Social Media Moderations, User Ban, and Content Generation: Evidence from Zhihu

Social media platforms have evolved as major outlets for many entities to distribute and consume information. The content on social media sites, however, are often considered inaccurate, misleading, or even harmful. To deal with such challenges, the platforms have developed rules and guidelines to moderate and regulate the content on their sites. In this study, we explore user banning as a moderation strategy that restricts, suspends, or bans a user who the platform deems as violating community rules from further participation on the platform for a predetermined period of time. We examine the impact of such moderation strategy using data from a major Q&A platform. Our analyses indicate that user banning increases a user’s contribution after the platform lifts the ban. The magnitude of the impact, however, depends on the user’s engagement level with the platform. We find that the increase in contributions is smaller for a more engaged user. Additionally, we find that the quality of the user-generated content (UGC) decreases after the user ban is lifted. Our research is among the first to empirically evaluate the effectiveness of platform moderations. The findings have important implications for platform owners in managing the content on their sites

The Effects of Quote Retweet on Subsequent Posting Behavior and Morality Expression on Social Media

Guided by the Threshold model and Self-justification theory, we propose and test a research model regarding the impact of online discussion activity on users’ behaviors on social media. Specifically, we examine the effects of quote retweeting a tweet related to immigration policies and border issues on users’ subsequent posting behaviors and morality expression. In addition, we test the moderating effect of individual threshold level and behavior-opinion inconsistency on the main effect. Results indicate that individuals, who quote retweeted the selected-topic tweets, are likely to post more topic-related tweets and express more on morality. This impact can be strengthened when individuals have higher threshold levels or larger behavior-opinion inconsistency. These findings provide both theoretical and practical implications for social media governance

Determinants of capital structure: Some UK evidences

Capital structure theories could be one of the most contentious issues in the theory of financial world. This study develops preliminary study to explain the cross-sectional variation in the capital structure of non-financial UK companies. We find that firm size, uniqueness are significant correlated to firms leverage level. The non-debt tax shield only significantly correlated to short term leverage. We fail to give significant support to the impact of firms profitability, volatility and growth opportunities on firm's borrowing behaviour. However, a strong industrial classification effect is founded in the UK firms and that result is consistent with Bennett & Donnelly (1993) and Bradley et al (1984). Furthermore, we find transaction cost have significant impact on firm's leverage policies. The correlations between empirical proxies and short term debt ratios are quite different from long term debt ratios

Identifying progressive imaging genetic patterns via multi-task sparse canonical correlation analysis: a longitudinal study of the ADNI cohort

Motivation Identifying the genetic basis of the brain structure, function and disorder by using the imaging quantitative traits (QTs) as endophenotypes is an important task in brain science. Brain QTs often change over time while the disorder progresses and thus understanding how the genetic factors play roles on the progressive brain QT changes is of great importance and meaning. Most existing imaging genetics methods only analyze the baseline neuroimaging data, and thus those longitudinal imaging data across multiple time points containing important disease progression information are omitted. Results We propose a novel temporal imaging genetic model which performs the multi-task sparse canonical correlation analysis (T-MTSCCA). Our model uses longitudinal neuroimaging data to uncover that how single nucleotide polymorphisms (SNPs) play roles on affecting brain QTs over the time. Incorporating the relationship of the longitudinal imaging data and that within SNPs, T-MTSCCA could identify a trajectory of progressive imaging genetic patterns over the time. We propose an efficient algorithm to solve the problem and show its convergence. We evaluate T-MTSCCA on 408 subjects from the Alzheimer’s Disease Neuroimaging Initiative database with longitudinal magnetic resonance imaging data and genetic data available. The experimental results show that T-MTSCCA performs either better than or equally to the state-of-the-art methods. In particular, T-MTSCCA could identify higher canonical correlation coefficients and capture clearer canonical weight patterns. This suggests that T-MTSCCA identifies time-consistent and time-dependent SNPs and imaging QTs, which further help understand the genetic basis of the brain QT changes over the time during the disease progression. Availability and implementation The software and simulation data are publicly available at https://github.com/dulei323/TMTSCCA. Supplementary information Supplementary data are available at Bioinformatics online

Image-based quantitative analysis of gold immunochromatographic strip via cellular neural network approach

"(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works."Gold immunochromatographic strip assay provides a rapid, simple, single-copy and on-site way to detect the presence or absence of the target analyte. This paper aims to develop a method for accurately segmenting the test line and control line of the gold immunochromatographic strip (GICS) image for quantitatively determining the trace concentrations in the specimen, which can lead to more functional information than the traditional qualitative or semi-quantitative strip assay. The canny operator as well as the mathematical morphology method is used to detect and extract the GICS reading-window. Then, the test line and control line of the GICS reading-window are segmented by the cellular neural network (CNN) algorithm, where the template parameters of the CNN are designed by the switching particle swarm optimization (SPSO) algorithm for improving the performance of the CNN. It is shown that the SPSO-based CNN offers a robust method for accurately segmenting the test and control lines, and therefore serves as a novel image methodology for the interpretation of GICS. Furthermore, quantitative comparison is carried out among four algorithms in terms of the peak signal-to-noise ratio. It is concluded that the proposed CNN algorithm gives higher accuracy and the CNN is capable of parallelism and analog very-large-scale integration implementation within a remarkably efficient time

Fast Multi-Task SCCA Learning with Feature Selection for Multi-Modal Brain Imaging Genetics

Brain imaging genetics studies the genetic basis of brain structures and functions via integrating both genotypic data such as single nucleotide polymorphism (SNP) and imaging quantitative traits (QTs). In this area, both multi-task learning (MTL) and sparse canonical correlation analysis (SCCA) methods are widely used since they are superior to those independent and pairwise univariate analyses. MTL methods generally incorporate a few of QTs and are not designed for feature selection from a large number of QTs; while existing SCCA methods typically employ only one modality of QTs to study its association with SNPs. Both MTL and SCCA encounter computational challenges as the number of SNPs increases. In this paper, combining the merits of MTL and SCCA, we propose a novel multi-task SCCA (MTSCCA) learning framework to identify bi-multivariate associations between SNPs and multi-modal imaging QTs. MTSCCA could make use of the complementary information carried by different imaging modalities. Using the G2,1-norm regularization, MTSCCA treats all SNPs in the same group together to enforce sparsity at the group level. The l2,1-norm penalty is used to jointly select features across multiple tasks for SNPs, and across multiple modalities for QTs. A fast optimization algorithm is proposed using the grouping information of SNPs. Compared with conventional SCCA methods, MTSCCA obtains improved performance regarding both correlation coefficients and canonical weights patterns. In addition, our method runs very fast and is easy-to-implement, and thus could provide a powerful tool for genome-wide brain-wide imaging genetic studies

On Separate Normalization in Self-supervised Transformers

Self-supervised training methods for transformers have demonstrated remarkable performance across various domains. Previous transformer-based models, such as masked autoencoders (MAE), typically utilize a single normalization layer for both the [CLS] symbol and the tokens. We propose in this paper a simple modification that employs separate normalization layers for the tokens and the [CLS] symbol to better capture their distinct characteristics and enhance downstream task performance. Our method aims to alleviate the potential negative effects of using the same normalization statistics for both token types, which may not be optimally aligned with their individual roles. We empirically show that by utilizing a separate normalization layer, the [CLS] embeddings can better encode the global contextual information and are distributed more uniformly in its anisotropic space. When replacing the conventional normalization layer with the two separate layers, we observe an average 2.7% performance improvement over the image, natural language, and graph domains.Comment: NIPS 202