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Framing Otherness on Twitter: gender, elections and networks


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Figure 1:

Topic modeling based on 187,244 tweets mentioning the account of @ FranciaMarquezM. Topic 1. The interactive visualization can be found here
Topic modeling based on 187,244 tweets mentioning the account of @ FranciaMarquezM. Topic 1. The interactive visualization can be found here

Figure 3:

Topic modeling based on 29,037 tweets by women mentioning the account of @ FranciaMarquezM. Topic 3. The interactive visualization can be found here
Topic modeling based on 29,037 tweets by women mentioning the account of @ FranciaMarquezM. Topic 3. The interactive visualization can be found here

Figure 4:

Topic modeling based on 54.848 tweets by men mentioning the account of @ FranciaMarquezM. Topic 1. The interactive visualization can be found here
Topic modeling based on 54.848 tweets by men mentioning the account of @ FranciaMarquezM. Topic 1. The interactive visualization can be found here

Figure 5:

Co-occurrences visualization based on 10 more frequent female authors and 38 more frequent hashtags from a dataset of 29.000 tweets. The size of the nodes denote their frequency. The interactive visualization can be seen here https://app.vosviewer.com/?json=https://drive.google.com/uc?id=1nNYzAi0PgT_R8aHzZ6XcKve7iHmfWHQX
Co-occurrences visualization based on 10 more frequent female authors and 38 more frequent hashtags from a dataset of 29.000 tweets. The size of the nodes denote their frequency. The interactive visualization can be seen here https://app.vosviewer.com/?json=https://drive.google.com/uc?id=1nNYzAi0PgT_R8aHzZ6XcKve7iHmfWHQX

Figure 6:

Co-occurrences visualization based on 10 more frequent male authors and 38 more frequent hashtags from a dataset of 54,848 tweets. The interactive visualization can be seen here: https://app.vosviewer.com/?json=https://drive.google.com/uc?id=1X9UtGCgFc9Ek5vDZtlYlUy4Wj1fUN2T
Co-occurrences visualization based on 10 more frequent male authors and 38 more frequent hashtags from a dataset of 54,848 tweets. The interactive visualization can be seen here: https://app.vosviewer.com/?json=https://drive.google.com/uc?id=1X9UtGCgFc9Ek5vDZtlYlUy4Wj1fUN2T

Topics and terms of the whole dataset on the base of 187,244 tweets.

Terms Topics & tokens %
1 «vam,»graci»,»esper», «mujer»,»seg»,»muj»,»dignid»,»amor»,»luch», “vid” Women-dignity 10.3%
2 “apoy”, «navarrowolf», “angelamroble», «antanasmockus», «corrupt», «cuent», «aliriouribemunoz», «guerriller», “mentir”, “trabaj” Support-issues 10.2%
3 «vot»,»cambi»,»petropresident»,»vid»,»sal»,»necesit», “famili»,»gent»,»colomb»,»paz» Change 10.1%
4 “petro”, «president»,»vicepresident»,»franc»,»primer»,»quiero»,»gustav», «vic», “marquez» Petro-Francia 10.1%
5 “viv», «sabros»,»social»,»quer»,»paz», “derech”, “histor”, “pact”, “trabaj”, “comun” Better life 10.1%
6 “usted”, “ingrodolfohndez», «sab»,»habl»,»rodolf”, «mied»,»castillomarel», “debat”, “respet”, “alavaroleyv” Opponent candidate 9.9%
7 «pued”, «ecoanec»,»alexlopezmay», «ver», “adrianaluci”, “santialarconu”, “dianangel”, “polodemocr”, “odi”, “bien” Supporting figures 9.9%
8 “colomb”,»gan»,»hoy»,»puebl»,»nuev»,»dios»,»colombian»,»felicit»,»nadi»,»histori» Victory 9.9%_
9 «politica»,»campa»,»mism»,»arielanaliz», «urib», “van”, “eljulisastoque”, “corrupcn”, “verdad”, “candida” Politics 9.8%
10 alfonsopr,»margaritarosadf»,»gustavoboliv»,»pactocol»,»aabenedetti»,»pizarrom ariaj»,»pactohistor»,»mafecarrascal»,»sofiapetro»,»veronicalcocerg» Supporting figures 9.8%

Topics and terms on the base of 54.848 tweets attributed to males mentioning the account of @FranciaMarquezM.

Terms Topics & tokens %
1 «viv», “apoy»,»pac»,»sabros»,»arieanaliz», «social», «franc»,»dignid», “asesin”, “mujer” Pact-Francia 20.4%
2 “ser»,»pued»,»navarrowolf»,»angelamrobled»,»antanasmockus»,»ver»,»mal»,»aliriour ibemuoz»,»gloriasniet»,»gobiern» Political figures 20.4%
3 «col»,»cambi»,»esper»,,»vid», “pueblo”, “mejor”, “nuevo”, “paz”, “histori”,“democraci” Change 20%
4 «ingrodolfohndez»,»alfonsopr»,»gustavoboliv»,»aabenedetti»,»pactocol», “pactohistor”, “urib”, wradiocolombi”, “corrupt”, “mied” Pact-Colombian issues 19.7%
5 «colombian»,» margaritarosadf», «pizarromariaj»,»segu»,»sofiapetro», “gustav’, “tobonsanin”, “famil”, “amor”, “susanaboreal” Supporting figures 19.5%

Topics and terms on the base of 29.037 tweets attributed to females mentioning the account of @FranciaMarquezM.

Terms Topics & tokens %
1 «ingrodolfohndez», «alfonsopr», “futur”, “soypetrosoypaz”, “apoy”, “pac”, “migente”, “votarpor”, “votartempran”, “pactocol” Elections 25.5%
2 «petr»,»cambi»,»president»,»vicepresident»,»vamos», “gan”, “nuev”, “paz”, “esper”, “democrac” Change-peace 25.3%
3 «margaritarosadf»,»vid»,»mujer»,»muj», “pueb”, “dios”, «represent», «esper», «dignid”, “pizarromariaj” Women 24.7%
4 «viv,»quier»,»pued»,»sabros»,»trabaj»,»mejo»,»pod»,»navarrowolf»,»ver»,»pact», “angelamrobledo” Living with taste 24.4%

Comparison of words and mentions proportionally more used by the two genders.

words males DiffInP z words females DiffInP z at males DiffInP z at females DiffInP z
orchestrated 12 peace (in plural) −33.7 @petrogustavo 10.8 @independent news channel −25.6
pettiness 11.8 we will have −31.7 @women’s movement 10.5 @ news channel −11.7
wake up! 11.8 we do −30.7 @female artist 9.6 @women’s movement −7.4
to generate 11.2 reasons −29.8 @women from political party 9.3 @female activist −7.1
to look for 11.2 past −27.6 @male politician 8.9 @male political analyst −7.1
to lie 10.9 future −23.8 @male politician 8.9 @National Registry of Civil Status −6.8
resentment 10.5 millions −19.8 @male politician 8.6 @female activist −6.6
decency 10.2 I have −19.5 @male politician 8.4 @male activist −6.5
values 10.1 thanks −16.6 @male politician 8.1 @female activist −6.2
colombians 9.9 help us −16.5 @male politician 7.9 @male activist −6.1
dirty 9.8 vicepresident −15.4 @male politician 7.8 @male activist/influencer −6
lies 9.7 hope −14.1 @female politician 7.4 @female writer −6
hatred 9.4 mary −13.1 @female politician 7.3 @female former president
to burn 9.2 woman −13 @female politician 7.2 @male politician −5.9
it/they will burn 9 women −12.8 @female politician 7 @female influencer −5.6
burning 8.9 francia −12.5 @female artist 7 @chancellor’s office
hunger 8.9 virgin −11.8 @activist movement 6.9 @male influencer −5.3
tired of 8.8 I love −11.8 @male politician 6.8 @male politician −4.8
equality 8.7 mother −11.2 @male politician 6.7 @female journalist-opposition −4.7
white 8.7 proud (female) −11.1 @political party 6.7
roy 8.4 happiness −10.4 @news radio station 6.6
it goes 8.4 festival −10.1 @female indigenous politician 6.3
pact 8.4 duet −9.9 @news journal 6.3
dream 8.4 we (women) −9.4 @news journal 6.2
campaign 8 dance −8.8 @political party 6.2
rodolfo 7.5 reach −8.8 @political party 6.2
farc 7.3 we are −8.8 @male politician 6
they go 7.3 deared −8.7 @female activist 5.9
fact 7.3 we want −8.6 @male activist 5.9
benedetti 6.9 victory −8.1 @male independent journalist 5.8
violence 6.8 happy −8.1 @female indigenous politician 5.6
devil 6.7 let’s go −7.9 @newspaper 5.6
senator 6.7 feminist −7.8 @male politician 5.4
assassins 6.6 god −7.7 @female activist 5.4
petro 6.6 music −7.6 @news channel 5.4
criminal E we go ahead −7.5 @male politician 5.4
crisis 6.5 emotion −7.4 @army member 5.3
let’s do 6.5 I feel −7.4 @male politician 5.2
eln 6.4 confident (woman) −6.4 @political party 5
power 6.4 beauty −6.3 @female politician 5
rat 6.3 history −6.3 @political party 4.9
followers 6.3 to live −6.3
tendencies 6.2 wisdom −6.3
bandits 6.1 we love −6.3
uribism 6.1 to help −6.2
proud (male) 6 we deserve −6.1
estate 5.9 márquez −6
gustavo 5.9 we will go −6
to grow 5.8 excited (woman) −5.9
communist 5.7 I cry −5.8
they are 5.7 I admire −5.8
old 5.7 represented (woman) −5.7
equal 5.6 to give us back −5.7
historic 5.6 to dream −5.7
politics 5.5 hearing −5.7
prada 5.5 faith −5.7
boss 5.5 we believe −5.7
cuba 5.3 habit −5.6
case 5.3 tasty −5.6

E-I index of the mentions between the group of females and between the group of males.

FEMALE SET % of total MALE SET % of total
(Total number of tweets in total dataset: 187244)
Total no. of tweets in classified dataset 29037 15,5 54848 29,3
Total no. unique classified usernames Tweeters 11827 40,7 22842 41,6
(Total overall mentions (incl. non classified)) 105445 215902
Total females from list mentioned 6598 6,3 16236 7,5
Total unique females from list mentioned 1090 16,5 1399 8,6
Total males from list mentioned 9530 9,0 26711 12,4
Total unique males from list mentioned 1630 17,1 2943 11,0
Total male and female mentions 16128 15,3 42947 19,9
E-I index 0,18 −0,24
Expected E-I index (from overall twitter stats) 0,17 −0,17
eISSN:
0226-1766
Language:
English
Publication timeframe:
Volume Open
Journal Subjects:
Social Sciences, other