Predicting the Way and the Degree of Users’ Content Contribution in the Social Question and Answer Community

Most predictions of user behavior occur after a user has participated in the community for a while, and those who have just registered are easily overlooked because their community characteristics have not yet been revealed. However, users are easy to be lost in the early stage. Based on the theory of social capital, this paper proposes a new approach to predict the willingness, mode, and degree of content contribution of the newly registered user based on users\u27 information disclosure behavior aiming at reducing the churn rate of newly registered users. We crawled the data of 4 million users in the Zhihu community and deeply studied the relationship between the disclosure behavior of different types of information and the content contribution degree of users through statistical analysis methods and machine learning algorithms. The result shows that if a user discloses personal information, the probability of his in-depth response contribution and in-depth questioning contribution will increase correspondingly, and different types of information disclosure will lead to a different probability of an increase. Furthermore, In addition, users\u27 disclosure of different types of information will lead to differences in their preference for the way they contribute content

Compliments to Accomplishments: The Effect of Compliments by Digital Platforms on Consumer Behavior

When shopping online, consumers sometimes hesitate, for example, because they are uncertain about product quality, or they do not know whether the price is reasonable. In the offline shopping context, sellers can encourage purchases by complimenting consumers. This study aims to explore how digital platforms can adopt the compliment tactic to catalyze consumers’ purchase decisions. We hypothesize that online compliments, like offline compliments, can effectively reduce consumers’ uncertainties in online shopping and thus encourage purchases. We plan to run a lab experiment to test the hypothesis. This study enhances previous research on offline compliments and contributes to e-commerce research by providing causal evidence of how digital platforms can use compliments to influence consumer behavior

Detecting space–time agglomeration processes over the Great Recession using firm-level micro-geographic data

We analyze the spatio-temporal agglomeration dynamics that occurred in the Italian manufacturing industry during the recent period of the Great Recession. To study this phenomenon, we employ three different statistical methods—namely, Ellison and Glaeser’s index of industrial geographic concentration, the spatial K-function, and the space–time K-function—, and rely on a large sample of geo-referenced, single-plant manufacturing firms observed over the period 2007–2012. First, we demonstrate that different statistical techniques can lead to (very) different results. Second, we find that most Italian manufacturing sectors experienced spatial dispersion processes during the period of the Great Recession. Finally, we show that space–time dispersion processes occurred at small spatial distances and short time horizon, although we do not detect statistically significant space–time interactions

Analysis of How to Improve Internal Staff Cohesion from the Perspective of Knowledge Management and Cultural Management——Taking ByteDance as an Example

Knowledge management and cultural management, as two modules in enterprise management, do not have any relationship or connection between gain and cohesion. By taking ByteDance as an example, this study discusses the positive impact of good knowledge management and cultural management system on the cohesion of employees and the development of the company from the perspectives of internal and external atmosphere achievements. The research concludes that a good knowledge management and cultural management system has a positive impact on the cohesion of employees, and has a positive effect on the development of the enterprise

Understanding User Contributions in Smoking cessation Online Health Communities

Users make contributions to online communities in different ways. Prior literature has rarely investigated how different user groups make contributions to smoking cessation OHCs. To illuminate the contribution of different user groups in smoking cessation OHCs, this study aims to evaluate user contribution from two dimensions (Content-contribution and popularity) associated with users’ questioning and answering behaviors. Based on the user log data collected from a smoking cessation OHC in Finland (Stumppi.fi), we plan to assess user contribution level for four different user groups (lurker, conversation-starter, conversation-replier, and Conversation-starter & replier) based on user activity data via applying entropy method. The research might make theoretical contributions to the literature on user contribution and offer practical implications to smoking cessation OHC service providers

DROP: Dynamics Responses from Human Motion Prior and Projective Dynamics

Synthesizing realistic human movements, dynamically responsive to the environment, is a long-standing objective in character animation, with applications in computer vision, sports, and healthcare, for motion prediction and data augmentation. Recent kinematics-based generative motion models offer impressive scalability in modeling extensive motion data, albeit without an interface to reason about and interact with physics. While simulator-in-the-loop learning approaches enable highly physically realistic behaviors, the challenges in training often affect scalability and adoption. We introduce DROP, a novel framework for modeling Dynamics Responses of humans using generative mOtion prior and Projective dynamics. DROP can be viewed as a highly stable, minimalist physics-based human simulator that interfaces with a kinematics-based generative motion prior. Utilizing projective dynamics, DROP allows flexible and simple integration of the learned motion prior as one of the projective energies, seamlessly incorporating control provided by the motion prior with Newtonian dynamics. Serving as a model-agnostic plug-in, DROP enables us to fully leverage recent advances in generative motion models for physics-based motion synthesis. We conduct extensive evaluations of our model across different motion tasks and various physical perturbations, demonstrating the scalability and diversity of responses.Comment: SIGGRAPH Asia 2023, Video https://youtu.be/tF5WW7qNMLI, Website: https://stanford-tml.github.io/drop

4-(4,4-Difluoro-1,3,5,7-tetra­methyl-3a-aza-4a-azonia-4-borata-s-indacen-8-yl)benzonitrile

The title compound, C20H18BF2N3, contains one C9BN2 (Bodipy) framework and one cyano­benzyl group. The Bodipy framework is essentially planar with a maximum deviation of 0.041 (2) Å. The introduction of two methyl groups at positions 1 and 7 of s-indacene in the Bodipy unit results in almost orthogonal configuration between the mean plane of the Bodipy unit and the cyano­benzyl group [dihedral angle = 89.78 (4)°]