Parabola shaped normalized rank diversity in a model closed system of size 500.

The top L = 48 can be considered as an open system.</p

Categorized proportion of hashtags that have stayed at certain ranks on HSL for longer than 2 hours.

(A)(B)(C)(D) show the content distribution of hashtags at ranks 8, 16, 28, and 33 respectively, corresponding to the sudden drops in Fig 7A. (E) Averaged proportion of hashtags by content category at ranks 5, 12, 21, 25, 30, 37.</p

Circadian patterns of the Sina Weibo Hot Search List (HSL).

(A) Increment of number of new hashtags per Δt = 5 minutes on the HSL during the observation period from 22 May 2020 to 29 September 2020. (B) Time series of the median of search volume index of all hashtags on the HSL at a timestamp, advertisement rank positions excluded. represents the median value hotness H of hashtags on Sina Weibo HSL at a timestamp. In both (A) and (B) the one-week gap due to the suspension of HSL by the cyberspace authority of China is visible. An enlarged part of (A) is in S1 Appendix.</p

Rank dynamics comparison between empirical data and a ranking model with anchoring.

(A) Empirical rank diversity separated for day (upper line) and night (lower line). The sudden drops are at ranks 8, 16, 28, and 33. (B) Simulated rank diversity with the anchor effect.</p

Relationship between hashtags’ duration on the HSL and the time <i>t</i>_<i>i</i>.

(A) Scatter plot of hashtags’ duration on the HSL and the time of the day they first appear on the HSL. Each point is a hashtag, colored by the category it is clustered in Fig 2. (B) Distribution of hashtags’ duration on the HSL according to different time intervals during the day of first appearance on HSL.</p

Prehistory length <i>t</i>_<i>HSL</i>, enter-ranks <i>r</i>_<i>i</i>(<i>t</i>_<i>i</i>), the highest rank , and duration <i>d</i>_<i>i</i> of hashtags on the Sina Weibo HSL.

(A) The relationship between the hashtags’ prehistory time length and the ranks they first enter on the HSL. (B) The relationship between the hashtags’ prehistory time length and the highest rank during stay on the HSL. (C) The relationship between the hashtags’ prehistory time length and the duration they stay on the HSL. (D) Parameterized probability density function of the hashtag duration on the HSL by prehistory time length, using kernel density estimation (KDE) [41], with the parameter bw = “scott” [42].</p

Ranking dynamics characterization of hashtags on the Sina Weibo HSL from 17 July 2020 to 17 Sep 2020.

(A) Distribution of ri(ti) and ri(Ti). (B) Scatter plot of and di. (C) Scatter plot of ri(ti) and di, hashtags with high enter-rank and short duration are circled red. (D) Scatter plot of ri(Ti) and di, rank 33 marked by red arrow.</p

Clustering patterns of hashtag rank trajectories on the Sina Weibo HSL.

(A) Distribution of hashtag duration on the HSL, divided into two sections based on local minima at 1 hour. Results of k-means clustering with 3 clusters in each section for time series data are shown, metric is Dynamic Time Warping (DTW) distance, y-axis is normalized to the mean and the standard deviation and the x-axis by di. (B), (C), (D) correspond to duration interval from 0 to 1 hour (Section 1). (E), (F), (G) correspond to duration interval larger than 1 hour (Section 2). Red curves depict clustering centers (centroid) [39], computed as the barycenters [40] with respect to DTW. (We performed the clustering also with 4 clusters for both categories, see S1 Appendix).</p

Supplementary material to the manuscript.

Microblogging sites are important vehicles for the users to obtain information and shape public opinion thus they are arenas of continuous competition for popularity. Most popular topics are usually indicated on ranking lists. In this study, we investigate the public attention dynamics through the Hot Search List (HSL) of the Chinese microblog Sina Weibo, where trending hashtags are ranked based on a multi-dimensional search volume index. We characterize the rank dynamics by the time spent by hashtags on the list, the time of the day they appear there, the rank diversity, and by the ranking trajectories. We show how the circadian rhythm affects the popularity of hashtags, and observe categories of their rank trajectories by a machine learning clustering algorithm. By analyzing patterns of ranking dynamics using various measures, we identify anomalies that are likely to result from the platform provider’s intervention into the ranking, including the anchoring of hashtags to certain ranks on the HSL. We propose a simple model of ranking that explains the mechanism of this anchoring effect. We found an over-representation of hashtags related to international politics at 3 out of 4 anchoring ranks on the HSL, indicating possible manipulations of public opinion.</div

Pt-embedded Janus WSTe monolayer for adsorption and detection of XO₂ (X = C, N and S) gases: a first-principles study

For sensing XO2 (X = C, N and S) gas species, this work purposes Pt-embedded WSTe monolayer, and using first-principles theory uncovers related gas adsorption properties and sensing mechanism. Results indicate that Pt-embedding is more energy favourable by replacing Te atom of the Janus WSTe monolayer with the formation energy of −1.78 eV, narrowing the bandgap to 0.926 eV. Besides, the Pt-WSTe monolayer performs weak physisorption upon CO2 with adsorption energy (Ead) of −0.17 eV, while strong chemisorption upon NO2 and SO2 with Ead of −1.43 and −1.17 eV, respectively. The analysis of electronic property uncovers the sensing potential of Pt-WSTe monolayer as a resistance-type NO2 or SO2 gas sensor with higher sensing response upon SO2, and the analysis of work function (WF) uncovers the sensing potential of Pt-WSTe monolayer as a WF-type NO2 or SO2 gas sensor with higher sensing response upon NO2. We are hopeful that the findings in this work can help to explore the possible application of Pt-WSTe monolayer in the gas sensing field and also to make some other explorations on Janus WSTe-based material for gas detections.</p