🧵1/I have done an analysis of those responding to #NazaninZaghariRatcliffe's interview, specifically focusing on those calling her an 'ungrateful cow' for not showing enough deference or gratitude to the UK government. The Brexit partisanship on the topic is startling. Read on! 
2/ First up. Accusations that Nazanin was 'ungrateful' started surfacing following her press conference that went viral on Twitter yesterday. She was calm and assertive, as was her husband Richard. Watch the video here
https://twitter.com/BBCNews/status/1505882936947707906#NazaninZaghariRatcliffe
3/ I analysed around 14,000 tweets involving around 8600 unique accounts. I downloaded tweets that included the hashtag "send her back" and "ungrateful cow" and the keywords "Nazanin and ungrateful". (horrendous I know). The below network graph of tweets was generated
4/ Now bear in mind such searches return tweets hating on Nazanin as well as those supporting her/ criticizing the haters. As the graph below shows, there are two distinct clusters, 1 and 2.
Cluster 1 are those defending Nazanin
Cluster 2 are those criticizing Nazanin
Cluster 1 are those defending Nazanin
Cluster 2 are those criticizing Nazanin
5/ Now what can we say about each group. Well, if we analyse the bios of group 2, the haters, we find that the third most common descriptor is Brexit. If we check the concordance, they are pro-Brexit. Woke comes in 6th! (as in, anti-woke) EU is mentioned in 12, (as in anti-EU).
6/ Brexiteer and conservative come in 11th and 12. Back Boris is also in 15th. So a very distinctly pro-Brexit and conservative community are generally the ones labelling Nazanin as ungrateful on Twitter.
What about group 1? Well let's see...
What about group 1? Well let's see...
7/ FBPE (Follow back pro EU) is in 4th place, with 'socialist' in 5th. Brexit is 6th, but concordance shows it's all pro-EU. 'Johnson Out' is in 7th place. So those defending Nazanin are indeed, more likely to to be pro-EU and left-leaning!
8/ As for the 'ungrateful cow' crowd. Here are a few of the most influential people on the hashtag accusing Nazanin of being an 'ungrateful cow'. Note the gratuitous use of caps, misogyny, and general unpleasantness. #NazaninZaghariRatcliffe
9/ So there you have it, proof that hating on a newly released hostage of the Iranian regime - who also happens to be a female person of colour, who has missed 6 years of their life and daughter growing up & endured hellish jail conditions - is more a right-wing Twitter past time
10/ Also proof that those who are pro-EU and left-leaning tend to be the ones exhibiting more empathy in the case of Nazanin. How many of those people are real is a different question, & one that I will not try to answer right now. Twitter is awash with anonymous generic accounts
11/ Some may ask about the cluster in the middle - well it's @JuliaHB1 - who actually features in the middle of the map between the two communities. While saying that Nazanin 'moaned', Julia also did say she would have punched someone if she was Nazanin.
special thanks to @rosieniven for bringing this issue to my attention!
12/ I will update keywords later but interesting to see who else is involved. Brexit campaign-funder and Putin apologist @Arron_banks has been joining the pile on casting aspersions on #NazaninZaghariRatcliffe - and getting a lot of engagement
13/ Update: Dunno if it's because of this thread but one of the largest infuencers of the anti Nazanin discourse was suspended in the past few hours #NazaninZaghariRatcliffe
14/ For those asking how I did this analysis. I downloaded the tweets using import function on NodeXL, exported that to a GEXF file, and then visualised it using Gephi. AntConc was used for corpus analysis. Of course there is a little more to it but they're the main bases.
15/ For anyone asking about clusters and communities (colours). The algorithm sorts out clusters based on connected edges (the lines, which represent tweets,RTs, replies etc). So what this graph indicates is polarisation, a lack of interaction across opposing communities on the
topic. Essentially it reflects how Twitter interactions across this particular issue reflect partisan echo Chambers. it's also important to bear in mind most twitter discussions consist of RTs. So most interactions within a sample are retweets
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