Identity effects in social media.

Identity cues appear ubiquitously alongside content in social media today. Some also suggest universal identification, with names and other cues, as a useful deterrent to harmful behaviours online. Unfortunately, we know little about the effects of identity cues on opinions and online behaviours. Here we used a large-scale longitudinal field experiment to estimate the extent to which identity cues affect how people form opinions about and interact with content online. We randomly assigned content produced on a social news aggregation website to 'identified' and 'anonymous' conditions to estimate the causal effect of identity cues on how viewers vote and reply to content. The effects of identity cues were significant and heterogeneous, accounting for between 28% and 61% of the variation in voting associated with commenters' production, reputation and reciprocity. Our results also showed that identity cues cause people to vote on content faster (consistent with heuristic processing) and to vote according to content producers' reputations, production history and reciprocal votes with content viewers. These results provide evidence that rich-get-richer dynamics and inequality in social content evaluation are mediated by identity cues. They also provide insights into the evolution of status in online communities. From a practical perspective, we show via simulation that social platforms may improve content quality by including votes on anonymized content as a ranking signal.

Initial growth rates of malware epidemics fail to predict their reach.

Empirical studies show that epidemiological models based on an epidemic's initial spread rate often fail to predict the true scale of that epidemic. Most epidemics with a rapid early rise die out before affecting a significant fraction of the population, whereas the early pace of some pandemics is rather modest. Recent models suggest that this could be due to the heterogeneity of the target population's susceptibility. We study a computer malware ecosystem exhibiting spread mechanisms resembling those of biological systems while offering details unavailable for human epidemics. Rather than comparing models, we directly estimate reach from a new and vastly more complete data from a parallel domain, that offers superior details and insight as concerns biological outbreaks. We find a highly heterogeneous distribution of computer susceptibilities, with nearly all outbreaks initially over-affecting the tail of the distribution, then collapsing quickly once this tail is depleted. This mechanism restricts the correlation between an epidemic's initial growth rate and its total reach, thus preventing the majority of epidemics, including initially fast-growing outbreaks, from reaching a macroscopic fraction of the population. The few pervasive malwares distinguish themselves early on via the following key trait: they avoid infecting the tail, while preferentially targeting computers unaffected by typical malware.

Realistic modelling of information spread using peer-to-peer diffusion patterns.

In computational social science, epidemic-inspired spread models have been widely used to simulate information diffusion. However, recent empirical studies suggest that simple epidemic-like models typically fail to generate the structure of real-world diffusion trees. Such discrepancy calls for a better understanding of how information spreads from person to person in real-world social networks. Here, we analyse comprehensive diffusion records and associated social networks in three distinct online social platforms. We find that the diffusion probability along a social tie follows a power-law relationship with the numbers of disseminator's followers and receiver's followees. To develop a more realistic model of information diffusion, we incorporate this finding together with a heterogeneous response time into a cascade model. After adjusting for observational bias, the proposed model reproduces key structural features of real-world diffusion trees across the three platforms. Our finding provides a practical approach to designing more realistic generative models of information diffusion.

Exploring the complex pattern of information spreading in online blog communities.

Information spreading in online social communities has attracted tremendous attention due to its utmost practical values in applications. Despite that several individual-level diffusion data have been investigated, we still lack the detailed understanding of the spreading pattern of information. Here, by comparing information flows and social links in a blog community, we find that the diffusion processes are induced by three different spreading mechanisms: social spreading, self-promotion and broadcast. Although numerous previous studies have employed epidemic spreading models to simulate information diffusion, we observe that such models fail to reproduce the realistic diffusion pattern. In respect to users behaviors, strikingly, we find that most users would stick to one specific diffusion mechanism. Moreover, our observations indicate that the social spreading is not only crucial for the structure of diffusion trees, but also capable of inducing more subsequent individuals to acquire the information. Our findings suggest new directions for modeling of information diffusion in social systems, and could inform design of efficient propagation strategies based on users behaviors.

Searching for superspreaders of information in real-world social media.

A number of predictors have been suggested to detect the most influential spreaders of information in online social media across various domains such as Twitter or Facebook. In particular, degree, PageRank, k-core and other centralities have been adopted to rank the spreading capability of users in information dissemination media. So far, validation of the proposed predictors has been done by simulating the spreading dynamics rather than following real information flow in social networks. Consequently, only model-dependent contradictory results have been achieved so far for the best predictor. Here, we address this issue directly. We search for influential spreaders by following the real spreading dynamics in a wide range of networks. We find that the widely-used degree and PageRank fail in ranking users' influence. We find that the best spreaders are consistently located in the k-core across dissimilar social platforms such as Twitter, Facebook, Livejournal and scientific publishing in the American Physical Society. Furthermore, when the complete global network structure is unavailable, we find that the sum of the nearest neighbors' degree is a reliable local proxy for user's influence. Our analysis provides practical instructions for optimal design of strategies for "viral" information dissemination in relevant applications.

Edge removal balances preferential attachment and triad closing.

Most network formation analysis studies are centered on edge addition. However, edges in real world networks often have a rapid turnover with a large number of edges added and removed between each node addition or removal steps. In such a case, quasiequilibrium is obtained between edge addition and deletion. Edges have been shown to be added to nodes with a high degree and between pairs of nodes with a high number of common neighbors. If not balanced by a degree dependent edge removal, the preference for high degree nodes and node pairs with many common neighbors is expected to increase the average degree of high degree nodes and their clustering coefficient until very large cliques will be formed. Since such large cliques are not formed in real world networks, we conclude that the edge removal probability around high degree nodes and between node pairs with many common neighbors should be higher than around other nodes. We here show the existence of such a balancing mechanism through the relation between the future edge removal probability around nodes and their degree and a similar relation between the edge removal probability and the number of common neighbors of node pairs. In some networks, this preferential detachment process represents an explicit saturation process, and in others, it represents a random deletion process accompanied by a sublinear edge preferential attachment process. A more complex mechanism emerges in directed networks where the preferential detachment can be proportional to the in and out degrees of the nodes involved. In such networks, preferential detachment is stronger for the incoming edges than for the outgoing edges. We hypothesize multiple possible mechanisms that could explain this phenomenon.

Social influence bias: a randomized experiment.

Our society is increasingly relying on the digitized, aggregated opinions of others to make decisions. We therefore designed and analyzed a large-scale randomized experiment on a social news aggregation Web site to investigate whether knowledge of such aggregates distorts decision-making. Prior ratings created significant bias in individual rating behavior, and positive and negative social influences created asymmetric herding effects. Whereas negative social influence inspired users to correct manipulated ratings, positive social influence increased the likelihood of positive ratings by 32% and created accumulating positive herding that increased final ratings by 25% on average. This positive herding was topic-dependent and affected by whether individuals were viewing the opinions of friends or enemies. A mixture of changing opinion and greater turnout under both manipulations together with a natural tendency to up-vote on the site combined to create the herding effects. Such findings will help interpret collective judgment accurately and avoid social influence bias in collective intelligence in the future.

Origins of power-law degree distribution in the heterogeneity of human activity in social networks.

The probability distribution of number of ties of an individual in a social network follows a scale-free power-law. However, how this distribution arises has not been conclusively demonstrated in direct analyses of people's actions in social networks. Here, we perform a causal inference analysis and find an underlying cause for this phenomenon. Our analysis indicates that heavy-tailed degree distribution is causally determined by similarly skewed distribution of human activity. Specifically, the degree of an individual is entirely random - following a "maximum entropy attachment" model - except for its mean value which depends deterministically on the volume of the users' activity. This relation cannot be explained by interactive models, like preferential attachment, since the observed actions are not likely to be caused by interactions with other people.

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.

Self-emergence of knowledge trees: extraction of the Wikipedia hierarchies.

The rapid accumulation of knowledge and the recent emergence of new dynamic and practically unmoderated information repositories have rendered the classical concept of the hierarchal knowledge structure irrelevant and impossible to impose manually. This led to modern methods of data location, such as browsing or searching, which conceal the underlying information structure. We here propose methods designed to automatically construct a hierarchy from a network of related terms. We apply these methods to Wikipedia and compare the hierarchy obtained from the article network to the complementary acyclic category layer of the Wikipedia and show an excellent fit. We verify our methods in two networks with no a priori hierarchy (the E. Coli genetic regulatory network and the C. Elegans neural network) and a network of function libraries of modern computer operating systems that are intrinsically hierarchical and reproduce a known functional order.

Copying nodes versus editing links: the source of the difference between genetic regulatory networks and the WWW.

UNLABELLED: We study two kinds of networks: genetic regulatory networks and the World Wide Web. We systematically test microscopic mechanisms to find the set of such mechanisms that optimally explain each networks' specific properties. In the first case we formulate a model including mainly random unbiased gene duplications and mutations. In the second case, the basic moves are website generation and rapid surf-induced link creation (/destruction). The different types of mechanisms reproduce the appropriate observed network properties. We use those to show that different kinds of networks have strongly system-dependent macroscopic experimental features. The diverging properties result from dissimilar node and link basic dynamics. The main non-uniform properties include the clustering coefficient, small-scale motifs frequency, time correlations, centrality and the connectivity of outgoing links. Some other features are generic such as the large-scale connectivity distribution of incoming links (scale-free) and the network diameter (small-worlds). The common properties are just the general hallmark of autocatalysis (self-enhancing processes), while the specific properties hinge on the specific elementary mechanisms. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Online.

Diagnosis of thin-cap fibroatheromas by a self-contained intravascular magnetic resonance imaging probe in ex vivo human aortas and in situ coronary arteries.

OBJECTIVES: We sought to correlate findings obtained from a self-contained magnetic resonance imaging (MRI) probe with plaque morphology of ex vivo human aortas and coronary arteries. BACKGROUND: Early detection of thin-cap fibroatheromas (TCFAs) may allow for early preventive treatment of acute coronary syndromes. We developed an intravascular MRI catheter capable of imaging the arterial wall without external magnets or coils by differentiating lipid-rich and fibrotic-rich areas of the atherosclerotic plaque on the basis of differential water diffusion. METHODS: Aortic samples (n = 16) and coronary arteries were obtained within 12 h of death. Coronary specimens were intermediate in angiographic severity (30% to 60% luminal narrowing, n = 18). Blinded histologic and immunohistochemical analyses of the tissues were performed and correlated to MRI findings. RESULTS: The 16 aortic lesions included four ulcerated plaques, two TCFAs, two thick-cap fibrous atheromas, two intimal xanthomas, and six adaptive intimal thickenings. The MRI scan correctly correlated with the histologic diagnosis in 15 (94%) of 16 lesions. The 18 coronary lesions included one plaque rupture, three TFCAs, seven thick-cap fibrous atheromas, four fibrocalcific plaques, two intimal xanthomas, and one adaptive intimal thickening. The MRI scan correlated with the histologic diagnosis in 16 of 18 lesions (sensitivity 100%, specificity 89%). CONCLUSIONS: The self-contained intravascular MRI catheter successfully identified TCFA and may prove to be an important diagnostic approach to determining the presence of lesions with increased risk of causing death or myocardial infarction.

Scaling and memory in volatility return intervals in financial markets.

For both stock and currency markets, we study the return intervals tau between the daily volatilities of the price changes that are above a certain threshold q. We find that the distribution function Pq(tau) scales with the mean return interval tau as Pq(tau)=tau(-1)f(tau/tau). The scaling function fx is similar in form for all seven stocks and for all seven currency databases analyzed, and fx is consistent with a power-law form, fx approximately x(-gamma) with gamma approximately 2. We also quantify how the conditional distribution Pq(tau/tau0) depends on the previous return interval tau0 and find that small (or large) return intervals are more likely to be followed by small (or large) return intervals. This "clustering" of the volatility return intervals is a previously unrecognized phenomenon that we relate to the long-term correlations known to be present in the volatility.

Miniature self-contained intravascular magnetic resonance (IVMI) probe for clinical applications.

A miniature (1.73 mm in diameter) NMR probe, which contains a magnet and a radiofrequency (RF) coil, is presented. This probe is integrated at the tip of a standard catheter and can be inserted into the human coronary arteries, creating local magnetic fields needed to obtain the NMR signal from the blood vessel walls, without the need for external magnet or RF coils. The basic theory governing the probe performance in terms of signal-to-noise-ratio and contrast parameters is presented, along with measured results from test samples. The NMR signal can be analyzed to obtain tissue contrast parameters such as T1, T2 and the diffusion coefficient, which may be used to detect lipid-rich vulnerable plaques in the coronary arteries.