Analysis of Knowledge Sharing Activities on a Social Network Incorporated Discussion Forum: A Case Study of DISboards

Li, Zhuozhao; Chandler, Harrison; Shen, Haiying

doi:10.1109/tbdata.2017.2749307

Cited by 7 publications

(7 citation statements)

References 28 publications

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“…Measurements on local clustering coefficients agree with the results presented here, showing that the clustering coefficient is rather small and is decreasing with increasing node degree [46]- [49]. Measurement results in the high node degree region are however not really reliable due to the small number of the samples.…”

Section: Resultssupporting

confidence: 83%

“…Many of the measured large social networks show power-law degree distribution [7], [47], [49], supporting out analysis. However, in some cases the decay of the node degrees is faster, [45], [48], showing the need of modeling the limiting effects on link formation.…”

Section: Resultssupporting

confidence: 62%

“…Let us validate the characteristics of the proposed network model utilizing large scale measurement results from the literature [7], [32], [34], [45]- [49].…”

Section: Resultsmentioning

confidence: 99%

“…Measurement results on on-line social networks, however, often show a transition, and large networks are disassortative [34], [45], [46], a result that complies with our conclusions. Still, many measurements, often considering networks of smaller size, show that the networks are neutral [46]- [49]. This shows that further studies are needed on attachment preferences.…”

Section: Resultsmentioning

confidence: 99%

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Will Scale-Free Popularity Develop Scale-Free Geo-Social Networks?

Liu

Fodor

Rasmussen

2019

IEEE Trans. Netw. Sci. Eng.

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Empirical results show that spatial factors such as distance, population density and communication range affect our social activities, also reflected by the development of ties in social networks. This motivates the need for social network models that take these spatial factors into account. Therefore, in this paper we propose a gravity-low-based geo-social network model, where connections develop according to the popularity of the individuals, but are constrained through their geographic distance and the surrounding population density. Specifically, we consider a power-law distributed popularity, and random node positions governed by a Poisson point process. We evaluate the characteristics of the emerging networks, considering the degree distribution, the average degree of neighbors and the local clustering coefficient. These local metrics reflect the robustness of the network, the information dissemination speed and the communication locality. We show that unless the communication range is strictly limited, the emerging networks are scale-free, with a rank exponent affected by the spatial factors. Even the average neighbor degree and the local clustering coefficient show tendencies known in non-geographic scale-free networks, at least when considering individuals with low popularity. At high-popularity values, however, the spatial constraints lead to popularity-independent average neighbor degrees and clustering coefficients.The first attempt to describe communities with random connection patterns dates back to the Erdos-Renyi (ER) random graph model from 1959 [19]. This model, however, failed to describe some inherent properties of networks that emerged through social connections: the ER model shows the desired small-world phenomenon, but not the clustering experienced in real networks. This called for more complex network structures, such as the Watts-Strogatz (WS) smallworld network [20], providing high clustering and tunable average path length, still preserving homogeneous connectivity with a Poisson degree distribution. It has been recognized, though that many of the networks, from biological structures to the Internet, are not homogeneous, but scalefree [21], where the degree distribution follows a power law k −γ , k denoting the degree and γ the scaling exponent, with typical values of 2 < γ < 3.The most well-known example of constructing a scalefree topology is the Barabasi-Albert model [9], where the network is formed by adding new vertices, and connect them through a preferential attachment strategy. The link preference in the BA model is proportional to the degree of vertices already in network, which results in a scale-free network with scaling exponent γ = 3. More flexible versions of the preferential attachment model are presented in [22] [23] [24].Another hypothesis of the emergence of scale-free networks is based on the inherent popularity of the members of the network. This hypothesis has been proposed in [10], [11] and is extensively tested for interactions of Wikipedia contributors in [7], [...

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Section: Resultssupporting

confidence: 83%

Section: Resultssupporting

confidence: 62%

“…Let us validate the characteristics of the proposed network model utilizing large scale measurement results from the literature [7], [32], [34], [45]- [49].…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Will Scale-Free Popularity Develop Scale-Free Geo-Social Networks?

Liu

Fodor

Rasmussen

2019

IEEE Trans. Netw. Sci. Eng.

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“…Users can respond to another person's thread, which is referred to as a "reply." Thus, a post made by a user on a forum can either be an original thread created by the user or a reply to another user's thread [36]. We crawled all threads on the 3 focus disease forums from their respective forum establishment dates (LCF: November 15, 2013; BCF: August 25, 2015; DCF: September 1, 2005) to October 20, 2020.…”

Section: Data Collectionmentioning

confidence: 99%

User Behaviors and User-Generated Content in Chinese Online Health Communities: Comparative Study

Lei¹,

Li²,

Zhou³

2021

J Med Internet Res

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Background Online health communities (OHCs) have increasingly gained traction with patients, caregivers, and supporters globally. Chinese OHCs are no exception. However, user-generated content (UGC) and the associated user behaviors in Chinese OHCs are largely underexplored and rarely analyzed systematically, forfeiting valuable opportunities for optimizing treatment design and care delivery with insights gained from OHCs. Objective This study aimed to reveal both the shared and distinct characteristics of 2 popular OHCs in China by systematically and comprehensively analyzing their UGC and the associated user behaviors. Methods We concentrated on studying the lung cancer forum (LCF) and breast cancer forum (BCF) on Mijian, and the diabetes consultation forum (DCF) on Sweet Home, because of the importance of the 3 diseases among Chinese patients and their prevalence on Chinese OHCs in general. Our analysis explored the key user activities, small-world effect, and scale-free characteristics of each social network. We examined the UGC of these forums comprehensively and adopted the weighted knowledge network technique to discover salient topics and latent relations among these topics on each forum. Finally, we discussed the public health implications of our analysis findings. Results Our analysis showed that the number of reads per thread on each forum followed gamma distribution (HL=0, HB=0, and HD=0); the number of replies on each forum followed exponential distribution (adjusted RL2=0.946, adjusted RB2=0.958, and adjusted RD2=0.971); and the number of threads a user is involved with (adjusted RL2=0.978, adjusted RB2=0.964, and adjusted RD2=0.970), the number of followers of a user (adjusted RL2=0.989, adjusted RB2=0.962, and adjusted RD2=0.990), and a user’s degrees (adjusted RL2=0.997, adjusted RB2=0.994, and adjusted RD2=0.968) all followed power-law distribution. The study further revealed that users are generally more active during weekdays, as commonly witnessed in all 3 forums. In particular, the LCF and DCF exhibited high temporal similarity (ρ=0.927; P<.001) in terms of the relative thread posting frequencies during each hour of the day. Besides, the study showed that all 3 forums exhibited the small-world effect (mean σL=517.15, mean σB=275.23, and mean σD=525.18) and scale-free characteristics, while the global clustering coefficients were lower than those of counterpart international OHCs. The study also discovered several hot topics commonly shared among the 3 disease forums, such as disease treatment, disease examination, and diagnosis. In particular, the study found that after the outbreak of COVID-19, users on the LCF and BCF were much more likely to bring up COVID-19–related issues while discussing their medical issues. Conclusions UGC and related online user behaviors in Chinese OHCs can be leveraged as important sources of information to gain insights regarding individual and population health conditions. Effective and timely mining and utilization of such content can continuously provide valuable firsthand clues for enhancing the situational awareness of health providers and policymakers.

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Research on the mechanism of knowledge diffusion in the MOOC learning forum using ERGMs

2021

Computers & Education

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Analysis of Knowledge Sharing Activities on a Social Network Incorporated Discussion Forum: A Case Study of DISboards

Cited by 7 publications

References 28 publications

Will Scale-Free Popularity Develop Scale-Free Geo-Social Networks?

Will Scale-Free Popularity Develop Scale-Free Geo-Social Networks?

User Behaviors and User-Generated Content in Chinese Online Health Communities: Comparative Study

Research on the mechanism of knowledge diffusion in the MOOC learning forum using ERGMs

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