Economics of ‘Tipping’ Button in Social Media: An Empirical Analysis of Content Monetization

As the success of social media platforms heavily depends on the amount and the nature of user-generated content, content monetization has been introduced as a mechanism to incentivize users to generate content. In particular, content contributors can be paid (i.e. tipped) by readers who like the story. We adopted difference-in-differences approach with robustness matching estimator to examine the impact of content monetization. Our results confirm that the content monetization effectively motivate content demand and supply and also improves content quality. Furthermore, such economic incentives have a spillover effect on ordinary weibo users before they are eligible to adopt “tipping” function. However, the verified users who have already been the experts or celebrities in teh society may be depressed after open application of the program. This result suggests that start-ups are able to survive and earn profit even in markets that are dominated by famous celebrities because of the monetization mechanism

Spillover Effect of Content Marketing in E-commerce Platform under the Fan Economy Era

As the proliferation of social media and live streaming, online celebrity endorsement is a common practice of content marketing in e-commerce platform. Despite the prevalent use of social media and online community, empirical research investigating the economic values of user-generated-content (UGC) and marketer-generated-content (MGC) still lags. This study seeks to contribute theoretically and practically to an understanding of how online celebrity endorsement and fans interaction behaviors affect e-commerce sales. We adopt cross-sectional regression to assess the economic value of online celebrity endorsement, and we employ panel vector autoregressive model to explain the dynamic relationship between marketers’ and consumers’ content marketing behaviors and e-commerce product sales. Empirical results highlight that the interaction within fans community has spillover effect on content marketing under “Fan Economy” era

Internet Celebrity Endorsement: How Internet Celebrities Bring Referral Traffic to E-commerce Sites?

Endorsement marketing has been widely used to generate consumer attention, interest, and purchase behaviors among targeted audience of celebrities. Internet celebrities who become famous by means of the Internet are more dependent on strategy intimacy to appeal to their followers. Limited studies have addressed the new business models in Internet celebrities economy: content advertising and online retailing. Our study aims to examine how Internet celebrity endorsement influencing the consumers’ clickon behaviors and purchase behaviors in the context of e-commerce business. Results suggest that content marketing using Internet celebrity endorsement exhibit a significant role in bringing referral traffic to e-commerce sites but less helpful to boost sales. The impact of Internet celebrity endorsement on consumers’ click-on decisions is U-shaped, but the role of Internet celebrities as online retailers will “shape-flip” such relationship to a negative linear relation. Therefore, Internet celebrity endorsement provides effective ways to bring referral traffic to e-commerce sites

Design, Fabrication, and Characterization of a Bifrequency Colinear Array

Ultrasound imaging with high resolution and large penetration depth has been increasingly adopted in medical diagnosis, surgery guidance, and treatment assessment. Conventional ultrasound works at a particular frequency, with a −6 dB fractional bandwidth of ~70 %, limiting the imaging resolution or depth of field. In this paper, a bi-frequency co-linear array with resonant frequencies of 8 MHz and 20 MHz was investigated to meet the requirements of resolution and penetration depth for a broad range of ultrasound imaging applications. Specifically, a 32-element bi-frequency co-linear array was designed and fabricated, followed by element characterization and real-time sectorial scan (S-scan) phantom imaging using a Verasonics system. The bi-frequency co-linear array was tested in four different modes by switching between low and high frequencies on transmit and receive. The four modes included the following: (1) transmit low, receive low, (2) transmit low, receive high, (3) transmit high, receive low, (4) transmit high, receive high. After testing, the axial and lateral resolutions of all modes were calculated and compared. The results of this study suggest that bi-frequency co-linear arrays are potential aids for wideband fundamental imaging and harmonic/sub-harmonic imaging

Systematic Identification of Synergistic Drug Pairs Targeting HIV

The systematic identification of effective drug combinations has been hindered by the unavailability of methods that can explore the large combinatorial search space of drug interactions. Here we present a multiplex screening method named MuSIC (Multiplex Screening for Interacting Compounds), which expedites the comprehensive assessment of pair-wise compound interactions. We examined ~500,000 drug pairs from 1000 FDA-approved or clinically tested drugs and identified drugs that synergize to inhibit HIV replication. Our analysis reveals an enrichment of anti-inflammatory drugs in drug combinations that synergize against HIV, indicating HIV benefits from inflammation that accompanies its infection. Multiple drug pairs identified in this study, including glucocorticoid and nitazoxanide, synergize by targeting different steps of the HIV life cycle. As inflammation accompanies HIV infection, our findings indicate that inhibiting inflammation could curb HIV propagation. MuSIC can be applied to a wide variety of disease-relevant screens to facilitate efficient identification of compound combinations

Microblog Users’ Life Time Activity Prediction

With the fast development of online social media, social network services have become an important research area nowadays. We are now in the era of social colonization, in which technologies such as Facebook Connect and Google Friend Connect have standardized social functionalities among a vast majority of websites. Particularly, microblog as a new star needs more attention. Although most of current studies have focused on the effect of social network on the diffusion of services or information, usually those studies are descriptions or explanations of what already has happened. Limited study has been conducted focusing on SNS users and analysing their behaviours dynamically. In this paper, we used probability models such as Pareto/NBD and BG/NBD to predict customer lifetime vitality. The data we used include information on users’ tweet and retweet behaviour, such as recency and frequency. Our results showed that both Pareto/NBD model and BG/NBD model showed effective ability to fit and predict SNS users’ usage behaviour on microblog website. Tweet behaviors are more suitable for such probability models than retweet behaviors. Managerial implications of the two models should be highlighted as well. Interaction rate and dropout rate can be considered as the vitality index of the whole user base measuring how active users are and how likely a user is active. Managerial questions such as how active the users are in this platform now and how active the users will be in the future can be answered by applying those models

Predicting microblog users' lifetime activities - A user-based analysis

With the rapid development of online social media, social networking services have become an important research area in recent years. In particular, microblogging as a new social media platform draws much attention from both researchers and practitioners. Although most current studies focus on the effect of social networks on the diffusion of services or information, most are descriptions or explanations of what has already happened. This study focuses on future activity by employing probability models such as the Pareto/NBD and BG/NBD models to predict user lifetime vitality. Three experiments were implemented to test the two models. Our results showed that both the Pareto/NBD model and the BG/NBD model were effective in predicting SNS user usage behavior on microblogging websites. It was found that tweeting behavior is more suitable for such probability models than retweeting behavior and user segmentation can improve prediction accuracy by distinguishing between currently active and inactive users. ? 2014 Elsevier B.V

Towards Dense and Accurate Radar Perception Via Efficient Cross-Modal Diffusion Model

Millimeter wave (mmWave) radars have attracted significant attention from both academia and industry due to their capability to operate in extreme weather conditions. However, they face challenges in terms of sparsity and noise interference, which hinder their application in the field of micro aerial vehicle (MAV) autonomous navigation. To this end, this paper proposes a novel approach to dense and accurate mmWave radar point cloud construction via cross-modal learning. Specifically, we introduce diffusion models, which possess state-of-the-art performance in generative modeling, to predict LiDAR-like point clouds from paired raw radar data. We also incorporate the most recent diffusion model inference accelerating techniques to ensure that the proposed method can be implemented on MAVs with limited computing resources.We validate the proposed method through extensive benchmark comparisons and real-world experiments, demonstrating its superior performance and generalization ability. Code and pretrained models will be available at https://github.com/ZJU-FAST-Lab/Radar-Diffusion.Comment: 8 pages, 6 figures, submitted to RA-

Microblog users' life time activity prediction

Conference Name:2013 10th International Conference on Service Systems and Service Management, ICSSSM 2013. Conference Address: Hong Kong, China. Time:July 17, 2013 - July 19, 2013.IEEE Systems, Man and Cybernetics Society (IEEE SMC); The Chinese University of Hong Kong (CUHK); Research Center for Contemporary Management; of Tsinghua University; Southwest Jiaotong UniversityAs the fast development of online social media, social network services have become an important research area nowadays. Particularly, microblog as new social media needs more attention. Most of current studies are usually static descriptions or explanations of what already has happened. Limited study has been conducted focusing on SNS users and analysing their behaviors dynamically. In this paper, we firstly segment microblog users based on the recency and frequency of tweet and retweet behavior, then use probability models such as Pareto/NBD and BG/NBD to predict customer lifetime vitality. Our results showed that both Pareto/NBD model and BG/NBD model showed effective ability to fit and predict SNS users' usage behavior on microblog website. Tweet behaviors of sustainably active user base are more suitable for the probability models. Managerial implications of the two models should be highlighted as well. Interaction rate and dropout rate can be considered as the vitality index of the whole user base measuring how active users are and how likely a user is active. Managerial questions such as how active the users are in this platform now and how active the users will be in the future can be answered by applying those models. ? 2013 IEEE