Not only Online Review but also its Helpfulness is Manipulated: Evidence from Peer to Peer Lending Forum

Online reviews have become proposed as useful information for consumers to make decision. Meanwhile, review manipulation will weaken the credibility of online reviews. Except manipulating the review text and rating, we propose that review helpfulness, an important signal for consumer to filter the reviews, could also be manipulated. This study thus explores the existence of review helpfulness manipulation and the relationship between firm quality and review manipulation. Based on a dataset from a review forum in www.wdzj.com which is the leading and largest portal of peer to peer lending industry in China, we get the following interesting results. First, due to the manipulation of review helpfulness, a manipulated positive review is more likely to receive higher helpfulness, while a manipulated negative is more likely to get lower helpfulness. Second, a manipulated review tends to be lower quality in terms of readability and word count, which are found as positive predictors for review helpfulness. Third, high quality firms tend to manipulate more positive reviews, and at the same time high quality firms will receive more negative manipulated reviews. This study extends current understanding about online review manipulation, thereby providing theoretical and practice implications

Transforming Wikipedia into Augmented Data for Query-Focused Summarization

The manual construction of a query-focused summarization corpus is costly and timeconsuming. The limited size of existing datasets renders training data-driven summarization models challenging. In this paper, we use Wikipedia to automatically collect a large query-focused summarization dataset (named as WIKIREF) of more than 280,000 examples, which can serve as a means of data augmentation. Moreover, we develop a query-focused summarization model based on BERT to extract summaries from the documents. Experimental results on three DUC benchmarks show that the model pre-trained on WIKIREF has already achieved reasonable performance. After fine-tuning on the specific datasets, the model with data augmentation outperforms the state of the art on the benchmarks

The Value of Backers’ Word-of-Mouth in Screening Crowdfunding Projects: An Empirical Investigation

Reward-based crowdfunding is an emerging financing channel for entrepreneurs to raise money for their innovative projects. How to screen the crowdfunding projects is critical for crowdfunding platform, project founder, and potential backers. This study aims to investigate whether backers’ word-of-mouth (WOM) is a valuable input to generate collective intelligence for project screening. Specially, we answer three questions. First, is backers’ WOM an effective signal for implementation performance of crowdfunding projects? Second, how do the WOM help screen projects during the fund-raising process? Third, which kind of comments (positive or negative) is more effective in screening crowdfunding projects? Research hypotheses were developed based on theories of collective intelligence and WOM communication. Using a cross section dataset and a panel dataset, we get the following findings. First, backers’ negative WOM can effectively predict project implementation performance, however positive WOM does not have that prediction power. The prediction power of positive and negative WOM differs significantly. One possible reason is that negative WOM does contain more information of project quality. Second, project with more accumulative negative WOM tend to attract fewer subsequent backers. However, accumulative positive WOM is not helpful for attracting more potential backers. We conclude that negative WOM is useful for project screening project, because it is a signal of project quality, and meanwhile it could prevent backers make subsequent investments

A Major Ingredient of Green Tea Rescues Mice from Lethal Sepsis Partly by Inhibiting HMGB1

Background. The pathogenesis of sepsis is mediated in part by bacterial endotoxin, which stimulates macrophages/ monocytes to sequentially release early (e.g., TNF, IL-1, and IFN-c) and late (e.g., HMGB1) pro-inflammatory cytokines. Our recent discovery of HMGB1 as a late mediator of lethal sepsis has prompted investigation for development of new experimental therapeutics. We previously reported that green tea brewed from the leaves of the plant Camellia sinensis is effective in inhibiting endotoxin-induced HMGB1 release. Methods and Findings. Here we demonstrate that its major component, (-)-epigallocatechin-3-gallate (EGCG), but not catechin or ethyl gallate, dose-dependently abrogated HMGB1 release in macrophage/monocyte cultures, even when given 2–6 hours post LPS stimulation. Intraperitoneal administration of EGCG protected mice against lethal endotoxemia, and rescued mice from lethal sepsis even when the first dose was given 24 hours after cecal ligation and puncture. The therapeutic effects were partly attributable to: 1) attenuation of systemic accumulation of proinflammatory mediator (e.g., HMGB1) and surrogate marker (e.g., IL-6 and KC) of lethal sepsis; and 2) suppression of HMGB1-mediated inflammatory responses by preventing clustering of exogenous HMGB1 on macrophage cell surface. Conclusions. Taken together, these data suggest a novel mechanism by which the major green tea component, EGCG, protects against lethal endotoxemia and sepsis

UNDERSTANDING INVESTMENT INTENTION TOWARDS P2P LENDING: AN EMPIRICAL STUDY

P2P lending is an innovation of micro-financial operation pattern, which is mainly used to meet the petty loan and investment demands of small and micro businesses and individuals. Given the rapid development of P2P market, there is a pressing need to understand lenders’ initial investment intentions in P2P platform. Although there are some studies exploring the factors explaining P2P lenders’ investment intentions, none of research has been reported from the perspective of the platform. This study extended technology acceptance model with perceived risk and initial trust as a theoretical framework to examine the roles of individual factors and platform factors in determining P2P lenders’ initial investment intentions. This study suggests that risk appetite, trust propensity, perceived ease of use, perceived security assurance, perceived privacy protection, perceived reputation, third-party certification, perceived risk and initial trust together provide a strong explanation for initial investment intention in P2P lending. The finding of this research provided a theoretical foundation for future academic studies as well as practical guidance for rapid development of P2P platform

The Antecedents and Consequences of Crowdfunding Investors’ Citizenship Behaviors – an Empirical Research on Motivations and Stickiness

This study investigates the antecedents (internal and external motivations) and consequences (stickiness intentions) of crowdfunding investors’ citizenship behavior. In addition, this study examines the moderating effects of investors’ perceived project novelty on the relationships between motivations and citizenship behavior. Based on a sample of 226 crowdfunding investors, results indicate that internal and external motivations significantly influence investors’ citizenship behavior, which further affect investors’ stickiness intentions. Furthermore, results show that investors’ perceived project novelty moderates the relationships between internal/ external motivation and citizenship behavior

Multi-Objective Evolutionary for Object Detection Mobile Architectures Search

Recently, Neural architecture search has achieved great success on classification tasks for mobile devices. The backbone network for object detection is usually obtained on the image classification task. However, the architecture which is searched through the classification task is sub-optimal because of the gap between the task of image and object detection. As while work focuses on backbone network architecture search for mobile device object detection is limited, mainly because the backbone always requires expensive ImageNet pre-training. Accordingly, it is necessary to study the approach of network architecture search for mobile device object detection without expensive pre-training. In this work, we propose a mobile object detection backbone network architecture search algorithm which is a kind of evolutionary optimized method based on non-dominated sorting for NAS scenarios. It can quickly search to obtain the backbone network architecture within certain constraints. It better solves the problem of suboptimal linear combination accuracy and computational cost. The proposed approach can search the backbone networks with different depths, widths, or expansion sizes via a technique of weight mapping, making it possible to use NAS for mobile devices detection tasks a lot more efficiently. In our experiments, we verify the effectiveness of the proposed approach on YoloX-Lite, a lightweight version of the target detection framework. Under similar computational complexity, the accuracy of the backbone network architecture we search for is 2.0% mAP higher than MobileDet. Our improved backbone network can reduce the computational effort while improving the accuracy of the object detection network. To prove its effectiveness, a series of ablation studies have been carried out and the working mechanism has been analyzed in detail