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285
Maximizing the Spread of Influence Through a Social Network
- In KDD
, 2003
"... Models for the processes by which ideas and influence propagate through a social network have been studied in a number of domains, including the diffusion of medical and technological innovations, the sudden and widespread adoption of various strategies in game-theoretic settings, and the effects of ..."
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Cited by 990 (7 self)
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Models for the processes by which ideas and influence propagate through a social network have been studied in a number of domains, including the diffusion of medical and technological innovations, the sudden and widespread adoption of various strategies in game-theoretic settings, and the effects of “word of mouth ” in the promotion of new products. Recently, motivated by the design of viral marketing strategies, Domingos and Richardson posed a fundamental algorithmic problem for such social network processes: if we can try to convince a subset of individuals to adopt a new product or innovation, and the goal is to trigger a large cascade of further adoptions, which set of individuals should we target? We consider this problem in several of the most widely studied models in social network analysis. The optimization problem of selecting the most influential nodes is NP-hard here, and we provide the first provable approximation guarantees for efficient algorithms. Using an analysis framework based on submodular functions, we show that a natural greedy strategy obtains a solution that is provably within 63 % of optimal for several classes of models; our framework suggests a general approach for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks. We also provide computational experiments on large collaboration networks, showing that in addition to their provable guarantees, our approximation algorithms significantly out-perform nodeselection heuristics based on the well-studied notions of degree centrality and distance centrality from the field of social networks.
Influential Nodes in a Diffusion Model for Social Networks
- IN ICALP
, 2005
"... We study the problem of maximizing the expected spread of an innovation or behavior within a social network, in the presence of "word-of-mouth" referral. Our work builds on the observation that individuals' decisions to purchase a product or adopt an innovation are strongly influe ..."
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Cited by 152 (3 self)
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We study the problem of maximizing the expected spread of an innovation or behavior within a social network, in the presence of "word-of-mouth" referral. Our work builds on the observation that individuals' decisions to purchase a product or adopt an innovation are strongly influenced by recommendations from their friends and acquaintances. Understanding
Socially embedded consumer transactions: For what kinds of purposes do people use networks most?” American Sociological Review, 63: 619-637
- Journal of Economics
, 1998
"... Support to NORC for the collection of these data from the Lilly ..."
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Cited by 109 (1 self)
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Support to NORC for the collection of these data from the Lilly
Searching Social Networks
, 2003
"... A referral system is a multiagent system whose member agents are capable of giving and following referrals. The specific cases of interest arise where each agent has a user. The agents cooperate by giving and taking referrals so each can better help its user locate relevant information. This use of ..."
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Cited by 102 (7 self)
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A referral system is a multiagent system whose member agents are capable of giving and following referrals. The specific cases of interest arise where each agent has a user. The agents cooperate by giving and taking referrals so each can better help its user locate relevant information. This use of referrals mimics human interactions and can potentially lead to greater effectiveness and efficiency than in single-agent systems. Existing approaches consider what referrals may be given and treat the referring process simply as path search in a static graph. By contrast, the present approach understands referrals as arising in and influencing dynamic social networks, where the agents act autonomously based on local knowledge. This paper studies strategies using which agents may search dynamic social networks. It evaluates the proposed approach empirically for a community of AI scientists (partially derived from bibliographic data). Further, it presents a prototype system that assists users in finding other users in practical social networks.
Examining the Relationship between Reviews and Sales: The Role of Reviewer Identity
- Disclosure in Electronic Markets, NYU CeDER Working Paper
, 2006
"... doi 10.1287/isre.1080.0193 ..."
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Creating Social Contagion Through Viral Product Design Management Science 57(9
- 2011 INFORMS 1639 Epsilon. 2009. Epsilon Q1 2009 email trends and
"... We examine how firms can create word-of-mouth peer influence and social contagion by designing viralfeatures into their products and marketing campaigns. To econometrically identify the effectiveness of different viral features in creating social contagion, we designed and conducted a randomized fie ..."
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Cited by 88 (3 self)
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We examine how firms can create word-of-mouth peer influence and social contagion by designing viralfeatures into their products and marketing campaigns. To econometrically identify the effectiveness of different viral features in creating social contagion, we designed and conducted a randomized field experiment involving the 1.4 million friends of 9,687 experimental users on Facebook.com. We find that viral features generate econometrically identifiable peer influence and social contagion effects. More surprisingly, we find that passive-broadcast viral features generate a 246 % increase in peer influence and social contagion, whereas adding active-personalized viral features generate only an additional 98 % increase. Although active-personalized viral messages are more effective in encouraging adoption per message and are correlated with more user engagement and sustained product use, passive-broadcast messaging is used more often, generating more total peer adoption in the network. Our work provides a model for how randomized trials can identify peer influence in social networks. Key words: peer influence; social contagion; social networks; viral marketing; viral product design; information systems; randomized experiment
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.
Modeling Information Diffusion in Implicit Networks
"... Abstract—Social media forms a central domain for the production and dissemination of real-time information. Even though such flows of information have traditionally been thought of as diffusion processes over social networks, the underlying phenomena are the result of a complex web of interactions a ..."
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Cited by 83 (2 self)
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Abstract—Social media forms a central domain for the production and dissemination of real-time information. Even though such flows of information have traditionally been thought of as diffusion processes over social networks, the underlying phenomena are the result of a complex web of interactions among numerous participants. Here we develop a Linear Influence Model where rather than requiring the knowledge of the social network and then modeling the diffusion by predicting which node will influence which other nodes in the network, we focus on modeling the global influence of a node on the rate of diffusion through the (implicit) network. We model the number of newly infected nodes as a function of which other nodes got infected in the past. For each node we estimate an influence function that quantifies how many subsequent infections can be attributed to the influence of that node over time. A nonparametric formulation of the model leads to a simple least squares problem that can be solved on large datasets. We validate our model on a set of 500 million tweets and a set of 170 million news articles and blog posts. We show that the Linear Influence Model accurately models influences of nodes and reliably predicts the temporal dynamics of information diffusion. We find that patterns of influence of individual participants differ significantly depending on the type of the node and the topic of the information. I.
Exploring the Value of Online Product Reviews in Forecasting Sales: The Case of Motion Pictures
"... The growing popularity of online product review forums invites the development of models and metrics that allow firms to harness these new sources of information for decision support. Our work contributes in this direction by proposing a novel family of diffusion models that capture some of the uniq ..."
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Cited by 83 (2 self)
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The growing popularity of online product review forums invites the development of models and metrics that allow firms to harness these new sources of information for decision support. Our work contributes in this direction by proposing a novel family of diffusion models that capture some of the unique aspects of the entertainment industry and testing their performance in the context of very early post-release motion picture revenue forecasting. We show that the addition of online product review metrics to a benchmark model that includes pre-release marketing, theater availability and professional critic reviews substantially increases its forecasting accuracy; the forecasting accuracy of our best model outperforms that of several previously published models. In addition to its contributions in diffusion theory our study reconciles some inconsistencies among previous studies with respect to what online review metrics are statistically significant in forecasting entertainment good sales. Furthermore, it demonstrates the value of online product review metrics for purposes other than assessing consumer sentiment about a product. For example, we show that the early volume of online reviews provides a good proxy of early box office sales, whereas metrics of online reviewer demographics provide useful indications regarding a movie’s breadth of appeal across different population segments.
We are what we post? Self-presentation in personal web space
- Journal of Consumer Research
, 2003
"... This article examines personal Web sites as a conspicuous form of consumer self-presentation. Using theories of self-presentation, possessions, and computer-medi-ated environments (CMEs), we investigate the ways in which consumers construct identities by digitally associating themselves with signs, ..."
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Cited by 72 (2 self)
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This article examines personal Web sites as a conspicuous form of consumer self-presentation. Using theories of self-presentation, possessions, and computer-medi-ated environments (CMEs), we investigate the ways in which consumers construct identities by digitally associating themselves with signs, symbols, material objects, and places. Specifically, the issues of interest include why consumers createpersonal Web sites, what consumers want to communicate, what strategies they devise to achieve their goal of self-presentation, and how those Web space strategies compare to the self-presentation strategies of real life (RL). The data reveal insights into the strategies behind constructing a digital self, projecting a digital likeness, digitally associating as a new form of possession, and reorganizing linear narrativestructures. Consumption can be a self-defining and self-expressivebehavior. People often choose products and brands that are self-relevant and communicate a given identity: “Con-sumption serves to produce a desired self through the images and styles conveyed through one’s possessions ” (Thompson and Hirschman 1995, p. 151). In this way consumers make their identities tangible, or self-present, by associating them-selves with material objects and places. Although consumer researchers have included symbols and signs in the set of objects and materiality they study (Mick 1986), even these symbols often refer to physical objects or places. With the advent of new technology, computer-mediated environments (CMEs) have emerged, allowing virtual worlds in which consumers can present themselves using digital rather than physical referents. The CMEs are virtual digital places that occupy neither space nor time. They are inherently discursive spaces where people actively convene to commune with others (Kozinets
