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A Network Analysis of Twitter's Crackdown on the QAnon Conversation


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Figure 1

Tweets About #QAnon: Daily Aggregated #QAnon Status Counts
Tweets About #QAnon: Daily Aggregated #QAnon Status Counts

Figure 2

Main (Weak) Components of Peak (Left) and Post (Right) Retweet Networks
Main (Weak) Components of Peak (Left) and Post (Right) Retweet Networks

Figure 3

Main (Weak) Component Collapsed by Girvan-Newman Subgroups
Main (Weak) Component Collapsed by Girvan-Newman Subgroups

Figure 4

Indegree Distribution, Peak and Post Retweets Network
Indegree Distribution, Peak and Post Retweets Network

Data Summary

Period Tweets Tweets with URL Tweets with Social Media URL Tweets with External Social Media URL
April – November 2,330,581 451,490 (19.4%) 166,392 (7.1%) 78,439 (3.4%)
Peak (July 11th – 14th) 124,670 30,966 (24.8%) 12,374 (9.9%) 5,938 (4.8%)
Post (July 21st – August 11th) 115,882 17,459 (15.1%) 7,355 (6.3%) 4,420 (3.8%)

Summary of Hypotheses and Results

Hypotheses Result Comments
H1. A handful of actors will account for most of the link-sharing of external social media sites. + New central actors emerged after the crackdown to diffuse external content.
H2. Reliable actors will be central before and after Twitter's July 2020 crackdown, even if they are different users in each period. +
H3. Both networks will exhibit relatively high levels of clustering. +
H4: Both networks will be moderately to highly centralized. +/− See H8 below.
H5: The retweet networks will be scale-free. + See H9 below.
H6: Both networks will exhibit low levels of reciprocity. +
H7: Both networks will be sparse and exhibit low levels of transitivity. + Unsurprisingly, the post network was smaller and sparser than the peak network.
H8: The network will become less centralized after Twitter's crackdown in July 2020. +
H9: The network will become less scale-free after Twitter's crackdown in July 2020. The post network was more strongly scale-free than the peak network.

Post Network Correlations Between Key Centrality Metrics and User Account Attributes

Indegree Betweenness Hubs Authorities
Retweets 0.76*** 0.63*** −0.02 0.95***
Tweets 0.21*** 0.26*** −0.00 0.25***
URLs 0.31*** 0.31*** 0.05* 0.38***
Favorites 0.73*** 0.60*** −0.02 0.99***
Friends 0.06** 0.04* −0.04 0.07**
Followers 0.15*** 0.11*** −0.05* 0.17**

Peak Period URL Statistics

Rank Platform Unique Links Tweets Users % Users
1 YouTube 1,311 4,980 3,408 24.5
2 Facebook 66 85 62 0.4
3 PeriscopeTV 62 39 31 0.2
4 Instagram 56 29 24 0.2
5 Bitchute 31 42 27 0.2
6 Pastebin 13 21 8 0.0
7 TikTok 8 8 5 0.0
8 Spotify 7 8 6 0.0
9 Telegram 6 4 4 0.0
10 Soundcloud 6 4 4 0.0

Topographical and Girvan-Newman Clustering Metrics of Peak and Post Networks

Metric Retweet Network

Peak Post
Size 3,130 1,985
Size (Largest Component) 2,566 988
Density < 0.001 < 0.001
Average Degree 1.988 1.747
Transitivity < 0.001 < .0010
Reciprocity 0.000 0.000
Degree Centralization 0.341 0.159
Betweenness Centralization 0.586 0.149
Diameter 15 14
Average Path Length 4.416 4.670

Number of Groups 217 301
Modularity 0.821 0.905
Normalized Modularity 0.824 0.908

Post Period URL Statistics

Rank Platform Unique Links Tweets Users % Users
1 YouTube 1,550 3,162 2,122 21.0
2 Instagram 362 208 139 1.4
3 PeriscopeTV 138 88 49 0.5
4 Facebook 97 117 97 1.0
5 Bitchute 39 343 324 3.2
6 TikTok 24 31 24 0.2
7 Etsy 15 30 24 0.2
8 Tumblr 13 12 8 0.1
9 Reddit 13 11 10 0.1
10 Soundcloud 12 16 14 0.1

Peak Network Correlations Between Key Centrality Metrics and User Account Attributes

Indegree Betweenness Hubs Authorities
Retweets 0.61*** 0.59*** −0.06*** 0.53***
Tweets 0.05* 0.07** −0.09*** 0.02*
URLs 0.06** 0.08** −0.04** 0.03*
Favorites 0.90*** 0.88*** −0.04* 0.90***
Friends 0.13** 0.13** −0.08*** 0.04*
Followers 0.78*** 0.76*** −0.06*** 0.76***
eISSN:
1529-1227
Język:
Angielski
Częstotliwość wydawania:
Volume Open
Dziedziny czasopisma:
Social Sciences, other