Data Mining Practical Machine Learning Tools and Techniques

What makes organizations so similar? We contend that the engine of rationalization and bureaucratization has moved from the competitive marketplace to the state and the professions. Once a set of organizations emerges as a field, a paradox arises: rational actors make their organizations increasingly similar as they try to change them. We describe three isomorphic processes-coercive, mimetic, and normative—leading to this outcome. We then specify hypotheses about the impact of resource centralization and dependency, goal ambiguity and technical uncertainty, and professionalization and structuration on isomorphic change. Finally, we suggest implications for theories of organizations and social change.

The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields (Chinese Translation)

人教版高中英语新课程教材中,语言运用（Using Language）是每个单元必不可少的部分,提供了围绕单元中心话题的听、说、读、写的综合性练习,是单元中心话题的延续和升华.如何设计Using Language部分的教学,使自己的教学模式既不落俗套,又能真正体现新课程标准所倡导的教学理念,正是广大一线英语教师一直努力探索的问题.

打磨Using Language,倡导新理念

Influencers on Instagram: Antecedents and consequences of opinion leadership

Text categorization-assignment of natural language texts to one or more predefined categories based on their content-is an important component in many information organization and management tasks.Different automatic learning algorithms for text categori-zation have different classification accuracy.Very accurate text classifiers can be learned automatically from training examples.

Inductive learning algorithms and representations for text categorization

We study the drivers of the emergence of opinion leaders in a networked community where users establish links to others, indicating their “trust” for the link receiver's opinion. This leads to the formation of a network, with high in-degree individuals being the opinion leaders. We use a dyad-level proportional hazard model with time-varying covariates to model the growth of this network. To estimate our model, we use Weighted Exogenous Sampling with Bayesian Inference, a methodology that we develop for fast estimation of dyadic models on large network data sets. We find that, in the Epinions network, both the widely studied “preferential attachment” effect based on the existing number of inlinks (i.e., a network-based property of a node) and the number and quality of reviews written (i.e., an intrinsic property of a node) are significant drivers of new incoming trust links to a reviewer (i.e., inlinks to a node). Interestingly, we find that time is an important moderator of these effects—intrinsic node c...

/pdf/the-emergence-of-opinion-leaders-in-a-networked-online-1u5gytel9z.pdf

The Emergence of Opinion Leaders in a Networked Online Community: A Dyadic Model with Time Dynamics and a Heuristic for Fast Estimation

We study the drivers of the emergence of opinion leaders in a networked community where users share information with each other. Our specific setting is that of Epinions.com, a website dedicated to user-generated product reviews. Epinions.com employs a novel mechanism in which every member of the community can include other members, whose reviews she trusts, in her “web of trust.” This leads to the formation of a network of trust among reviewers with high in-degree individuals being the opinion leaders. Accordingly, we study the emergence of opinion leaders in this community using a network formation paradigm. We model network growth by using a dyad-level proportional hazard model with time-varying covariates. To estimate this model, we use Weighted Exogenous Sampling with Bayesian Inference (WESBI), a methodology that we develop for fast estimation of dyadic models on large network datasets. We find that, in the Epinions network, both the widely-studied “preferential attachment” effect based on the existing number of inlinks (i.e., a network-based property of a node) and the number and quality of reviews written (i.e., an intrinsic property of a node) are significant drivers of new incoming trust links to a reviewer (i.e., inlinks to a node). Interestingly, time is an important moderator of these effects — the number of recent reviews written has a stronger effect than the effect of the number of recent inlinks received on the current rate of attracting inlinks; however, the aggregate number of reviews written in the past has no effect, while the aggregate number of inlinks obtained in the past has a significant effect on the current rate of attracting inlinks. This leads to the novel and important implication that, in a network growth setting, intrinsic node characteristics are a stronger short-term driver of additional inlinks, while the preferential attachment effect has a smaller impact but it persists for a longer time. We discuss the managerial implications of our results for the design and organization of online review communities.The Online Technical Appendix for this paper is available at the following URL: http://ssrn.com/abstract=2206016

/pdf/everyone-can-be-a-star-quantifying-grassroots-online-sellers-41fhvuzqhd.pdf

Everyone Can Be a Star: Quantifying Grassroots Online Sellers’ Live Streaming Effects on Product Sales

Many companies have adopted technology driven social learning platforms such as social customer relationship management (crowdsourcing customer support) to support knowledge sharing among customers. A number of these self-evolving, online customer support communities have reported the emergence of a core- periphery knowledge sharing network structure. In this study, we investigate why such a structure emerges and its implications for knowledge sharing within the community. We propose a dynamic structural model with endogenized knowledge-sharing and network formation. Our model recognizes the dynamic and interdependent nature of knowledge seeking and sharing decisions and allows them to be driven by knowledge increments and social status building in anticipation of future reciprocal rewards from peers.

Applying this model to a fine grained panel dataset from a social customer support forum for a telecom firm, we illustrate that a user in this community gains value from being linked to other individuals with higher social status. As a result, a user is more inclined to answer the questions of those who are in the core (well connected) than questions from those who are in the periphery (not well connected). We find that users are taking into account the expected likelihood of their questions receiving a solution before asking a question. With the emergence of a core-periphery network structure, peripheral individuals are discouraged from asking questions as their expectation of receiving a solution to their question is very low. Thus, the core-periphery structure has created a barrier to knowledge flow to new customers who need the knowledge the most. Our counterfactuals show that hiding the identity of the knowledge seeker or making the individual contributions obsolete faster helps break the core-periphery structure and improves knowledge sharing in the community.

Is a core-periphery network good for knowledge sharing? a structural model of endogenous network formation on a crowdsourced customer support forum

Many companies have adopted technology driven social learning platforms such as social CRM (crowdsourcing customer support from customers) to support knowledge sharing among customers. A number of these self-evolving online customer support communities have reported the emergence of a core-periphery knowledge sharing network structure. In this study, we investigate why such a structure emerges and its implications for knowledge sharing within the community. We propose a dynamic structural model with endogenized knowledge-sharing and network formation. Our model recognizes the dynamic and interdependent nature of knowledge-seeking-and-sharing decisions and allows them to be driven by knowledge increments and social status building in anticipation of future reciprocal rewards from peers. Applying this model to a fine grained panel data set from a social customer support forum for a telecom firm, we illustrate that a user in this community values being connected to other well connected individuals. As a result, a user is more inclined to answer questions of those who are in the core (well connected) than the ones who are in the periphery (not well connected). We find that users are taking into account the expected likelihood of their questions receiving a solution before asking a question. With the emergence of core-periphery network structure, the peripheral individuals are discouraged from asking questions as their expectation of receiving a solution to their question is very low. Thus, the core-periphery structure has created a barrier to knowledge flow to new customers who need the knowledge the most. Our counterfactuals show that hiding the identity of the knowledge seeker or making the individual contributions obsolete faster helps break the core-periphery structure and improves knowledge sharing in the community.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2639510_code588223.pdf?abstractid=2486892&mirid=5

Yingda Lu

Papers

The Emergence of Opinion Leaders in a Networked Online Community: A Dyadic Model with Time Dynamics and a Heuristic for Fast Estimation

The Emergence of Opinion Leaders in a Networked Online Community: A Dyadic Model with Time Dynamics and a Heuristic for Fast Estimation

Everyone Can Be a Star: Quantifying Grassroots Online Sellers’ Live Streaming Effects on Product Sales

Is a core-periphery network good for knowledge sharing? a structural model of endogenous network formation on a crowdsourced customer support forum

Is Core-Periphery Network Good for Knowledge Sharing? A Structural Model of Endogenous Network Formation on a Crowdsourced Customer Support Forum