24,068 research outputs found
The Impact of Online Harassment on the Performance of Projects in Crowdfunding
In the consequence-free and anonymous online environment, online harassment has become a serious problem. In many crowdfunding platforms, there exists offensive speech on the project pages, which might force potential funders to leave the discussion and to give up investment. The effect of online harassment on project performance remains unknown. This study attempts to investigate to what extent the textual online harassment score and the project creator’s attitude towards textual online harassment might affect project performance. We constructed a Kickstarter panel dataset consisting of 388,100 projects and designed a novel framework and an algorithm BiLSTM-CNN to extract the textual online harassment score from comments, which can reach column-wise mean ROC AUC of 0.9463. This study contributes to crowdfunding and online harassment literature and provides important implications for reputation management of projects and crowdfunding platform design
Multi-crop Contrastive Learning for Unsupervised Image-to-Image Translation
Recently, image-to-image translation methods based on contrastive learning
achieved state-of-the-art results in many tasks. However, the negatives are
sampled from the input feature spaces in the previous work, which makes the
negatives lack diversity. Moreover, in the latent space of the embedings,the
previous methods ignore domain consistency between the generated image and the
real images of target domain. In this paper, we propose a novel contrastive
learning framework for unpaired image-to-image translation, called MCCUT. We
utilize the multi-crop views to generate the negatives via the center-crop and
the random-crop, which can improve the diversity of negatives and meanwhile
increase the quality of negatives. To constrain the embedings in the deep
feature space,, we formulate a new domain consistency loss function, which
encourages the generated images to be close to the real images in the embedding
space of same domain. Furthermore, we present a dual coordinate channel
attention network by embedding positional information into SENet, which called
DCSE module. We employ the DCSE module in the design of generator, which makes
the generator pays more attention to channels with greater weight. In many
image-to-image translation tasks, our method achieves state-of-the-art results,
and the advantages of our method have been proved through extensive comparison
experiments and ablation research
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