A 2 (number of diggs: a few, many) × 2 (source credibility: low, high) × 2 (recency: more recent, less recent) between-subjects experiment was conducted to explore how three news cues individually and interactively affected perception of credibility, newsworthiness, click likelihood, and sharing behavioral intentions toward the news feed on a social bookmarking website. The findings indicated that social recommendation in the form of number of diggs was the primary factor to influence perceived news credibility and click likelihood. The influence of source credibility was dependent upon the effect of other cues. There was also a cue-cumulation effect of source credibility and recency on perceived newsworthiness.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Social media have been increasingly adopted by health agencies and professionals to disseminate information, interact with public, and understand public opinion. Among them, the Centers for Disease Control and Prevention (CDC) is arguably one of the first government health agencies to adopt social media during health emergencies and crisis. It had been active on Twitter during the 2016 Zika epidemic that caused 5,168 domestic cases in the United States. Objective: This study aims to quantify the temporal variabilities in CDC’s tweeting activities over the course of Zika epidemic, public engagement in these CDC-initiated tweets (i.e., retweets and replies), and Zika case counts. It then compares the patterns of these three sets of data to identify the discrepancy and consistency among actual domestic Zika case counts, CDC response on Twitter, and public engagement in this topic. Methods: All of the CDC-initiated tweets published in 2016 with corresponding retweets and replies were collected from 67 CDC-associated Twitter accounts. Both univariate (ARIMA model) and multivariate time series analyses (CCF, mutual Shannon information entropy, ARIMAX model, Granger test) were performed in each quarter of 2016 for domestic Zika case counts, CDC tweeting activities, and public engagement in the CDC-initiated tweets. Results: CDC sent out more than 84% of its Zika tweets in the first quarter of 2016 when Zika case counts were low in the 50 U.S. states and territories. While Zika case counts increased dramatically in the second and the third quarters, CDC efforts on Twitter plunged. Time series of public engagement in the CDC-initiated tweets generally differed among quarters and from that of original CDC tweets based on ARIMA model parameters. Original CDC tweets and public engagement both had highest mutual information with Zika case counts in the 2nd quarter. Public engagement in the original CDC tweets was also substantially influenced by and preceded actual Zika epidemic. Conclusions: There were substantial discrepancies among CDC’s original tweets regarding Zika, public engagement in these tweets, and actual Zika epidemic. The patterns of these discrepancies also varied between different quarters in 2016. We discovered that although CDC was very effective in early warning of Zika in the first quarter of 2016, it failed to respond in a timely manner on Twitter during the rest of the Zika epidemic. Public engagement in CDC’s original tweets served as a more prominent predictor of actual Zika epidemic than number of CDC’s original tweets later in the year.

This study adopted a quantitative content analysis to examine how source attributes of opinion leaders and their message framing influenced user engagement in the public discourse of genetically modified organism (GMO) on Chinese social media. The findings showed that different source attributes and message frames used by opinion leaders varied in their respective influences on three dimensions of user engagement—reposts, comments, and likes. The attribute of account verification only predicted surface-level engagement (i.e., liking), whereas account type significantly influenced in-depth engagement (i.e., reposting and commenting). The fact, opportunity, pro-GMO, and international frames positively predicted user engagement.

Compared to linear independent component analysis (ICA), non-linear ICA is more suitable for the decomposition of mixed components. Existing studies of functional magnetic resonance imaging (fMRI) data by using linear ICA assume that the brain's mixed signals, which are caused by the activity of brain, are formed through the linear combination of source signals. But the application of the non-linear combination of source signals is more suitable for the mixed signals of brain. For this reason, we investigated statistical differences in resting state networks (RSNs) on 32 healthy controls (HC) and 38 mild cognitive impairment (MCI) patients using post-nonlinear ICA. Post-nonlinear ICA is one of the non-linear ICA methods. Firstly, the fMRI data of all subjects was preprocessed. The second step was to extract independent components (ICs) of fMRI data of all subjects. In the third step, we calculated the correlation coefficient between ICs and RSN templates, and selected ICs of the largest spatial correlation coefficient. The ICs represent the corresponding RSNs. After finding out the eight RSNs of MCI group and HC group, one sample t-tests were performed. Finally, in order to compare the differences of RSNs between MCI and HC groups, the two-sample t-tests were carried out. We found that the functional connectivity (FC) of RSNs in MCI patients was abnormal. Compared with HC, MCI patients showed the increased and decreased FC in default mode network (DMN), central executive network (CEN), dorsal attention network (DAN), somato-motor network (SMN), visual network(VN), MCI patients displayed the specifically decreased FC in auditory network (AN), self-referential network (SRN). The FC of core network (CN) did not reveal significant group difference. The results indicate that the abnormal FC in RSNs is selective in MCI patients.

The identification of abnormal cognitive decline at an early stage becomes an increasingly significant conundrum to physicians and is of major interest in the studies of mild cognitive impairment (MCI). Support vector machine (SVM) as a high-dimensional pattern classification technique is widely employed in neuroimaging research. However, the application of a single SVM classifier may be difficult to achieve the excellent classification performance because of the small-sample size and noise of imaging data. To address this issue, we propose a novel method of the weighted random support vector machine cluster (WRSVMC) in which multiple SVMs were built and different weights were given to corresponding SVMs with different classification performances. We evaluated our algorithm on resting state functional magnetic resonance imaging (RS-fMRI) data of 93 MCI patients and 105 healthy controls (HC) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. The maximum accuracy given by the WRSVMC is 87.67%, demonstrating excellent diagnostic power. Furthermore, the most discriminative brain areas have been found out as follows: gyrus rectus (REC.L), precentral gyrus (PreCG.R), olfactory cortex (OLF.L), and middle occipital gyrus (MOG.R). These findings of the paper provide a new perspective for the clinical diagnosis of MCI.