Macroscopic study of the social networks formed in web-based discussion forums

Yeung, Yau Yuen

doi:10.3115/1149293.1149388

Cited by 11 publications

(6 citation statements)

References 6 publications

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“…The effects of analysing the relationships between web forum users on the structure of the network (reconstructed from the messages sent) were studied in1314. Also, the type of interaction or content being exchanged have been considered616.…”

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confidence: 99%

The rich club phenomenon in the classroom

Vaquero

Cebrián

2013

Sci Rep

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We analyse the evolution of the online interactions held by college students and report on novel relationships between social structure and performance. Our results indicate that more frequent and intense social interactions generally imply better score for students engaging in them. We find that these interactions are hosted within a “rich-club”, mediated by persistent interactions among high performing students, which is created during the first weeks of the course. Low performing students try to engage in the club after it has been initially formed, and fail to produce reciprocity in their interactions, displaying more transient interactions and higher social diversity. Furthermore, high performance students exchange information by means of complex information cascades, from which low performing students are selectively excluded. Failure to engage in the rich club eventually decreases these students' communication activity towards the end of the course.

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confidence: 99%

The rich club phenomenon in the classroom

Vaquero

Cebrián

2013

Sci Rep

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“…To illustrate the asymmetrical functional form of the penetration curve, we simulated adoption on a 100,000-node network for different classes of networks. Adoption processes under each of four common classes of network degree distributions-Gaussian, uniform, lognormal, and scale-free-are given in Figures 1(a)-1(d), respectively, (for empirical examples of these networks distributions, see Amaral et al 2000, Barabási and Albert 1999, Liben-Nowell and Kleinberg 2008, Limpert et al 2001, Newman 2005, Newman et al 2002, Yeung 2005.…”

Section: Degree Heterogeneity Leads To Highly Skewed Adoption Curvesmentioning

confidence: 99%

“…Because network structure is typically invisible, the few diffusion models that incorporate network structure do so by resorting to rough assumptions (e.g., division into two markets) that oversimplify the diffusion process and its dynamics. One of these oversimplifying assumptions in the literature is that networks generally exhibit scale-free degree distributions, 1 despite the fact that other types of distributions have been documented in studies of social networks, including Gaussian/Poissonian distributions in offline social groups (Amaral et al 2000), online forums, and active email networks (Liben-Nowell and Kleinberg 2008, Newman et al 2002, Yeung 2005, as well as lognormal distributions in a Web linkage structure and in online social networks (Gomez et al 2009, Pennock et al 2002, Stutzbach and Rejaie 2005. Here, we model the network structure-specifically, its degree distribution-and we investigate its effect on the adoption curve.…”

Section: Introductionmentioning

confidence: 99%

Network Traces on Penetration: Uncovering Degree Distribution from Adoption Data

2012

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W e show how networks modify the diffusion curve by affecting its symmetry. We demonstrate that a network's degree distribution has a significant impact on the contagion properties of the subsequent adoption process, and we propose a method for uncovering the degree distribution of the adopter network underlying the dissemination process, based exclusively on limited early-stage penetration data. In this paper we propose and empirically validate a unified network-based growth model that links network structure and penetration patterns. Specifically, using external sources of information, we confirm that each network degree distribution identified by the model matches the actual social network that is underlying the dissemination process. We also show empirically that the same method can be used to forecast adoption using an estimation of the degree distribution and the diffusion parameters at an early stage (15%) of the penetration process. We confirm that these forecasts are significantly superior to those of three benchmark models of diffusion. Our empirical analysis indicates that under heavily right-skewed degree distribution conditions (such as scale-free networks), the majority of adopters (in some cases, up to 75%) join the process after the sales peak. This strong asymmetry is a result of the unique interaction between the dissemination process and the degree distribution of its underlying network.

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“…Preliminary answers to the "how" come from several works that analyzed the "macroscopic" effects (effects on structure) depending on relationships reconstructed from the messages sent [13,14] or also considering the type of interaction being held [15]. This trend on acquiring knowledge from interactions was also followed by Erlin et al, who considered the content under discussion in addition to the interactions themselves [16].…”

Section: Introductionmentioning

confidence: 99%

The Weakness of Weak Ties in the Classroom

Vaquero,

Cebrian

2012

Preprint

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Granovetter's "strength of weak ties" hypothesizes that isolated social ties offer limited access to external prospects, while heterogeneous social ties diversify one's opportunities. We analyze the most complete record of college student interactions to date (approximately 80,000 interactions by 290 students -16 times more interactions with almost 3 times more students than previous studies on educational networks) and compare the social interaction data with the academic scores of the students. Our first finding is that social diversity is negatively correlated with performance. This is explained by our second finding: highly performing students interact in groups of similarly performing peers. This effect is stronger the higher the student performance is. Indeed, low performance students tend to initiate many transient interactions independently of the performance of their target. In other words, low performing students act disassortatively with respect to their social network, whereas high scoring students act assortatively. Our data also reveals that highly performing students establish persistent interactions before mid and low performing ones and that they use more structured and longer cascades of information from which low performing students are excluded.

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Macroscopic study of the social networks formed in web-based discussion forums

Cited by 11 publications

References 6 publications

The rich club phenomenon in the classroom

The rich club phenomenon in the classroom

Network Traces on Penetration: Uncovering Degree Distribution from Adoption Data

The Weakness of Weak Ties in the Classroom

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