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Affinity Paths and Information Diffusion in Social Networks

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Abstract

Widespread interest in the diffusion of information through social networks has produced a large number of Social Dynamics models. A majority of them use theoretical hypothesis to explain their diffusion mechanisms while the few empirically based ones average out their measures over many messages of different content. Our empirical research tracking the step-by-step email propagation of an invariable viral marketing message delves into the content impact and has discovered new and striking features. The topology and dynamics of the propagation cascades display patterns not inherited from the email networks carrying the message. Their disconnected, low transitivity, tree-like cascades present positive correlation between their nodes probability to forward the message and the average number of neighbors they target and show increased participants' involvement as the propagation paths length grows. Such patterns not described before, nor replicated by any of the existing models of information diffusion, can be explained if participants make their pass-along decisions based uniquely on local knowledge of their network neighbors affinity with the message content. We prove the plausibility of such mechanism through a stylized, agent-based model that replicates the \emph{Affinity Paths} observed in real information diffusion cascades.

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... However, the dominant paradigm is rooted in a dyadic approach to studying relationships and overlooks the complex connections among organizations and stakeholders (O'Connor and Shumate, 2018;Yang and Saffer, 2019). The dyadic approach is increasingly inadequate and outdated in the networked society where relationships and networks are ubiquitous (Iribarren and Moro, 2011). Yang and Taylor (2015) applied a network approach to re-conceptualize relationship management as a strategic network management. ...
... Community network positions. In networks, actors do not randomly connect with others; rather, the process of social selection creates clusters of small groups, cliques, and subnetworks (Iribarren and Moro, 2011). The small clusters are the microstructures within a larger social network that are the basic building blocks of communities. ...
... The last component of the network contingency model of public attention is the concept of strategic contingency, which refers to the idea that actors' communication outcomes in a networked communication context are contingent upon the fit between actors' network positions and the network structure. In this study we draw upon the literature on information diffusion in networks (Iribarren and Moro, 2011;Morales et al., 2014;Spitzberg, 2014) and propose two sets of conditions that would lead to strategic fit. ...
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... With the rapid development of the Internet, especially the development of online social networks, such as SinaWeibo, 1 Twitter, 2 Facebook, 3 which have led to revolutionary changes in people's manners of access and disseminate information. ...
... The emergence of online social networks and other social media have changed the traditional ways of information dissemination, while online social networks now gradually become mainstream platforms for information post and dissemination. Using online social platforms to post and disseminate information can establish a huge interpersonal communication network, which can strengthen the human-to-human interaction that further promote the diffusion of information and in turn affects the users interpersonal relationships, and thus forming an interactive-feedback mechanism; besides, interpersonal relationships and information dissemination meet a dynamic co-evolution situation over time [1][2][3][4][5]. ...
... (4) The significance profile of cascade ratio of IDPs depicted in lower part of Fig. 11 further proves the correctness of the analysis from (1) to (3). The fact that R-Value of cascade ratio of IDP is greater than or equal to 1, together with the out-degree distribution, verified the consistency of nodes out-degree between tree_1(Sina) and its randomized copy generated by the cut_zero_null_tree. ...
Article
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... The set of social implications where online social networks play an important role is very large. For instance, on Facebook, people can post information about an event, which can then be shared by other people in their network and thus propagate information in this manner very efficiently to a large audience [4]. Other applications include online marketing [5], spreading viruses [6] and community formation [7] which can be studied using analysis methods, dynamic processes, network metrics, visualization techniques and clustering algorithms on large realistic datasets. ...
... To calculate the homophily of networks, we again use the method proposed by [61] for numerical and categorical values assigned to nodes. We randomly assign five numerical values (1)(2)(3)(4)(5) and three categorical values (A,B,C) in equal proportion to generated networks of node size 1000. The experiment was repeated 10 times for each value to obtain average scores of Fig. 11. ...
Preprint
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... At the local level, network modularity and network transitivity address the segregation of users within the network by elucidating how users are divided into smaller communities and how information spreads within and between these local communities (Scott, 1991). Larger network modularity and transitivity imply more efficient information spreading within the originating local community or the even smaller triad nested in a repost network, while smaller modularity and transitivity indicate more efficient information spreading between local communities or between triad groups (Iribarren and Moro, 2011;Nematzadeh et al., 2014). By examining the structure of repost networks at both the global and the local levels, we are able to understand how users are connected with each other via reposting relationships and how these connections contribute to the dissemination of posts about GMOs: ...
... Network transitivity is a property typically found in acquaintance networks by which two individuals with a common connection are more likely to know each other (Iribarren and Moro, 2011). It measures the degree to which the relation connecting two accounts in a network can be connected by a transitive edge (Wasserman and Faust, 1994), M = 0.02, SD = 0.04. ...
... If an individual's unique words (with high TF-IDF values) were adopted by co-workers in subsequent emails, we can expect that the idea expressed by these words was influential and being spread across the network [63]. However, the timing of emails is the only mechanism controlling the direction of influence. ...
... Gloor et al. [13] Job turnover Iribarren and Moro [63] Information sharing Gloor [57] Communication behavior Dynamics ...
Preprint
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... At the local level, network modularity and network transitivity address the segregation of users within the network by elucidating how users are divided into smaller communities and how information spreads within and between these local communities (Scott, 1991). Larger network modularity and transitivity imply more efficient information spreading within the originating local community or the even smaller triad nested in a repost network, while smaller modularity and transitivity indicate more efficient information spreading between local communities or between triad groups (Iribarren and Moro, 2011;Nematzadeh et al., 2014). By examining the structure of repost networks at both the global and the local levels, we are able to understand how users are connected with each other via reposting relationships and how these connections contribute to the dissemination of posts about GMOs: ...
... Network transitivity is a property typically found in acquaintance networks by which two individuals with a common connection are more likely to know each other (Iribarren and Moro, 2011). It measures the degree to which the relation connecting two accounts in a network can be connected by a transitive edge (Wasserman and Faust, 1994), M = 0.02, SD = 0.04. ...
Article
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Using network analysis, this study investigates how information veracity and account verification influence the dissemination of information in the context of discourse about genetically modified organisms on social media. We discovered that misinformation and true information about genetically modified organisms demonstrated different dissemination patterns on social media. In general, the dissemination networks of misinformation about genetically modified organisms were found to have higher structural stability than those of true information about genetically modified organisms, as shown by the denser network structure with fewer distinct subgroups residing within the dissemination networks. More importantly, unverified account status significantly boosted the dissemination of misinformation by increasing network density. In addition, we found that the posts about genetically modified organisms from unverified accounts received more reposts and had more layers of information relay than those from the verified accounts. Theoretical and practical implications of these findings on combating misinformation are discussed in the article.
... If an individual's unique words (with high TF-IDF values) were adopted by co-workers in subsequent emails, we can expect that the idea expressed by these words was influential and being spread across the network [63]. However, the timing of emails is the only mechanism controlling the direction of influence. ...
... Gloor et al. [13] Job turnover Iribarren and Moro [63] Information sharing Gloor [57] Communication behaviour ...
Article
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... The problem being addressed in this paper is closely related to the study of human behavioral spreading which has been actively pursued in various disciplines, such as the study of social contagion, information diffusion on social networks, viral marketing in business, innovation adoption, and so on. Previous works related to the study of phenomena arising from collective networked behavior can be generally categorized as theoretical modeling and analysis [6], [12], [13] and empirical study of historical data [14], [15]. Specifically, Young [12] studied the effect of incorporating heterogeneity into several broad classes of models, and Campbell et al. [6] proposed a model relating demand, pricing and advertising when individuals are engaged in word-of-mouth communication between friends. ...
... Another empirical work tracks the step-by-step email propagation of an invariable viral marketing message. The resulting cascade network, formed by almost purely trees of very low clustering, shows several features that are not observed in other social dynamic processes like rumor spreading, innovation adoption or email chain-letters [15]. The predictive performance of a new model called Function Regression with several other models has been compared [1]. ...
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Many products and services can only achieve their intended functions and performance when being used in conjunction with or under the operation of other products and services. Such products and services, having varying degrees of hierarchical dependence, are becoming dominant in the global market. This trend has intensified as a result of the rapid growth in the use of networked technology, and is particularly visible in computer and Internet related products and services. The growth of the user population is a result of collective decision behavior of users and prospective users as well as the hierarchical dependence of the streams of products or services. Based on construction of a networked community and two fundamental decision-making behaviors, we derive a model in the form of ordinary differential equations that describe the growth of the user population of such hierarchically-dependent markets. Then, we use this model to examine real-life data. Results show that all these data follow our growth equation, and the numerical algorithm for estimating the model parameters allows important market information hidden in the data to be unveiled, such as the relative effectiveness of customer service and promotional efforts, spending power of users, effective market size, and market growth rate.
... The authors in [5] focused on identifying the optimal network that best describes information propagation in news media and blogs. In [14], authors analysed word of mouth through email. Social influence modeling was studied in [6]. ...
... • Aim to identify patterns of propagation similar to the work described in [14] • Validate virality upon time with an easy to calculate criterion similar to metrics described in works [3,6,13,30,9,11] • Expand the concept of information diffusion across multiple OSNs, unlike the works described in [32,18,4,27], and [16] that deal with the same concept but within one OSN ...
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Information flows are the result of a constant exchange in Online Social Networks (OSNs). OSN users create and share varying types of information in real-time throughout a day. Virality is introduced as a term to describe information that reaches a wide audience within a small time-frame. As a case, we measure propagation of information submitted in Reddit, identify different patterns and present a multi OSN diffusion analysis on Twitter, Facebook, and 2 hosting domains for images and multimedia, ImgUr and YouTube. Our results indicate that positive content is the most shared and presents the highest virality probability, and the overall virality probability of user created information is low. Finally, we underline the problems of limited access in OSN data. Keywords: Online Social Networks, Virality, Diffusion, Viral Content, Reddit, Twitter, Facebook, ImgUr, YouTube
... Recently, some variant epidemiological models capturing the willingness of people to disseminate information have been reported. In [14], a SIR-like model (called message affinity model) was introduced by assigning to the substrate network nodes a propensity value representing their affinity with the message/news being disseminated. Here, S refers to the susceptible agents who have not received the information, I the informed agents who are propagating the information, and R the refractory agents who do not spread the information anymore. ...
... As in [14,16], we assign the affinity value a i ∈ [0, 1] to each agent i, which represents her propensity to engage in forwarding the information in question. {a i } N i=1 are independently identically distributed according to the uniform distribution. ...
Article
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In this paper we develop a dynamical information diffusion model which features the affinity of people with information disseminated in social networks. Four types of agents, i.e., susceptible, informed, known, and refractory ones, are involved in the system, and the affinity mechanism composing of an affinity threshold which represents the fitness of information to be propagated is incorporated. The model can be generally described by a time-inhomogeneous Markov chain, which is governed by its master (Kolmogorov) equation. Based on the Wei-Norman method, we derive analytical solutions of the model by constructing a low-dimensional Lie algebra. Numerical examples are provided to illustrate the obtained theoretical results. This study provides useful insights into the closed-form solutions of complex social dynamics models through the Lie algebra method.
... Most of existing works only measure the behavioral factors one at a time. In these works, user virality is often measured based on the number of items the user has propagated, or the number of friends the user has propagated the item(s) to [14,19,9]. Similarly, user susceptibility is often measured based on the numbers of times the user has exposed and been infected with items [11,2]. An item's virality is often measured simply based on the numbers of times the item is adopted or propagated [34,7,39,28], or the structure of its propagation cascades [12,10]. ...
... User virality models. Existing user virality models include fan-out [14,19] and propagation count [9]. The fan-out of user u, denoted by V f o (u) is defined by the average number of times u propagates item(s) per u's adoption. ...
Conference Paper
In social media, the magnitude of information propagation hinges on the virality and susceptibility of users spreading and receiving the information respectively, as well as the virality of information items. These users' and items' behavioral factors evolve dynamically at the same time interacting with one another. Previous works however measure the factors statically and independently in a restricted case: each user has only a single adoption on each item, and/or users' exposure to items are observable. In this work, we investigate the inter-relationship among the factors and users' multiple adoptions on items to propose both new static and temporal models for measuring the factors without requiring user - item exposure. These models are designed to cope with even more realistic propagation scenarios where an item may be propagated many times from the same user(s) to the same other user(s). We further propose an incremental model for measuring the factors in large data streams. We evaluated the proposed models and existing models through extensive experiments on a large Twitter dataset covering information propagation in one month. The experiments show that our proposed models can effectively mine the behavioral factors and outperform the existing ones in a propagation prediction task. The incremental model is shown more than 10 times faster than the temporal model, while still obtains very similar results.
... An agent-based model (ABM) is a computer program that simulates the actions and interactions of autonomous agents (both individual or collective entities such as organizations or social groups) in order to assess their effects on the system as a whole (for a review of ABMs see Niazi & Hussain, 2011). Because ABMs enable us to manipulate variables and observe the effects in a more controlled manner than in real life, they have proven useful for investigating questions concerning the diffusion of creative novelty and its impact on cultural evolution (e.g., Gabora, 2008aGabora, , 2008bGuardiola, Diaz-Guilera, Perez, Arenas, & Llas, 2002;Iribarren & Moro, 2011;Jackson & Yariv, 2005;Liu, Madhavan, & Sudharshan, 2005;Sosa & Connor, 2015;Spencer, 2012;Watts & Gilbert, 2014). For example, results obtained with ABMs suggest that agents in large, diverse populations tend to be more creative (Gabora, 2008a;Spencer, 2012), the density of communication links amongst agents produces diminishing returns in term of the benefits on the invention rate (Bhattacharyya & Ohlsson, 2010), and diverse communities are better at generating novelty while communities of specialized agents may be better at communicating novelty Spencer, 2012). ...
Preprint
Although creativity is encouraged in the abstract it is often discouraged in educational and workplace settings. Using an agent-based model of cultural evolution, we investigated the idea that tempering the novelty-generating effects of creativity with the novelty-preserving effects of imitation is beneficial for society. In Experiment One we systematically introduced individual differences in creativity, and observed a trade-off between the ratio of creators to imitators, and how creative the creators were. Excess creativity was detrimental because creators invested in unproven ideas at the expense of propagating proven ones. Experiment Two tested the hypothesis that society as a whole benefits if individuals adjust how creative they are in accordance with their creative success. When effective creators created more, and ineffective creators created less (social regulation), the agents segregated into creators and imitators, and the mean fitness of outputs was temporarily higher. We hypothesized that the temporary nature of the effect was due to a ceiling on output fitness. In Experiment Three we made the space of possible outputs open-ended by giving agents the capacity to chain simple outputs into arbitrarily complex ones such that fitter outputs were always possible. With the capacity for chained outputs, the effect of social regulation could indeed be maintained indefinitely. The results are discussed in light of empirical data.
... Third, the results of this study contribute to the expansion of information diffusion theory by examining information acceptance and diffusion patterns in social media as well as fake news from a user-centered perspective. Information diffusion in social media describes the process by which information spreads to users through user interaction, and the rate of this diffusion depends on the network topology of the interaction and the number of users (Al-Taie & Kadry, 2017;Iribarren & Moro, 2011). This study showed that, in addition to the factors affecting the spread of information presented previously, there was a significant interaction effect between users' information processing systems, social conformity, and the type of message source on the acceptance of fake news. ...
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Social media has become a popular means for users to accept and share the news. At the same time, however, it has also enabled the wide spread of fake news. The negative impact of fake news on society has been rapidly increased. To mitigate this problem, this study aims to find out the effect of social media users’ types of perception of information toward the acceptance and intention of spreading fake news. We conducted an online experiment with 743 of social media users and showed the following results. First, users respond differently depending on the type of message provider even if the same fake news. Second, users who relied more on experiential information processing system were more likely to accept fake news regardless of their perceived social conformity.
... It is specified that there should be no re-edges or self-loops. In this paper, we choose the random rewriting probability of this small-world social network as 0.02 based on the existing literature on social networks (Iribarren & Moro, 2011;Yan et al., 2013;J. Han & Shin, 2016). ...
... We therefore reason that individuals are more likely to disseminate the message from another member if the two are connected through a shared third party via their misinformation sharing activities, thus forming transitive triads in their information sharing network. Finally, although information diffusion research has found that information diffusion in large scale networks may form a cascading structure with low connectivity and low transitivity (Iribarren and Moro 2011), research examining the diffusion of misinformation within more tightly connected networks has found that misinformation diffused in clusters often show strong tendencies of transitivity (Lai and Wong 2002). Building on previous research on transitivity, we propose: H1: Misinformation spread in communities is more likely to be transitive than by chance alone. ...
Article
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Networked social influence and strategic information manipulation are two social mechanisms fueling misinformation spread in online communities. However, it is unclear how these two mechanisms differ in their impacts. We conducted social network analyses on two online communities sharing misinformation concerning refugees in 2016 and COVID-19 in 2020. The results robustly showed that online misinformation spread is transitive and positively associated with members’ embedded authority (i.e., the extent to which members’ information is exclusively shared within the focal community). At the same time, strategic misinformation sharing by members of high community loyalty (i.e., targeted information sharing within the community) is less likely to gain momentum. The impact of bots on misinformation is contingent. Findings suggest that networked social influence is a more powerful driver of misinformation spread than strategic information manipulation.
... A network structure consists of nodes and edges, in which the nodes are actors in social interactions and the edges are the connections between nodes through such social activities [5]. From the perspective of network embeddedness, each node in the network plays the two roles of the mediator and filter, for network information flows [17]. us, the location of a node in the network determines the quantity and quality of information; it can access from the network [9] as well as has the ability to address information [18]. ...
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Based on the quarterly data of mutual funds in China from the fourth quarter of 2004 to the fourth quarter of 2019, this paper constructs a series of complex bipartite networks based on the overlapped portfolios of mutual funds and then explores the influences of fund network position on mutual fund’s investment behavior and performance. This paper finds that a mutual fund with shorter information transmission path to other entities in the fund network (i.e., having higher closeness centrality) or with stronger ties with those entities in important information positions (i.e., having higher eigenvector centrality) will achieve better investment performance. However, a stronger mediating role over the potential information flow of the fund network (i.e., having higher betweenness centrality) cannot help a mutual fund increase performance. The empirical results also indicate that a mutual fund holding stock portfolios with high valuation difficulties caused by the market or fundamental information uncertainty will achieve better investment performance, while holding hard-to-value portfolios caused by limited public information will reduce the performance of the fund. Furthermore, high closeness centrality or eigenvector centrality can help mutual funds deal with the disclose problems of public information, thus reducing the likelihood of a mutual fund holding hard-to-value portfolios caused by limited public information to achieve worse performance. Eigenvector centrality brings information advantages about company fundamentals, so it is easier for a mutual fund with high eigenvector centrality to profit from holding hard-to-value portfolios caused by the fundamental information uncertainty. The conclusions of this paper can enhance our understanding of the fund network and its information mechanism and shed new light on mutual fund’s information advantages and related asset allocation strategies.
... The discrepancy between private and expressed opinions arising from conformity pressure has been examined in [15,16]. The unique roles of psychological aversion/contrarian [18], affinity [19], first impression [20], and biased assimilation [21] have also been investigated. We refer the reader to [11,22] for updated surveys of opinion formation models. ...
Article
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In this paper, we study the consensus formation over a directed hypergraph, which is an important generalization of standard graph structure by allowing possible neighbor-dependent synergy. The proposed model is situated in the social dynamics providing key features including social observer effect and bounded confidence. Under the minimal siphon condition of a directed hypergraph (Petri net), we show that global consensus can be reached with the final consensus value residing in the common comfortable range if it is non-empty. To achieved this, we establish an equivalent condition for the commensurate graph of a finite state machine to be strongly connected. Convergence analysis is performed based on the proposed nonlinear dynamic system model and Petri net method. The consensus result holds for any non-negative confidence bound, which distinguishes from traditional bounded confidence opinion models as we measure the difference among neighbors rather than the gap between neighbors and the ego. Numerical studies are conducted to unravel some insights in relation to the influence of observers, hypergraph architecture, and confidence bounds on opinion evolution. The results and methodologies presented here facilitate research of social consensus and also offer a way to make sense of synergy in networked complex systems. MSC 2010: 91D30; 34H05; 05C65
... This leads to increased perceived homophily. Likewise, Iribarren and Moro (2011) stated that an information sender who is perceived as high affinity is more likely to give a reason for the emotions and classified as more credible. For this reason, information exchange through WOM or eWOM communication happens more frequent among individuals with high homophily . ...
Article
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Purpose With the ever-expanding online shopping, electronic word-of-mouth (eWOM) has become a significant factor affecting the consumer decision-making behaviour. This is specially the case when considering Generation Y (Millennials), who are old enough to be independent buyers and young to be almost immersed in online living. This article aims to assess the impact of eWOM on purchase intention by developing a conceptual model of hypotheses encompassing a multitude of factors that may be associated with this relationship. Design/methodology/approach The researcher investigates what factors impact eWOM credibility and make the consumer may adopt it when making a purchase. To examine our research model, a quantitative approach is employed for this purpose using a sample through online survey from Thailand – where there is a large number portion Generation Y consumer base. Findings It was found that source style as a visual attribute information is the most significant factor that may impact eWOM credibility in addition to source credibility, argument quality and source homophily, respectively. Practical implications From a practical point of view, it helps firms to understand what needs to be taken into consideration when building their marketing strategy. Originality/value This is believed to add significant insights into the eWOM literature by identifying its route of impact toward the purchase intention on Generation Y.
... A vast amount of literature on this topic has been produced by scholars during the last decade. Pioneering scholars from disparate academic disciplines have investigated collective action propagation and have advanced various terms for describing the same or similar propagation phenomena, e.g., rumor spreading and epidemic spreading [1], massive human phenomenon [2][3][4][5], social influence [6,7], popularity of online content [8][9][10], innovation diffusion of new technologies and products, viral marketing, and business growth [11,12]. In this paper, we will collectively refer to the phenomena described by these terms as ''collective action propagation.' ...
Article
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Collective action propagation, which can be as large as billions of people adopting Facebook or as small as a few researchers citing a paper, exists in various real-life scenarios. Here, we perform a large-scale investigation of collective action propagation with “recurrence” phenomena. We consider actions that propagate in a social network with multiple communities and find the growth in the propagation breadth of collective action can be explained by a simple mathematical model with an analytical solution. We use datasets on the growth of total views of TED and YouTube videos, the prize pool of Dota 2 tournaments, and a total gross of movies to investigate collective action propagation with recurrence phenomena. Experimental results reveal that our model can capture universal features of collective action propagation, validating the idea that collective action propagation with recurrence results from an action being transmitted from communities to communities.
... In a social network, as previously mentioned a user can communicate with other users by sharing a particular interest, activities of daily living, comments, and other emotional expressions [30]. A social network can influence information diffusion, and the degree of information diffusion is determined by the influence of the user posting the information [18]. In an SNS, it is possible to find users' similarity of birth date, birthplace, and interest, and interact with other social networking users. ...
Article
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This study proposes the multimedia recommendation method using Word2Vec-based social relationship mining. This is to analyze users with a similar tendency on the basis of the keywords related to multimedia content and sentiment words of comments, to build a trust relationship, and to recommend multimedia. In order to solve the problem of data sparsity, metadata of multimedia content are collected and then are clustered by genre. User’s evaluate a preference for multimedia content. With the use of evaluation data, the attributes preferred by users are predicted. In terms of propensities, the sentiment words in users comments are classified by SVM on the basis of sentiment dictionary. The classified sentiment words are presented in vector with the use of Word2Vec. In terms of the vector of sentiment words, the dynamic relationship between users of words in the same preference by the similarity using the distance scale. It helps to build a trust relationship between users with preferences that can change with a lapse of time. Accordingly, multimedia content are recommended to users with a similar tendency. In terms of performance evaluation, F-measure is compared with the uses of precision and recall for a recommendation. As the result of evaluation, the social relationship mining method is evaluated to be better than explicit and implicit recommendation methods. With the proposed method, it is possible to search with metadata of content and make a intelligent recommendation explicitly and implicitly according to user’s tendency.
... Social networks have influenced actors of different regions to share the information due to the advancement in the information technology. The main goal of a social network is to make the information space, where actors can share information like thoughts, personal data, events, etc. Visibility of information [3], structural variations [4], and access [5] are significant characteristics of a social network. ...
Article
The rapid development of communication and networking has lessened geographical boundaries further to social networking, which enables to set up relations among people who share common interests, activities or connections. In social networks, actors (or people) often want to acquire information based on their activities, education, role, etc. The social network concept handles human relationships in networks efficiently to achieve the information provision. Due to advent of social networks, the need for flexible, adaptable and rapid response time to information provision has become increasingly important. An academic social network is grouping of a specific academic faculty group members at different levels. For example, a communication group in a research institution could have the members like professor, faculty, research students, graduate students, project staff, lab assistants, etc. At each level group members, they need relevant information on the projects currently leading on elsewhere. Hence, we feel that any one of the group member searches for related research information for his level appropriately, the system intelligently makes other group members aware of developments on the issues. For example, a professor gets some information in a concept based environment, on the other hand, the system should be providing relevant lab oriented information to lab assistant. The information need to be provided suitable to actors with different requirements, hence, to enable such intelligent way of information provision, we need to consider various characteristic features of actors such as personal information, professional information, etc. Traditional networks provide static information which are not actor adaptive, and they do not use characteristic information of actors and their profile parameters to provide dynamically adapted information. In this paper, we present an Intelligent Method of Information Provisioning in an Academic Social Network (IMIPASON) by considering actor's characteristic features like activity, education, qualification, etc., which reflects on the web queries generated by actors. In this method, we classify academic group of actors based on their hierarchical relations with respect to academic activities. In the case of any group of actors raises a web query, the proposed system generates appropriate queries for rest of the actors who need information based on the activities of the entire group. The designed IMIPASON is tested over an Academic SOcial Network (ASON) which constitutes a set of actors related to the academic profession. The system generates appropriate queries for all the actors if any one of them desires the information. We have simulated different sets of academic actors and tested the system. Results were obtained for the accuracy of our proposed IMIPASON model, and the average service time required for generating queries for a set of actors.
... The SIR model was initially used to describe the mechanism of disease transmission and was later widely used in various information dissemination models (Iribarren and Moro, 2011;Ventresca and Aleman, 2013).The nodes in SIR model have three different states: susceptible, infected and recover. The infected nodes can infect susceptible one that change its state into infected, but the recover one will not change its state again. ...
Preprint
Influence overlap is a universal phenomenon in influence spreading for social networks. In this paper, we argue that the redundant influence generated by influence overlap cause negative effect for maximizing spreading influence. Firstly, we present a theoretical method to calculate the influence overlap and record the redundant influence. Then in term of eliminating redundant influence, we present two algorithms, namely, Degree-Redundant-Influence (DRS) and Degree-Second-Neighborhood (DSN) for multiple spreaders identification. The experiments for four empirical social networks successfully verify the methods, and the spreaders selected by the DSN algorithm show smaller degree and k-core values.
... Although these diffusion models and their variants have been successfully used to study diffusion dynamics (e.g. diffusion of URLs on Twitter [1], viral marketing [2]), they assume simple node (agent) models, as the focus is on the network and emerging diffusion patterns. However, there are many real scenarios that requires a more complex agent model. ...
... Identifying and understanding social influence are of tremendous interests for both analysis and design points of view; specifically, it is of significant interest to evaluate endogenous and exogenous from real-world data, which is also a notoriously hard statistical problem in general for distinguishing correlation from causality [8]. There exists a recent literature concerning the issue of collective propagation phenomenon on networks which can be categorized as theoretical analysis on mathematical model [4,13,14] and empirical research on real data [15,16]. However, to our understanding, few studies consider exogenous and endogenous influence together, and no literature rigorously derives a model clearly reflecting the importance of the endogenous and exogenous influences of collective behaviour propagation in social networks and applies the model to real-world data. ...
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Many markets include a product and a platform product, where the product can only achieve its intended functions and performance in conjunction with or under the operation of its platform, such as a video game can only run on its game console. The growth of the user population of these products or services is a kind of collective behaviour propagation phenomenon. Here, questions come: how can we describe the collective behaviour propagation as a function of time? How the endogenous and exogenous social effects influence the collective behaviour propagation and how to quantify these two effects? In order to answer all these questions, an ordinary differential equation model is proposed to describe the growth of the user population of this class of markets. Firstly, a networked community is constructed, where users and prospective users are considered as nodes, and their relationship provides the method of building edges. Then, two fundamental influences of decision-making can be realized based on the network. A useful application of the model can be conceived and illustrated by one new database containing weekly sales of 25,237 video games released in the home and handle consoles and personal computer in USA, UK, Germany, France and Japan from 1989 to 2018. Results show that historical sales profile of a video game follows the growth equation, and the numerical procedure for finding the model parameters allows the market size, and the relative effectiveness of customer service and promotional efforts to be estimated according to the available historical data.
... Social networks have influenced actors of different regions to share the information due to the advancement in the information technology. The main goal of a social network is to make the information space, where actors can share information like thoughts, personal data, events, etc. Visibility of information [3], structural variations [4], and access [5] are significant characteristics of a social network. ...
... News diffusion networks were found to have a core periphery structure, where a small set of core media sites diffused the information to the rest of the network, while blogs were mostly influenced by mass media. In [118], authors analysed word of mouth through web means, mainly through email forwarding. Two dynamic patterns were observed, namely "Transmissibility" and "Fanout ...
... Social networks have become a great source of sharing opinions, ideas, information and beliefs [24]. With the availability of huge amount of data, the analysis of information diffusion has become an interesting area to explore [25]. Virality has thus become an indicator of online ad effectiveness [26]. ...
Conference Paper
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In the current era of digitization specifically with the advent of Web 2.0, social media has become an imposing force in shaping up the way people perceive and react to the information around them. Social media platforms have empowered people to share almost instant feedback on the content posted by individuals and organizations facilitating two way interaction and better engagement among them. This continuous interaction among individuals and organizations creates huge amount of user-generated content (UGC) and associated tokens. This study attempts to understand various semantics that might affect the virality of Facebook posts. Several pages have been identified and shortlisted from domains including e-commerce, manufacturing, services and media. A total of 53,340 Facebook posts comprising of 37, 38, 168 words have been extracted using Facebook Graph API from each of the mentioned domains and subsequently analyzed using NOSQL databases. Further, the derived tokens are semantically grouped and used to gather insights by mapping to existing virality frameworks for identifying and ranking the ones that might be affecting the virality of a post. Findings indicate the virality of content shared has positive correlation with direct brand engagement, promotional offers, freebies and direct user mentions.
... An agent-based model (ABM) is a computer program that simulates the actions and interactions of autonomous agents (both individual or collective entities such as organizations or social groups) in order to assess their effects on the system as a whole (for a review of ABMs see Niazi & Hussain, 2011). Because ABMs enable us to manipulate variables and observe the effects in a more controlled manner than in real life, they have proven useful for investigating questions concerning the diffusion of creative novelty and its impact on cultural evolution (e.g., Gabora, 2008aGabora, , 2008bGuardiola, Diaz-Guilera, Perez, Arenas, & Llas, 2002;Iribarren & Moro, 2011;Jackson & Yariv, 2005;Liu, Madhavan, & Sudharshan, 2005;Sosa & Connor, 2015;Spencer, 2012;Watts & Gilbert, 2014). For example, results obtained with ABMs suggest that agents in large, diverse populations tend to be more creative (Gabora, 2008a;Spencer, 2012), the density of communication links among agents produces diminishing returns in term of the benefits on the invention rate (Bhattacharyya & Ohlsson, 2010), and diverse communities are better at generating novelty while communities of specialized agents may be better at communicating novelty Spencer, 2012). ...
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Although creativity is encouraged in the abstract it is often discouraged in educational and workplace settings. Using an agent-based model of cultural evolution, we investigated the idea that tempering the novelty-generating effects of creativity with the novelty-preserving effects of imitation is beneficial for society. In Experiment One we systematically introduced individual differences in creativity, and observed a trade-off between the ratio of creators to imitators, and how creative the creators were. Excess creativity was detrimental because creators invested in unproven ideas at the expense of propagating proven ones. Experiment Two tested the hypothesis that society as a whole benefits if individuals adjust how creative they are in accordance with their creative success. When effective creators created more, and ineffective creators created less (social regulation), the agents segregated into creators and imitators, and the mean fitness of outputs was temporarily higher. We hypothesized that the temporary nature of the effect was due to a ceiling on output fitness. In Experiment Three we made the space of possible outputs open-ended by giving agents the capacity to chain simple outputs into arbitrarily complex ones such that fitter outputs were always possible. With the capacity for chained outputs, the effect of social regulation could indeed be maintained indefinitely. The results are discussed in light of empirical data.
... Other studies, however, have focused not on the individual characteristics of opinion leaders, but rather on the position that certain users occupy in communication networks. This line of research has either relied on computer simulations (Goldenberg, Libai, and Muller 2001;Watts and Dodds 2007) or analyzed empirical data from discussions in online communities (Huffaker 2010) and email networks (Iribarren and Moro 2011;Leskovec, Adamic, and Huberman 2007), and demonstrated that the relationship between the sender and the receiver of the messages as well as the position that certain users occupy in communication networks influences their ability to trigger product-related information diffusion, eWOM and product adoption. ...
Article
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Using a sample of over 5300 tweets from top global brands, this study investigated how different types of users can influence brand content diffusion via retweets. Twitter users who influenced followers to retweet brand content were categorized as (1) influentials, because of their above average ability to influence others to retweet their tweets (in general), (2) information brokers, because of their position connecting groups of users or (3) having strong ties, because of their high percentage of friends in common and a mutual friend–follower relationship with the influenced follower. The results indicate that influentials and information brokers are associated with larger number of retweets for brand content. In addition, although information brokers have a larger overall influence on retweeting, they are more prone to do so when influentials are mentioned in the brand tweet, providing support for the strategy that aims to associate the brand with influential users.
... Such associations have long been observed by sociologists (Moody, 2001) and have been found in age, sexuality, race, gender, income, education level, religion, status and competence (Carley, 1991;Ibarra, 1993;Laumann, 1966;McPherson and Smith-Lovin, 1987). Homophily is an important concept in IS being used to explain information diffusion, technology adoption and team performance (Thelwall, 2009;Ruef et al., 2003;Hinds et al., 2000;McPherson et al., 2001;Iribarren and Moro, 2011). ...
Article
Purpose The purpose of this paper is to study the influence of user personality and values on the number of connections users make, the number of requests for connections that users give out, and the number of connection invitations users receive. Design/methodology/approach This is a field study of 179 participants interacting in a novel virtual world. The world’s server logs are used to capture sociometrics about the users and their interactions. Findings Findings suggest that personality and values influence the number of friends users make and the number of friendship requests users give out, but not the number of friendship invitations users receive. Only one personality trait – conscientiousness – exhibits homophily. Originality/value Personality and social value orientation have rarely been studied together in information systems (IS) research, despite research showing the two have an impact on IS relevant constructs. The use of server logs for data capture is novel. Avatar friendship is an under-researched concept in IS.
... Анализ распространения информации в социальных сетях широко используется в маркетинге. Чтобы избежать навязчивой массированной рекламы, фирмы стремятся повышать эффективность проводимых рекламных компаний, распространяя информацию о продукте для заинтересованных лиц, которые, в свою очередь, передают информацию своим знакомым и друзьям [54,56]. Подобный способ распространения рекламной информации называется вирусным маркетингом и обладает чертами распространения мемов. ...
Article
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The purpose of this paper is twofold. On the one hand, we will provide model for the meme transmission from its bearer to a member of his virtual friends set and meme spreading in social networks in the context of socio-humanitarian views on the nature of the internet memes. On the other hand, we will highlight issues of generation, circulation and assess the potential impact of memes and provide the presentation of case illustrations to outline the possibility of further development of the socio-engineering and other formal models of memes with the currently known results and the needs of field and analytical studies of social networks in sociology, political science, psychology, information technology and other related studies.
... The study of epidemic behaviors in the network sciences area, like viruses spreading, and transmitting diseases, is highly relevant for understanding various areas that may be modeled as networks and their growing patterns (Kempe et al, 2003) (Iribarren et al, 2011) (Barash et al., 2012. A marketing technique called viral marketing has as its main feature the exploitation of this potential inherent to every social network. ...
Article
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Companies have been increasingly seeking new mechanisms for making their electronic marketing campaigns to become viral, thus obtaining a cascading recommendation effect similar to word-of-mouth. We analysed a dataset of a magazine publisher that uses email as the main marketing strategy and found out that networks emerging from those campaigns form a very sparse graph. We show that online social networks can be effectively used as a means to expand recommendation networks. Starting from a set of users, called seeders, we crawled Google's Orkut and collected about 20 million users and 80 million relationships. Next, we extended the original recommendation network by adding new edges using Orkut relationships that built a much denser network. Therefore, we advocate that online social networks are much more effective than email-based marketing campaigns
... It shares the basic purpose of interaction and communication, and specifies goals and patterns that vary significantly across different regions of actors. Visibility of information [2], structural variations [3] and access [4] are the significant characteristics of a social network. The most distinguishing features of a social network are relationships among social entities, patterns and deduction of these. ...
Article
Purpose The purpose of this paper is to introduce and investigate social brokers who belong to and connect multiple groups in a social network. This paper also reveals the differential effects of innovative and following social brokers on content diffusion in terms of adoption timing, speed and size. Design/methodology/approach The paper collected field data related to 69,086 users on the largest social network platform in China and analysed their adoption behaviours of 2,492 pieces of content. Findings The analysis reveals that social brokers encourage content diffusion and accelerate the speed of content adoption in a social network. Specifically, following social brokers play a greater role than innovative social brokers in accelerating the speed of content adoption and expanding the size of content adoption. However, in the early stage of content diffusion within the social network, innovative social brokers could predict the success of content adoption more effectively than following social brokers. Research limitations/implications This research extends the current diffusion literature by introducing the social broker and examining the effect of social brokers on the process of content adoption. Practical implications The findings provide suggestions to marketing managers on how to improve the diffusion of marketing-related content, such as by seeding specific people – that is, social brokers – with content, so they can serve as content transmitters in marketing campaigns. In addition, the findings suggest that to optimise content adoption in a social network, managers should strategically target innovative social brokers or following social brokers at various stages of content seeding-based marketing campaigns. Originality/value To the best of the authors’ knowledge, this research is the first to test the effects of social brokers on content adoption and identify innovative and following social brokers. The findings enrich the literature on content marketing by providing new perspectives on social structures in social networks.
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La exposición de marca constituye un instrumento de proyección de gran impacto, en la actualidad el uso de medios sociales se convierte en una gran herramienta para lo anterior, utilizado por marcas personales. El conectar personas con seguidores como uno de sus grandes logros, facilitando el alcance de sus objetivos como marca. La presente investigación tuvo como objetivo identificar los factores que facilitan la exposición de una marca personal a través del uso de redes sociales y su posterior uso como medio publicitario. La metodología utilizada se basa en un enfoque cualitativo y como instrumento de recolección de datos se utilizó una entrevista semiestructurada de doce preguntas basadas en los aportes de los diferentes autores especializados en la gestión de marcas corporativas. Con lo anterior, se generó un acercamiento entre la teoría y la práctica a partir de identificar la equivalencia entre los conceptos teóricos y los testimonios de los participantes. Como principal hallazgo se identificó que el sello personal a partir de la originalidad es el factor de mayor impulso para aumentar la exposición de una marca personal en redes sociales.
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People react to informational content in social media and often propagate the same. The content available may either be authentic information or misinformation. This study focuses on modeling the user attributes of information and misinformation propagators, derived from user based and user generated content based attributes. In this study, 10,000 users and 5,55,684 tweets were analyzed to compute eighteen factors based on tweet and user parameters. Factor selection for the final analysis identified 11 statistically relevant factors. An approach is proposed to classify users as information or misinformation propagators using K-means integrated with bio inspired algorithms like firefly, cuckoo search and bat algorithms. Results show that the firefly algorithm with levy flights gives the highest accuracy while the bat algorithm converges to an optimum solution faster. Findings indicate that factors like emotion stability, polarity stability, hashtag consolidation ratio, hashtag diversity, lexical diversity, favorites count and friends count have relatively higher importance in predicting propagators differently. Computational findings are integrated with psychological behaviors of people by building upon the theory of personality traits. Findings are useful in domains of viral marketing and information governance to identify potential user groups which may play a role in the propagation of misinformation and information.
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Technology has grown exponentially in this 21st-century society with the alignment of the Industrial Revolution 4.0, where communication technology devices and knowledge are made readily available at an individual’s disposal. In line with I.R 4.0 and TVET, handheld devices and computers have started to infiltrate the tertiary education level across the nation. It is hoped that the iPad Pro usage in teaching and learning could assist the students with learning disabilities in learning effectively. This study is also meant to assist them in learning in the most meaningful way and nurturing them to be autonomous learners. In reality, Apple Teacher and the application of iPad Pro is not common in the teaching and learning session particularly in Malaysia; due to the educators’ limited knowledge in utilising iPad Pro as an instrument of teaching and learning. The purposes of this study are to examine the educators’ discernment towards iPad Pro as an instructional tool and to investigate the Apple Teachers’ capabilities in utilising iPad Pro in harmonising the teaching and learning session. Eighteen (18) participants who participated in the Apple Teacher course in developing teaching and learning materials using iPad Pro were selected for this study. The data collection was conducted through an online survey and online interview sessions to investigate their discernment and capabilities in utilising iPad Pro in developing the teaching and learning sessions. The preliminary findings via the survey reveal that the educators’ capabilities of utilising iPad Pro do change their pedagogical methods in teaching and learning. In addition, the findings indicate that the usage of iPad Pro proves to have potentials and positive impacts on the teaching and learning engagement. Keywords: iPad Pro; Instructional tools; Apple Teacher; Apple Teacher Discernment and Capabilities; Teaching and Learning
Conference Paper
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In recent years, social media have become an integral part of online news distribution and consumption. Many news organisations have established social media accounts on various social platforms. Competition for audience influence has never been higher. Few studies have researched engagement, influence and social media information diffusion. To fill this gap, this article attempts to examine previous studies that are concerned with news content and news sharing on Twitter in the period 2010-2020 through a review of scientific, peer-reviewed articles. Fiftysix articles were selected as relevant from the skimming of abstracts from two databases (Taylor & Francis and Sage) by searching the keywords "news content, sharing, Twitter." To discover the general trend and pattern, each article was content analysed along four primary dimensions: authorship profile, manuscript characteristics, research design, and research methodology. Three central research areas—news sharing users, content, and networks—were identified and systematically reviewed. The review results find that studies' focus in recent years lies in four aspects: the content, the users, the media organisations, and the network. The review results provide critical analysis of current research and give future research suggestions in related areas in the central concluding section. Keywords: News content; News sharing; Twitter; News diffusion
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Virtual identity is visually established through Avatars and their associated profiles. Realising that there are real people with real emotions on the other side of the screen, this research intends to uncover the virtual identity that users embrace in the virtual world. More specifically, we investigate how Avatars communicate their identity in Second Life beyond the visual representation. This research applied an ethnography approach in this study in which prolonged engagement in the online community, i.e. Second Life was undertaken to understand the culture of the virtual residents. Guided by Erving Goffman’s Theory, we conducted participant observation and open interviews to unveil the virtual identity. A total of 25.5 hours in-world time was spent in Second Life including interviews with six respondents. Data was collected through field notes and one-to-one interviews. Words, phrases and sentences were coded, and thematic analysis was employed to identify the dominant themes. The findings have shown that virtual identities are constructed through the words, phrases and sentences that the Avatars used in the online interactions with other users or Avatars. Each Avatar is unique when they carry their own identification based on the interactions. To some extent, the virtual world has strongly impacted on several aspects of human life, with identity being one of them. This study hopes that the findings may assist professionals as well as practitioners desiring to gain more understanding of virtual identity. Keywords: Virtual Identity; Second Life; Virtual World; Avatar
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The commitments given by the users of Second Life virtual world game for an extended time has sparked an interest for the researchers to investigate how Second Life virtual world has fulfilled the needs of its residents. Previous research highlighted that Second Life has served support for learners’ socialisation and motivation in their daily lives. The researchers gather information on motivations of the Second Life users with the aim to identify differences of the motivations based on two genders; males and females by employing virtual ethnographic study. The researchers collected the data on how Second Life virtual world has served the motivations of its residents using participant observation, informal interviews and one-to-one semi-structured interviews. Eight participants have taken part to provide relevant information. The data were analysed using inductive thematic analysis which resulted in the finding of four categories of motivations: experiential, social, escapism and functional. The results reveal that the two genders share similarities in their motivations but differ in their preferences of the motivations. The study has several implications as it enhances our understanding of the motivations of the two genders and their preferences which will be useful to understand the communication process among the users. This study also highlights the features offered by the Second Life virtual world serve different needs for its users. The findings of this study make several contributions to the current literature as it has revealed the differences of main motivations between males and females to be in virtual world games. This study also provides insights on experiential motivations of male avatars in Second Life which could lead to more future research on this topic. Keywords: Virtual world game; Second Life; Motivations
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In order to overcome the low level of literacy of the population in the issues of modern innovative technologies, the paper suggests a technological approach, which combines methods of classical marketing with elements of social engineering on the basis of a neuro-evolutionary paradigm. The essence of this approach is to organize social communications in the target group by analogy with neuron connections in mathematical neural networks, thus ensuring the group’s high efficiency in making collective decisions. At the stage of testing of this campaign, over 3 thousand people were involved in scientific and educational activities of a biomedical orientation.
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La difusión de productos a través de redes sociales es un campo de aplicación del mercadeo, donde la decisión de compra de un consumidor es influenciada por factores internos y externos como su red de conocidos y familiares. El propósito de esta investigación es identificar las principales perspectivas y plantear futuras investigaciones, apoyados en la revisión selectiva del estado del arte. Para la orientación de la búsqueda y la selección de artículos se utilizó la teoría de grafos, aprovechando las posibilidades de reconocer las conexiones entre los diferentes trabajos, arrojando para su análisis 18 artículos clásicos y 23 artículos actuales. A partir de esto se obtuvo, como resultado de la investigación, cuatro (4) estrategias de mercadeo diferentes: enfocadas a los influenciadores, a los no influenciadores, grupos pequeños y estrategias tradicionales de mercadeo.Palabras clave: difusión de productos, redes sociales, teoría de grafos. ABSTRACT The diffusion of products through social networking is an application field of marketing, where the buying decision of a consumer is influenced by internal and external factors as their network of friends and relatives. The purpose of this research is to identify the main perspectives and propose future research, supported in state of the art selective review. As input for the orientation of search and articles selection, graph theory was used, leveraging the odds of recognizing the links among different works, providing for analysis 18 classic articles and 23 current articles. The result of the investigation showed four different marketing strategies: focused on influencers, non-influencers, small groups and traditional marketing strategies.Keywords: diffusion of products, social networks, graph theory.
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Individuals’ access to information in a social network depends on how it is distributed and where in the network individuals position themselves. In addition, individuals vary in how much effort they invest in managing their social connections. Using data from a social media site, we study how the interplay between effort and network position affects social media users’ access to diverse and novel information. Previous studies of the role of networks in information access were limited in their ability to measure the diversity of information. We address this problem by learning the topics of interest to social media users from the messages they share online with followers. We use the learned topics to measure the diversity of information users receive from the people they follow online. We confirm that users in structurally diverse network positions, which bridge otherwise disconnected regions of the follower network, tend to be exposed to more diverse and novel information. We also show that users who invest more effort in their activity on the site are not only located in more structurally diverse positions within the network than the less engaged users but also receive more novel and diverse information when in similar network positions. These findings indicate that the relationship between network structure and access to information in networks is more nuanced than previously thought.
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We consider a network of interacting individuals, whose actions or transitions are determined by the states (behavior) of their neighbors as well as their own personal decisions. Specifically, we develop a model according to two simple decision-making rules that can describe the growth of the user population of a newly launched product or service. We analyze 22 sets of real-world historical growth data of a variety of products and services, and show that they all follow the growth equation. The numerical procedure for finding the model parameters allows the market size, and the relative effectiveness of customer service and promotional efforts to be estimated from the available historical growth data. We study the growth profiles of products and find that for a product or service to reach a mature stage within a reasonably short time in its user growth profile, the user growth rate corresponding to influenced transitions must exceed a certain threshold. Furthermore, results show that individuals in the group of celebrities having numerous friends become users of a new product or service at a much faster rate than those connected to ordinary individuals having fewer friends.
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Time-stamped data are ubiquitous in our daily life, such as twitter data, academic papers and sensor data. Finding clusters and their evolutionary trends in time-stamped data sets are receiving increasing attention from researchers. Most existing methods, however, can only tackle the clustering problem of a data set without time-stamped information which is inherent in almost all the data objects. Actually, not only the performance can be improved by effectively incorporating the time-stamped information in the clustering process on most data sets, but also we can find the evolutionary trends of the clusters with time information. In this paper, we introduce an approach for clustering time-stamped data and discovering the evolutionary trends of the clusters by using Multiple Nonnegative Matrices Factorization (MNMF) with smooth constraint over time. To utilize time-stamped information in the clustering process, an extra object-time matrix is constructed in our proposed method. Then, we jointly factorize multiple feature matrices using smooth constraint to perform the object-time matrix to obtain the clusters and their evolutionary trends. Experimental results on real data sets demonstrate that our proposed approach outperforms the comparative algorithms with respect to Fscore, NMI or Entropy.
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When a microblogging user adopts some content propagated to her, we can attribute that to three behavioral factors, namely, topic virality, user virality, and user susceptibility. Topic virality measures the degree to which a topic attracts propagations by users. User virality and susceptibility refer to the ability of a user to propagate content to other users, and the propensity of a user adopting content propagated to her, respectively. In this paper, we study the problem of mining these behavioral factors specific to topics from microblogging content propagation data. We first construct a three dimensional tensor for representing the propagation instances. We then propose a tensor factorization framework to simultaneously derive the three sets of behavioral factors. Based on this framework, we develop a numerical factorization model and another probabilistic factorization variant. We also develop an efficient algorithm for the models' parameters learning. Our experiments on a large Twitter dataset and synthetic datasets show that the proposed models can effectively mine the topic-specific behavioral factors of users and tweet topics. We further demonstrate that the proposed models consistently outperforms the other state-of-the-art content based models in retweet prediction over time.
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For a large variety of new products, the Bass Model (BM) describes the empirical cumulative-adoptions curve extremely well. The BM postulates that the trajectory of cumulative adoptions of a new product follows a deterministic function whose instantaneous growth rate depends on two parameters, one of which captures an individual's intrinsic tendency to purchase, independent of the number of previous adopters, and the other captures a positive force of influence on an individual by previous adopters. In this paper, we formulate a stochastic version of the BM, which we call the Stochastic Bass Model (SBM), where the trajectory of cumulative number of adoptions is governed by a pure birth process. We show that with an appropriately-chosen set of birth rates, the fractions of individuals who have adopted the product by time t in a family of SBMs indexed by the size of the target population converge in probability to the deterministic fraction in a corresponding BM, when the population size approaches infinity. The formulation therefore supports and expands the BM by allowing stochastic trajectories.
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Purpose To provide an explicit model to address the relationships between the structural characteristics of a network and the diffusion of innovations through it. Further, based on the above relationships, this research tries to provide a way to infer diffusion curve parameters (innovation coefficient and imitation coefficient) from network structure (e.g. centralization). Design/methodology/approach Based on the network and innovation literatures, we develop a model explicitly relating the structural properties of the network to its innovation and imitation potential, and in turn to the observed diffusion parameters (innovation and imitation coefficients). We first employ current theoretical and empirical results to develop postulates linking six key network properties to innovation and imitation outcomes, and then seek to model their effects in an integrative manner. We argue that the innovation and imitation potentials of a network may be increased by strategically re‐designing the underlying network structure. We validated the model by searching the published empirical literature for available published data on network properties and innovation and imitation coefficients. Findings We validated the model by searching the published empirical literature for available published data on network properties and innovation and imitation coefficients. The results reported from various relevant research papers support our model. Practical implications This research shows that the innovation and imitation potentials of a network may be increased by strategically re‐designing the underlying network structure; hence, provide guidelines for new product managers to enhance the performance of innovative products by re‐design the underlying network structure. Originality/value The model developed in this paper is a breaking through result of synthesizing various traditions of diffusion research, ranging from anthropology and economics to marketing which were developed independently. The research explicitly modeled the diffusion process in terms of the underlying network structure of the relevant population allowing managers and researchers to directly link the diffusion parameters to the structural properties of the network. By doing so, it added value by making it possible to infer diffusion potential from directly measurable network properties. Vis‐à‐vis the network diffusion literature in particular, we added value by “unpacking” the diffusion process into innovation and imitation processes that form the building blocks of contagion. Moreover, we developed a holistic structural model of network diffusion which integrates the several network properties that have hitherto been studied separately.
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