites to increase physical activity. This study aims to examine Facebook engagement among participants in the online social networking arm of a randomized controlled physical activity promotion trial (n = 67). Facebook communications were double coded and analyzed using ATLAS.ti. Regression procedures were used to determine predictors of Facebook use and associations between types of use and changes in perceived social support and physical activity. Changes in perceived social support and physical activity were more strongly associated with participants' individual Facebook use than use of the Facebook intervention group. The way social media sites are used in intervention design could have an impact on their effects. Including existing friends in interventions and using applications that incorporate intervention activities into a more naturalistic use of Facebook may improve the efficacy of future interventions.

Political science. When contact changes minds: an experiment on transmission of support for gay equality

Can a single conversation change minds on divisive social issues, such as same-sex marriage? A randomized placebo-controlled trial assessed whether gay (n = 22) or straight (n = 19) messengers were effective at encouraging voters (n = 972) to support same-sex marriage and whether attitude change persisted and spread to others in voters' social networks. The results, measured by an unrelated panel survey, show that both gay and straight canvassers produced large effects initially, but only gay canvassers' effects persisted in 3-week, 6-week, and 9-month follow-ups. We also find strong evidence of within-household transmission of opinion change, but only in the wake of conversations with gay canvassers. Contact with gay canvassers further caused substantial change in the ratings of gay men and lesbians more generally. These large, persistent, and contagious effects were confirmed by a follow-up experiment. Contact with minorities coupled with discussion of issues pertinent to them is capable of producing a cascade of opinion change.

Diffusion of lexical change in social media

Computer-mediated communication is driving fundamental changes in the nature of written language. We investigate these changes by statistical analysis of a dataset comprising 107 million Twitter messages (authored by 2.7 million unique user accounts). Using a latent vector autoregressive model to aggregate across thousands of words, we identify high-level patterns in diffusion of linguistic change over the United States. Our model is robust to unpredictable changes in Twitter's sampling rate, and provides a probabilistic characterization of the relationship of macro-scale linguistic influence to a set of demographic and geographic predictors. The results of this analysis offer support for prior arguments that focus on geographical proximity and population size. However, demographic similarity - especially with regard to race - plays an even more central role, as cities with similar racial demographics are far more likely to share linguistic influence. Rather than moving towards a single unified "netspeak" dialect, language evolution in computer-mediated communication reproduces existing fault lines in spoken American English.

Impact of heterogeneity and socioeconomic factors on individual behavior in decentralized sharing ecosystems

Tens of millions of individuals around the world use decentralized content distribution systems, a fact of growing social, economic, and technological importance. These sharing systems are poorly understood because, unlike in other technosocial systems, it is difficult to gather large-scale data about user behavior. Here, we investigate user activity patterns and the socioeconomic factors that could explain the behavior. Our analysis reveals that (i) the ecosystem is heterogeneous at several levels: content types are heterogeneous, users specialize in a few content types, and countries are heterogeneous in user profiles; and (ii) there is a strong correlation between socioeconomic indicators of a country and users behavior. Our findings open a research area on the dynamics of decentralized sharing ecosystems and the socioeconomic factors affecting them, and may have implications for the design of algorithms and for policymaking.

Prospect theory for online financial trading

Prospect theory is widely viewed as the best available descriptive model of how people evaluate risk in experimental settings. According to prospect theory, people are typically risk-averse with respect to gains and risk-seeking with respect to losses, known as the “reflection effect”. People are much more sensitive to losses than to gains of the same magnitude, a phenomenon called “loss aversion”. Despite of the fact that prospect theory has been well developed in behavioral economics at the theoretical level, there exist very few large-scale empirical studies and most of the previous studies have been undertaken with micro-panel data. Here we analyze over 28.5 million trades made by 81.3 thousand traders of an online financial trading community over 28 months, aiming to explore the large-scale empirical aspect of prospect theory. By analyzing and comparing the behavior of winning and losing trades and traders, we find clear evidence of the reflection effect and the loss aversion phenomenon, which are essential in prospect theory. This work hence demonstrates an unprecedented large-scale empirical evidence of prospect theory, which has immediate implication in financial trading, e.g., developing new trading strategies by minimizing the impact of the reflection effect and the loss aversion phenomenon. Moreover, we introduce three novel behavioral metrics to differentiate winning and losing traders based on their historical trading behavior. This offers us potential opportunities to augment online social trading where traders are allowed to watch and follow the trading activities of others, by predicting potential winners based on their historical trading behavior.

Harnessing Reciprocity to Promote Cooperation and the Provisioning of Public Goods

How can we maximize the common good? This is a central organizing question of public policy design, across political parties and ideologies. The answer typically involves the provisioning of public goods such as fresh air, national defense, and knowledge. Public goods are costly to produce but benefit everyone, thus creating a social dilemma: Individual and collective interests are in tension. Although individuals may want a public good to be produced, they typically would prefer not to be the ones who have to pay for it. Understanding how to motivate individuals to pay these costs is therefore of great importance for policy makers. Research provides advice on how to promote this type of “cooperative” behavior. Synthesizing a large body of research demonstrates the power of “reciprocity” for inducing cooperation: When others know that you have helped them, or acted to benefit the greater good, they are often more likely to reciprocate and help you in turn. Several conclusions stem from this line of thinking: People will be more likely to do their part when their actions are observable by others; people will pay more attention to how effective those actions are when efficacy is also observable; people will try to avoid situations where they could help, but often will help if asked directly; people are more likely to cooperate if they think others are also cooperating; and people can develop habits of cooperation that shape their default inclinations.

Internet research in psychology

Today the Internet plays a role in the lives of nearly 40% of the world's population, and it is becoming increasingly entwined in daily life. This growing presence is transforming psychological science in terms of the topics studied and the methods used. We provide an overview of the literature, considering three broad domains of research: translational (implementing traditional methods online; e.g., surveys), phenomenological (topics spawned or mediated by the Internet; e.g., cyberbullying), and novel (new ways to study existing topics; e.g., rumors). We discuss issues (e.g., sampling, ethics) that arise when doing research online and point to emerging opportunities (e.g., smartphone sensing). Psychological research on the Internet comes with new challenges, but the opportunities far outweigh the costs. By integrating the Internet, psychological research has the ability to reach large, diverse samples and collect data on actual behaviors, which will ultimately increase the impact of psychological research on society.

Integrating social networks and human social motives to achieve social influence at scale

The innovations of science often point to ideas and behaviors that must spread and take root in communities to have impact. Ideas, practices, and behaviors need to go from accepted truths on the part of a few scientists to commonplace beliefs and norms in the minds of the many. Moving from scientific discoveries to public good requires social influence. We introduce a structured influence process (SIP) framework to explain how social networks (i.e., the structure of social influence) and human social motives (i.e., the process of social influence wherein one person's attitudes and behaviors affect another's) are used collectively to enact social influence within a community. The SIP framework advances the science of scientific communication by positing social influence events that consider both the "who" and the "how" of social influence. This framework synthesizes core ideas from two bodies of research on social influence. The first is network research on social influence structures, which identifies who are the opinion leaders and who among their network of peers shapes their attitudes and behaviors. The second is research on social influence processes in psychology, which explores how human social motives such as the need for accuracy or the need for affiliation stimulate behavior change. We illustrate the practical implications of the SIP framework by applying it to the case of reducing neonatal mortality in India.

Social networks and health: a systematic review of sociocentric network studies in low- and middle-income countries

In low- and middle-income countries (LMICs), naturally occurring social networks may be particularly vital to health outcomes as extended webs of social ties often are the principal source of various resources. Understanding how social network structure, and influential individuals within the network, may amplify the effects of interventions in LMICs, by creating, for example, cascade effects to non-targeted participants, presents an opportunity to improve the efficiency and effectiveness of public health interventions in such settings. We conducted a systematic review of PubMed, Econlit, Sociological Abstracts, and PsycINFO to identify a sample of 17 sociocentric network papers (arising from 10 studies) that specifically examined health issues in LMICs. We also separately selected to review 19 sociocentric network papers (arising from 10 other studies) on development topics related to wellbeing in LMICs. First, to provide a methodological resource, we discuss the sociocentric network study designs employed in the selected papers, and then provide a catalog of 105 name generator questions used to measure social ties across all the LMIC network papers (including both ego- and sociocentric network papers) cited in this review. Second, we show that network composition, individual network centrality, and network structure are associated with important health behaviors and health and development outcomes in different contexts across multiple levels of analysis and across distinct network types. Lastly, we highlight the opportunities for health researchers and practitioners in LMICs to 1) design effective studies and interventions in LMICs that account for the sociocentric network positions of certain individuals and overall network structure, 2) measure the spread of outcomes or intervention externalities, and 3) enhance the effectiveness and efficiency of aid based on knowledge of social structure. In summary, human health and wellbeing are connected through complex webs of dynamic social relationships. Harnessing such information may be especially important in contexts where resources are limited and people depend on their direct and indirect connections for support.

A systematic review protocol: social network analysis of tobacco use

BACKGROUND

Tobacco use is the single most preventable cause of death in the world. Evidence indicates that behaviours such as tobacco use can influence social networks, and that social network structures can influence behaviours. Social network analysis provides a set of analytic tools to undertake methodical analysis of social networks. We will undertake a systematic review to provide a comprehensive synthesis of the literature regarding social network analysis and tobacco use. The review will answer the following research questions: among participants who use tobacco, does social network structure/position influence tobacco use? Does tobacco use influence peer selection? Does peer selection influence tobacco use?

METHODS

We will follow the Preferred Reporting Items for Systemic Reviews and Meta-Analyses (PRISMA) guidelines and search the following databases for relevant articles: CINAHL (Cumulative Index to Nursing and Allied Health Literature); Informit Health Collection; PsycINFO; PubMed/MEDLINE; Scopus/Embase; Web of Science; and the Wiley Online Library. Keywords include tobacco; smoking; smokeless; cigarettes; cigar and 'social network' and reference lists of included articles will be hand searched. Studies will be included that provide descriptions of social network analysis of tobacco use.Qualitative, quantitative and mixed method data that meets the inclusion criteria for the review, including methodological rigour, credibility and quality standards, will be synthesized using narrative synthesis. Results will be presented using outcome statistics that address each of the research questions.

DISCUSSION

This systematic review will provide a timely evidence base on the role of social network analysis of tobacco use, forming a basis for future research, policy and practice in this area. This systematic review will synthesise the evidence, supporting the hypothesis that social network structures can influence tobacco use. This will also include exploring the relationship between social network structure, social network position, peer selection, peer influence and tobacco use across all age groups, and across different demographics. The research will increase our understanding of social networks and their impact on tobacco use, informing policy and practice while highlighting gaps in the literature and areas for further research.

Social network analysis predicts health behaviours and self-reported health in African villages

The provision of healthcare in rural African communities is a highly complex and largely unsolved problem. Two main difficulties are the identification of individuals that are most likely affected by disease and the prediction of responses to health interventions. Social networks have been shown to capture health outcomes in a variety of contexts. Yet, it is an open question as to what extent social network analysis can identify and distinguish among households that are most likely to report poor health and those most likely to respond to positive behavioural influences. We use data from seven highly remote, post-conflict villages in Liberia and compare two prominent network measures: in-degree and betweenness. We define in-degree as the frequency in which members from one household are named by another household as a friends. Betweenness is defined as the proportion of shortest friendship paths between any two households in a network that traverses a particular household. We find that in-degree explains the number of ill family members, whereas betweenness explains engagement in preventative health. In-degree and betweenness independently explained self-reported health and behaviour, respectively. Further, we find that betweenness predicts susceptibility to, instead of influence over, good health behaviours. The results suggest that targeting households based on network measures rather than health status may be effective for promoting the uptake of health interventions in rural poor villages.

A simple generative model of collective online behavior

Human activities increasingly take place in online environments, providing novel opportunities for relating individual behaviors to population-level outcomes. In this paper, we introduce a simple generative model for the collective behavior of millions of social networking site users who are deciding between different software applications. Our model incorporates two distinct mechanisms: one is associated with recent decisions of users, and the other reflects the cumulative popularity of each application. Importantly, although various combinations of the two mechanisms yield long-time behavior that is consistent with data, the only models that reproduce the observed temporal dynamics are those that strongly emphasize the recent popularity of applications over their cumulative popularity. This demonstrates--even when using purely observational data without experimental design--that temporal data-driven modeling can effectively distinguish between competing microscopic mechanisms, allowing us to uncover previously unidentified aspects of collective online behavior.

The perception of nanotechnology and nanomedicine: a worldwide social media study

We explore at a world level the awareness of nanotechnology expressed through the most popular online social media: Facebook. We aimed at identifying future trends, the most interested countries and the public perception of ethics, funding and economic issues. We found that graphene and carbon nanotubes are the most followed nanomaterials. Our poll showed that the continents with the most interest are Asia and Africa. A total of 43% would like to have a world commission regulating nanomedicine. In addition, 43% would give priority to theranostics. Over 90% believe that nanomedicine has an economic impact. Finally, we observed that the continents of living and origin of poll contributors correlated with ethic and funding opinions. This study highlights the potential of online social media to influence scientific communities, grant committees and nanotechnology companies, spreading nanotechnology awareness in emerging countries and among new generations.