Reward-based Crowdfunding Success Prediction with Multimodal Data

As an increasing number of crowdfunding platforms recommend that entrepreneurs post multimodal data to improve data diversity and attract investors’ attention, it becomes necessary to study how functions of multimodal data take effect to predict fundraising outcomes (i.e., success or failure). There is a lack of research providing a comprehensive investigation of multimodal data in crowdfunding. Rooted in language and visual image metafunctional theories, we propose a framework to explore ideational, interpersonal, and textual metafunctions of multimodal data. We empirically examine the effectiveness of each metafunction, each modality, and their combination in predicting fundraising outcomes. The empirical evaluation shows the predictive utility of any metafunctions and metafunction combinations. The results also demonstrate that adding data modalities can help to improve the prediction performance

Predicting Fundraising Success in Reward-Based Crowdfunding

While there has been a rapid growth of the reward-based crowdfunding market, successfully achieving the target amount of a project is a big challenge. Entrepreneurs are eager to know the prospects of their campaigns. Investors also want to fund promising campaigns with high quality and low uncertainty. Therefore, effectively predicting the success of a project throughout the fundraising period is a crucial task for both entrepreneurs and investors. On the one hand, it provides guidance to entrepreneurs about project progress and potential, helping them adjust their campaigns in time. On the other hand, it helps investors manage their funding risks and reduce opportunity costs. However, most of the existing research has been aimed to explore the determinants of fundraising success, but much less attention has been paid to the success prediction problem. We explore the crowdfunding success prediction problem from two perspectives in the following two essays.In Essay 1, we mine semantic features from comments to improve fundraising success prediction. More and more participants share and discuss facts and opinions about projects by posting comments, which can influence investors’ funding decisions. Previous studies have mainly focused on quantity, sentiment, and linguistic features of comments, largely overlooking the value of semantic features, in predicting fundraising success. Rooted in information asymmetry and herding behavior theories, we posit that discovering semantic signals from comments and distinguishing actor roles will benefit fundraising success prediction. We propose a framework with novel latent semantic features of comments. Empirical evaluation using data from a prominent platform demonstrates the utility of the framework and reveals interesting patterns in the dynamic predictive effects of semantic features for different actor roles. In Essay 2, we apply features from multimodal data (texts, images, and videos) to improve fundraising success prediction. With the development in artificial intelligence and big data, multimodality has become one of the popular research areas of IS since multiple modalities can provide complementary information and improve the performance of the overall decision-making process. However, there is a lack of research providing a comprehensive investigation of multimodal data in crowdfunding. To gain a comprehensive review of linguistic and visual features of multimodality in crowdfunding, we propose a framework built on theories of Halliday’s metafunctions framework of languages (1985), Kress and Van Leeuwen’s functional visual design (1996), and Royce’s intersemiotic complementarity of languages and visual images (1998) to explore relevant features representing the ideational, interpersonal, and textual metafunctions of multimodal data in crowdfunding. We have conducted several experiments to study the effectiveness of each metafunction, each modality, and their interactions in predicting crowdfunding success

Remote sensing image classification based on optimized support vector machine

To resolve the problem of wetland remote sensing image classification, this paper presents an improved classification algorithm. In this algorithm, genetic algorithm (GA) selection, crossover operation is introduced to the standard particle swarm optimization algorithm (PSO) to form a hybrid particle swarm optimization algorithm (GAPSO). The hybrid algorithm can exploit the advantages of the genetic algorithm and particle swarm algorithm respectively to the full to obtain the global optimal parameters of support vector machine (SVM). Thus the wetland remote sensing image can be classified more accurately. Taking Ningxia Shahu wetland remote sensing images as an example, this paper makes a classification of wetland remote sensing images using optimized support vector machine, and the outcome of the experiment shows that this algorithm has better classification effect than that of similar algorithms. DOI : http://dx.doi.org/10.11591/telkomnika.v12i2.4325 

PGC-1α Protects against Hepatic Ischemia Reperfusion Injury by Activating PPARα and PPARγ and Regulating ROS Production

Peroxisome proliferator-activated receptors (PPARs) α and γ have been shown to be protective in hepatic ischemia/reperfusion (I/R) injury. However, the precise role of PPARγ coactivator-1α (PGC-1α), which can coactivate both of these receptors, in hepatic I/R injury, remains largely unknown. This study was designed to test our hypothesis that PGC-1α is protective during hepatic I/R injury in vitro and in vivo. Our results show that endogenous PGC-1α is basally expressed in normal livers and is moderately increased by I/R. Ectopic PGC-1α protects against hepatic I/R and hepatocyte anoxia/reoxygenation (A/R) injuries, whereas knockdown of endogenous PGC-1α aggravates such injuries, as evidenced by assessment of the levels of serum aminotransferases and inflammatory cytokines, necrosis, apoptosis, cell viability, and histological examination. The EMSA assay shows that the activation of PPARα and PPARγ is increased or decreased by the overexpression or knockdown of PGC-1α, respectively, during hepatic I/R and hepatocyte A/R injuries. In addition, the administration of specific antagonists of either PPARα (MK886) or PPARγ (GW9662) can effectively decrease the protective effect of PGC-1α against hepatic I/R and hepatocyte A/R injuries. We also demonstrate an important regulatory role of PGC-1α in reactive oxygen species (ROS) metabolism during hepatic I/R, which is correlated with the induction of ROS-detoxifying enzymes and is also dependent on the activations of PPARα and PPARγ. These data demonstrate that PGC-1α protects against hepatic I/R injury, mainly by regulating the activation of PPARα and PPARγ. Thus, PGC-1α may be a promising therapeutic target for the protection of the liver against I/R injury