Political polarization of news media and influencers on Twitter in the 2016 and 2020 US presidential elections.

Social media has been transforming political communication dynamics for over a decade. Here using nearly a billion tweets, we analyse the change in Twitter's news media landscape between the 2016 and 2020 US presidential elections. Using political bias and fact-checking tools, we measure the volume of politically biased content and the number of users propagating such information. We then identify influencers-users with the greatest ability to spread news in the Twitter network. We observe that the fraction of fake and extremely biased content declined between 2016 and 2020. However, results show increasing echo chamber behaviours and latent ideological polarization across the two elections at the user and influencer levels.

Metabolic profiling and pharmacokinetic studies of Baihu-Guizhi decoction in rats by UFLC-Q-TOF-MS/MS and UHPLC-Q-TRAP-MS/MS.

BACKGROUND: Baihu-Guizhi decoction (BHGZD) is a well-documented traditional Chinese Medicine (TCM) prescription that has been extensively applied to treating rheumatoid arthritis. Despite of its beneficial outcomes, the chemical constituents of BHGZD have not been fully portrayed and the in vivo absorption, distribution, metabolism, and excretion (ADME) patterns of absorbed components have never been described. METHODS: Characterization of absorbed components and in vivo biotransformation profiling of these feature compounds were based on the ultra-fast liquid chromatography-quadrupole-time-of-flight tandem mass spectrometry (UFLC-Q-TOF-MS/MS). Furthermore, the ultra-high-performance liquid chromatography tandem ion trap quadrupole mass spectrometry (UHPLC-Q-TRAP-MS/MS) system were performed to investigate the pharmacokinetics of active ingredients from BHGZD. RESULTS: In this study, we have identified and tentatively characterized 18 feature absorbed prototype and 15 metabolites of BHGZD in rat serum and the in vivo transformation pathways of these absorbed constituents were proposed. Besides, we have established novel quantitative methodology of five crucial components of BHGZD and have monitored the pharmacokinetic behaviors of these constituents spontaneously in rat serum after BHGZD gavage. After rats received two ways of BHGZD gavage, the pharmacokinetic behaviors of each compound exhibited relatively similar behaviors, as evidenced by similar curve track as well as relatively close time to reach maximum concentration (Tmax) and half washout time (T1/2). Whereas the maximum plasma concentration (Cmax) and area under the plasma concentration-time curve (AUC) values of five analytes with multiple dosage were a bit higher than single dosage. CONCLUSION: This study added knowledge into the material basis and bio-transformation patterns of BHGZD in vivo, which would be of great value for exploring pharmacological effects and mechanism of BHGZD.

Predicting Intrinsic and Extrinsic Goal Contents Pursuit on Social Media.

Goal contents pursuit reflects the motivational personality and can be an excellent indicator to predict individuals' life satisfaction and daily behaviors. However, due to the expense and subjective bias of questionnaires, it is challenging to obtain individual data and explore the effects of goal contents pursuit in conventional studies. Social media provides individuals with a communication context that can be used as a proxy to infer personality based on a massive of media footprints information. This study obtained 456 Weibo active users' self-reports of goal contents pursuit scale and their online behaviors that is established to train a competent machine learning model, which then successfully identifies the classification of intrinsic and extrinsic goals. From the perspective of Weibo users' features (i.e., basic, interactive, linguistic, and emotional features), the systematic comparison shows the significant differences in the pursuit level of intrinsic and extrinsic goals. This study advances the methodology of employing machine learning and online data to objectively delineate individual goal contents pursuit and paves the way to explore a massive number of individuals' personalities and behaviors.

RAVA: Region-Based Average Video Quality Assessment.

Video has become the most popular medium of communication over the past decade, with nearly 90 percent of the bandwidth on the Internet being used for video transmission. Thus, evaluating the quality of an acquired or compressed video has become increasingly important. The goal of video quality assessment (VQA) is to measure the quality of a video clip as perceived by a human observer. Since manually rating every video clip to evaluate quality is infeasible, researchers have attempted to develop various quantitative metrics that estimate the perceptual quality of video. In this paper, we propose a new region-based average video quality assessment (RAVA) technique extending image quality assessment (IQA) metrics. In our experiments, we extend two full-reference (FR) image quality metrics to measure the feasibility of the proposed RAVA technique. Results on three different datasets show that our RAVA method is practical in predicting objective video scores.