Cross-Promotion in Social Media: Choosing the Right Allies

This paper investigates the strategic use of cross-promotion for content producers in social media. In particular, we study how a producer chooses other producers to cross-promote so as to maximize the expected benefits of them cross-promoting him/her in return. Theories on homophily effect and social influence suggest that cross-promoted producers are more likely to cross-promote the initiator in return when they are in the similar categories or share more common friends and when the initiator has higher status. However, the cross-promotion from producers of different categories and social groups (i.e., share fewer common friends) tend to benefit the initiator more. The benefits also increase as the status of the initiator increases. We collected a panel of data consisting of 27,356 producers’ profile and status information, content categories, and their cross-promotion activities over a period of two months from YouTube. To test our hypotheses, we first employ a cox proportional hazard model to estimate the probability of cross-promotion in return. Then, we use a difference-in-differences method with panel fixed effects to evaluate the effect of cross-promotion in return on the initiator. Our results strongly support our hypotheses and provide valuable insights for both content producers and social media platforms

A Study of American Individualism: Taking Friends as an Exampl

Friends are a famous American situation comedy since 1990s. It tells the story of six “common” youths living in New York of the United States including their emotion, career, joys and pains. Based on the study of Friends case, this article analyses the importance of American individualism to American culture, and tells us the reasons why individualism became the core of American culture. It also discusses the concrete embodiment of individualism value in American daily life from the life, career and feelings. This paper understands the importance of American individualism in American culture through current situations of American individualism. It helps us to understand deeply the impact and important significance on American culture which is caused by the American individualism. And helps us to better understand and look at the essence of American society and culture, understand the difference between USA and China. It also contributes a lot to promote cultural exchanges and common progresses between China and America and common progress.

The Janus Face of Cross-Platform Spillover: Who Reap the Benefits?

The booming of online platforms has attracted academia’s increasing interest in cross-platform spillover of product consumption. This study investigates how physicians’ content creation in Tik Tok influences patients’ demands, comments and satisfaction towards the physicians on online health communities (OHCs). Using the difference-in-differences approach, we uncover asymmetric influences of cross-platform spillovers for high- and low-awareness physicians in Tik Tok. Specifically, low-awareness physicians do not enjoy the benefits (i.e., the increased volume of orders and comments on OHC) from content creation in Tik Tok, but their ratings turn to decline due to attention distraction caused by cross-platform activities. Conversely, for high-awareness physicians, we find a positive cross-platform spillover effect for orders and comments on OHC without decreasing their ratings. Despite the existence of attention distraction from cross-platform services for high-awareness physicians, the negative impact on feedbacks is offset by higher ratings from their cross-platform consumers

Dynamic pricing and inventory control of online retail of fresh agricultural products with forward purchase behavior

In this paper we formulate and analyze a novel model on a retailer’s inventory and pricing decisions for fresh agricultural products with consumers’ forward consuming behavior under online channel. We consider a dynamic stochastic setting, where every period consists of two stages, discounting pricing stage and regular sale stage. At the beginning of the period, the retailer decides how much new fresh agricultural products to order and sets discount price for leftover inventories from the previous period which will be disposed otherwise, and determines regular price for fresh products on the second stage, respectively. Since forward purchase consumers may buy the products during discount pricing stage, which may cannibalize future sales at regular price, the retailer needs to make a trade-off decision between regular price and discounting price. We bring forward a dynamic optimization model and use nonlinear programming method of Karush Kuhn Tucker condition to obtain the optimal dynamic strategy, which is comparatively analyzed to dominate the related static strategy. We also show that consumers forward buying behavior will negatively influence the retailer’s profit. When the price is set too low in regular or discounting sales, the profit will show an up-down trend if the inventory exceeds a certain threshold. Meanwhile, when fresh goods returns are allowed and resold in the secondary stage, the retailer’s profit will increase. We finally conduct numerical studies to further characterize the optimal policy, and to evaluate the loss of efficiency under static policies when compared to the optimal dynamic policy

Efficacy and prognostic factors of concurrent chemoradiotherapy in patients with stage Ib3 and IIa2 cervical cancer

Objectives: We investigated the efficacy, side effects, and prognostic factors of concurrent chemoradiotherapy for patientswith stage Ib3-IIa2 cervical cancer.Material and methods: We conducted a retrospective analysis of clinicopathologic data from 73 patients with stageIb3-IIa2 cervical cancer who received concurrent chemoradiotherapy from January 2008 to December 2013 in our hospital.Overall response and disease control rates were used to evaluate short-term outcomes; the 3-year and 5-year disease-freesurvival and overall survival were used to evaluate long-term efficacy. Toxicity reactions and prognostic factors were recorded.Results: With concurrent chemoradiotherapy, overall response and disease control rates were 91.78% and 97.26%, respectively.The 3-year disease-free and overall survival were 80.82% and 83.56%; the 5-year disease-free and overall survival were 75.34%and 79.45%, respectively. All side effects were tolerated and potentially alleviated by symptomatic treatment. Tumor pathologicaltype, differentiated degree, primary tumor size and squamous cell carcinoma antigen levels before and after treatment wereclosely related to survival (univariate analysis; p < 0.05). Pathological type, primary tumor size and squamous cell carcinomaantigen levels one month after treatment were independent prognostic factors for long-term outcome (multivariate analysis).Conclusions: Short- and long-term efficacy of concurrent chemoradiotherapy for stage Ib3-IIa2 cervical cancer is well-determinedand tolerable. Patients with adenocarcinomas, tumor diameter ≥ 5 cm and squamous cell carcinoma antigenlevels ≥ 1.5 ng/mL (one month after treatment) had poor prognosis and should be assessed further

Online Content Consumption: Social Endorsements, Observational Learning and Word-of-Mouth

The consumption of online content can occur through observational learning (OL) whereby consumers follow previous consumers’ choices or social endorsement (SE) wherein consumers receive content sharing from their social ties. As users consume content, they also generate post-consumption word-of-mouth (WOM) signals. OL, SE and WOM together shape the diffusion of the content. This study examines the drivers of SE and the effect of SE on content consumption and post-consumption WOM. In particular, we compare SE with OL. Using a random sample of 8,945 new videos posted on YouTube, we collected a multi-platform dataset consisting of data on video consumption and WOM from YouTube and data on tweet sharing of the video from Twitter. Applying a panel vector autoregression (PVAR) model, we find that OL increases consumption significantly more than SE in the short run. However, SE has a stronger effect on content consumption in the long run. This can be attributed to the impact of SE on WOM signals, which also increase content consumption. While OL and SE leads to similar amount of positive WOM, SE generates significantly more negative WOM than OL. Our results also show that SE is driven by WOM (i.e., likes and dislikes) but not content popularity. We further confirm the effects of OL vs. SE on content consumption and WOM using a randomized experiment at the individual consumer level. Implications for content providers and social media platforms are derived accordingly

AKConv: Convolutional Kernel with Arbitrary Sampled Shapes and Arbitrary Number of Parameters

Neural networks based on convolutional operations have achieved remarkable results in the field of deep learning, but there are two inherent flaws in standard convolutional operations. On the one hand, the convolution operation be confined to a local window and cannot capture information from other locations, and its sampled shapes is fixed. On the other hand, the size of the convolutional kernel is fixed to k $\times$ k, which is a fixed square shape, and the number of parameters tends to grow squarely with size. It is obvious that the shape and size of targets are various in different datasets and at different locations. Convolutional kernels with fixed sample shapes and squares do not adapt well to changing targets. In response to the above questions, the Alterable Kernel Convolution (AKConv) is explored in this work, which gives the convolution kernel an arbitrary number of parameters and arbitrary sampled shapes to provide richer options for the trade-off between network overhead and performance. In AKConv, we define initial positions for convolutional kernels of arbitrary size by means of a new coordinate generation algorithm. To adapt to changes for targets, we introduce offsets to adjust the shape of the samples at each position. Moreover, we explore the effect of the neural network by using the AKConv with the same size and different initial sampled shapes. AKConv completes the process of efficient feature extraction by irregular convolutional operations and brings more exploration options for convolutional sampling shapes. Object detection experiments on representative datasets COCO2017, VOC 7+12 and VisDrone-DET2021 fully demonstrate the advantages of AKConv. AKConv can be used as a plug-and-play convolutional operation to replace convolutional operations to improve network performance. The code for the relevant tasks can be found at https://github.com/CV-ZhangXin/AKConv.Comment: 10 pages, 5 figure