The Pandemic across Platform Societies: Weibo and Twitter at the Outbreak of the Covid-19 Epidemic in China and the West

Figures & data

Figure 1. Sentiment analysis of Covid-19 posts on Weibo and Twitter.

Figure 2. Semantic network of Covid-19 posts on Weibo and Twitter (Node size based on indegree centrality; Node color based on clusterization; ForceAtlas2 layout algorithm applied).

Table 1. Nodes with top degree centrality in the two semantic networks.

Weibo		Twitter
Node	Degree Centrality	Node	Degree Centrality
Health	1,137	person	454
Shuanghuanlian	664	Washington	451
Coronavirus	622	state	445
Novel	603	COVID-19	444
2020	553	died	436
China	524	say	424
Epidemic situation	494	coronavirus	389
Masks	403	death	365
Pneumonia	396	first	362

Figure 3. Weibo repost network and Twitter retweet network (Node size based on indegree centrality; Node color based on clusterization; ForceAtlas2 layout algorithm applied).

Table 2. Nodes with top indegree centrality in the Weibo repost network and Twitter retweet network.

Weibo		Twitter
Node	Indegree	Node	Indegree
@People’s Daily	19,565	@OH_mes2	8,005
@Liyongle Teacher	9,033	@tedlieu	4,787
@Global Time News	8,472	@Username1	4,589
@I am Jerry Guo	8,191	@Username2	2,774
@Headlines News	6,965	@Surgeon_General	2,693
@CCTV	5,477	@realDonaldTrump	2,531
@CCTV news	4,984	@charliekirk11	2,292
@Wang Bingru	4,082	@Education4Libs	2,274
@CCTV.Net	3,484	@RealJamesWoods	2,191
@Xinhua Viewpoint	2,445	@Username3	2,151