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The Role of Social Networks in Information Diffusion
, 2012
"... Online social networking technologies enable individuals to simultaneously share information with any number of peers. Quantifying the causal effect of these mediums on the dissemination of information requires not only identification of who influences whom, but also of whether individuals would sti ..."
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Cited by 87 (3 self)
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Online social networking technologies enable individuals to simultaneously share information with any number of peers. Quantifying the causal effect of these mediums on the dissemination of information requires not only identification of who influences whom, but also of whether individuals would still propagate information in the absence of social signals about that information. We examine the role of social networks in online information diffusion with a large-scale field experiment that randomizes exposure to signals about friends ’ information sharing among 253 million subjects in situ. Those who are exposed are significantly more likely to spread information, and do so sooner than those who are not exposed. We further examine the relative role of strong and weak ties in information propagation. We show that, although stronger ties are individually more influential, it is the more abundant weak ties who are responsible for the propagation of novel information. This suggests that weak ties may play a more dominant role in the dissemination of information online than currently believed.
The Structure of Online Diffusion Networks
, 2012
"... Models of networked diffusion that are motivated by analogy with the spread of infectious disease have been applied to a wide range of social and economic adoption processes, including those related to new products, ideas, norms and behaviors. However, it is unknown how accurately these models accou ..."
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Cited by 37 (2 self)
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Models of networked diffusion that are motivated by analogy with the spread of infectious disease have been applied to a wide range of social and economic adoption processes, including those related to new products, ideas, norms and behaviors. However, it is unknown how accurately these models account for the empirical structure of diffusion over networks. Here we describe the diffusion patterns arising from seven online domains, ranging from communications platforms to networked games to microblogging services, each involving distinct types of content and modes of sharing. We find strikingly similar patterns across all domains. In particular, the vast majority of cascades are small, and are described by a handful of simple tree structures that terminate within one degree of an initial adopting “seed. ” In addition we find that structures other than these account for only a tiny fraction of total adoptions; that is, adoptions resulting from chains of referrals are extremely rare. Finally, even for the largest cascades that we observe, we find that the bulk of adoptions often takes place within one degree of a few dominant individuals. Together, these observations suggest new directions for modeling of online adoption processes.
Diffusion, Strategic Interaction, and Social Structure
, 2008
"... How we act, as well as how we are acted upon, are to a large extent influenced by our relatives, friends and acquaintances. This is true of which profession we decide to pursue, whether or not we adopt a new technology, as well as whether or not we catch the flu. In this chapter we provide an overvi ..."
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Cited by 17 (1 self)
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How we act, as well as how we are acted upon, are to a large extent influenced by our relatives, friends and acquaintances. This is true of which profession we decide to pursue, whether or not we adopt a new technology, as well as whether or not we catch the flu. In this chapter we provide an overview of research that examines how social structure impacts
Virality prediction and community structure in social networks
- Nature Scientific Reports
"... How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily. Hence, the spread within highly clustered communities is enha ..."
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Cited by 12 (3 self)
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How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily. Hence, the spread within highly clustered communities is enhanced, while diffusion across communities is hampered. A common hypothesis is that memes and behaviors are complex contagions. We show that, while most memes indeed spread like complex contagions, a few viral memes spread across many communities, like diseases. We demonstrate that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration. The more communities a meme permeates, the more viral it is. We present a practical method to translate data about community structure into predictive knowledge about what information will spread widely. This connection contributes to our understanding in computational social science, social media analytics, and marketing applications. D iseases, ideas, innovations, and behaviors spread through social networks 22 , and social media analytics Community structure has been shown to affect information diffusion, including global cascades
Estimating average causal effects under general interference. Citeseer
, 2012
"... Abstract This paper presents randomization-based methods for estimating average causal effects under arbitrary interference of known form. Conservative estimators of the randomization variance of the average treatment effects estimators are presented, as is justification for confidence intervals ba ..."
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Cited by 11 (0 self)
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Abstract This paper presents randomization-based methods for estimating average causal effects under arbitrary interference of known form. Conservative estimators of the randomization variance of the average treatment effects estimators are presented, as is justification for confidence intervals based on a normal approximation. Examples relevant to research in environmental protection, networks experiments, "viral marketing," two-stage disease prophylaxis trials, and stepped-wedge designs are presented.
Transforming Graph Data for Statistical Relational Learning
, 2012
"... Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of Statistical Relational Learning (SRL) algorithms to these domains. In th ..."
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Cited by 10 (4 self)
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Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of Statistical Relational Learning (SRL) algorithms to these domains. In this article, we examine and categorize techniques for transforming graph-based relational data to improve SRL algorithms. In particular, appropriate transformations of the nodes, links, and/or features of the data can dramatically affect the capabilities and results of SRL algorithms. We introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. More specifically, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) systematically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey competing approaches for each of these tasks. We also discuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed.
The Role of Social Networks in Information Diffusion
, 2012
"... Online social networking technologies enable individuals to simultaneously share information with any number of peers. Quantifying the causal effect of these mediums on the dissemination of information requires not only identification of who influences whom, but also of whether individuals would sti ..."
Abstract
-
Cited by 6 (0 self)
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Online social networking technologies enable individuals to simultaneously share information with any number of peers. Quantifying the causal effect of these mediums on the dissemination of information requires not only identification of who influences whom, but also of whether individuals would still propagate information in the absence of social signals about that information. We examine the role of social networks in online information diffusion with a large-scale field experiment that randomizes exposure to signals about friends ’ information sharing among 253 million subjects in situ. Those who are exposed are significantly more likely to spread information, and do so sooner than those who are not exposed. We further examine the relative role of strong and weak ties in information propagation. We show that, although stronger ties are individually more influential, it is the more abundant weak ties who are responsible for the propagation of novel information. This suggests that weak ties may play a more dominant role in the dissemination of information online than currently believed.
Do linguistic style and readability of scientific abstracts affect their virality
- Proceedings of ICWSM-12
, 2012
"... ar ..."
Active Social Media Management: The Case of Health
, 2012
"... Given the demand for authentic personal interactions over social media, it is unclear how much firms should actively try and engage with consumers using social media. We investigate empirically what drives the degree of engagement that healthcare organizations generate by actively managing their soc ..."
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Cited by 6 (1 self)
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Given the demand for authentic personal interactions over social media, it is unclear how much firms should actively try and engage with consumers using social media. We investigate empirically what drives the degree of engagement that healthcare organizations generate by actively managing their social media presence. We find that active management of social media is more likely to generate incremental engagement from an organization’s employees than its clients. In other words, active management of social media by an organization seems more successful at boosting internal engagement than external engagement. This result holds when we explore exogenous variation in a firm’s relationships with its employees and clients that are explained by medical malpractice laws and distortions in Medicare incentives.
1 The effects of rewarding user engagement – The case of Facebook apps
"... We study the market for apps on Facebook, the dominant social networking platform, and make use of a rule change by Facebook by which highly engaging apps were rewarded with further opportunities to engage users. The rule change led to new applications with significantly higher user ratings being de ..."
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Cited by 5 (0 self)
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We study the market for apps on Facebook, the dominant social networking platform, and make use of a rule change by Facebook by which highly engaging apps were rewarded with further opportunities to engage users. The rule change led to new applications with significantly higher user ratings being developed. Moreover, user ratings became more important drivers of app success. Other drivers of app success are also affected by the rule change; sheer network size became a less important driver for app success, update frequency benefitted apps more in staying successful, and active users of Facebook apps declined less rapidly with age. Our results show that social media channels do not necessarily have to be managed through hard exclusion of participants, but can also be steered through “softer ” changes in reward and incentive systems.
