bla

full_data.csv

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:25a1cee9f8a673e0f509e13c7e2f069ee55a7f8874261f40076806e34287a76b
-size 671840957
+oid sha256:c989a986e4ca0d8fd3bfcbfa49b58a04a28e2b16a88271c5f8ef28fe10c461eb
+size 1050676452

scripts/figures.qmd

@@ -13,19 +13,21 @@ library(dplyr)
 library(tidyr)
 sysfonts::font_add_google("EB Garamond")
 theme_set(theme_minimal(base_family = "EB Garamond"))
-theme_update(text = element_text(size = 14))
+theme_update(text = element_text(size = 50))
 library(showtext)
 showtext_auto()
 
 
 data = read.csv("../full_data.csv")
-rubriques_lemonde = c("international","culture","politique","société","économie","sport","science/technologie","inclassable")
+#rubriques_lemonde = c("international","culture","politique","société","économie","sport","science/technologie","inclassable")
 
-z = data$rubrique %in% rubriques_lemonde
-data$rubrique[!z] = "inclassable"
+#z = data$rubrique %in% rubriques_lemonde
+#data$rubrique[!z] = "inclassable"
 
 data$sexe_prenom = data$sexe_prenom %>% recode(Femme = "Women",Homme = "Men")
-data$rubrique = data$rubrique %>% recode(économie = "Economics",politique = "Politics",société = 'Society',"science/technologie" = "Science/tech",culture="Culture",international = "International")
+data$rubrique[data$rubrique=="Science/tech"] = "Culture"
+data$rubrique = data$rubrique %>% recode(économie = "Economics",politique = "Politics",société = 'Society',"science/technologie" = "Science/tech",culture="Culture",international = "International",sport="Sport",inclassable = "Unclassifiable")
+
 
 
 ```
@@ -33,47 +35,60 @@ data$rubrique = data$rubrique %>% recode(économie = "Economics",politique = "Po
 ```{r}
 #| fig-cap: Masculinity rate of mentions and quotes in the whole corpus
 
-d = data %>% group_by(year) %>% summarise(
+d = data%>% group_by(year) %>% summarise(
   citations_men = sum(citations_men,na.rm=T),
   citations_women = sum(citations_women,na.rm=T),
   mentions_men = sum(mentions_men,na.rm=T),
-  mentions_women = sum(mentions_women,na.rm=T)
+  mentions_women = sum(mentions_women,na.rm=T),
+  nwords = sum(nwords)
+  #entities = sum(entities)
 ) 
 d$mentions_total = d$mentions_men + d$mentions_women
-ggplot(d %>% filter(mentions_women > 20),aes(year,mentions_men/(mentions_men+mentions_women),linetype="Mentions")) + geom_line() + 
-         geom_line(aes(year,citations_men/(citations_men+citations_women),linetype="Quotes")) +
-         ylab("Feminity rate") + xlab("Date") + labs(linetype="Measure") + ylim(NA, 1)
+fig = ggplot(d %>% filter(mentions_women > 20),aes(year,mentions_men/(mentions_men+mentions_women),linetype="Mentions")) + geom_line() + 
+       geom_line(aes(year,citations_men/(citations_men+citations_women),linetype="Quotes")) +   #scale_linetype_manual(values = c("Mentions" = "solid", "Quotes" = "dotted")) +
+         ylab("Masculinity rate") + xlab("Date") + labs(linetype="Measure") + ylim(.5, 1)
 
+ggsave("../figures/fig1.png",fig,dpi=320,bg="white",width=7, height=5)
 
 ```
 
 ```{r}
-#| fig-cap: Masculinity rate of mentions per news section, excluding sports and unclassifiable
+#| fig-cap: Masculinity rate of mentions per news section, excluding unclassifiable
 
 
-d = data %>% filter(!rubrique %in% c("inclassable","sport")) %>%
+d = data %>% 
+  filter(!rubrique %in% c("Unclassifiable")) %>%
   group_by(year,rubrique) %>% summarise(
   citations_men = sum(citations_men,na.rm=T),
   citations_women = sum(citations_women,na.rm=T),
   mentions_men = sum(mentions_men,na.rm=T),
-  mentions_women = sum(mentions_women,na.rm=T)
+  mentions_women = sum(mentions_women,na.rm=T),
+  signatures_men = sum(sexe_prenom=="Men"),
+  signatures_women= sum(sexe_prenom=="Women")
 ) 
+
 d$mentions_total = d$mentions_men + d$mentions_women
-ggplot(d %>% filter(mentions_women > 20),aes(year,mentions_men/(mentions_men+mentions_women),linetype="Mentions")) + geom_line() + 
+
+
+ggplot(d %>% filter(mentions_women > 20,year > 1944),aes(year,mentions_men/(mentions_men+mentions_women),linetype="Mentions")) + geom_line() + 
          geom_line(aes(year,citations_men/(citations_men+citations_women),linetype="Quotes")) +
-         ylab("Masculinity rate") + xlab("Date") + labs(linetype="Measure") + ylim(c(NA,1)) + facet_wrap(.~rubrique) + theme(
-  strip.text = element_text(size = 12, family = "EB Garamond"),
-  axis.text.x = element_text(size = 10)
+         #geom_line(data = filter(d,!year %in% missing_years),aes(year,signatures_men/(signatures_men+signatures_women),linetype="Signatures")) +
+ # scale_linetype_manual(values = c("Mentions" = "solid", "Quotes" = "dotted")) +
+         ylab("Masculinity rate") + xlab("Date") + labs(linetype="Measure") + ylim(c(.5,1)) + facet_wrap(.~rubrique,nrow = 2,ncol=3) + theme(
+  strip.text = element_text(size = 40, family = "EB Garamond"),
+  axis.text.x = element_text(size = 30)
 )
 
-
+ggsave("../figures/masc_mentions_sections.png",dpi=320,bg="white",width=7, height=5)
 ```
 
 ```{r}
 #| fig-cap: Masculinity rates of mentions and quotes, depending on the journalist gender. We ignore the points where there are less than 100 mentions or citations, and the 1987-1994 and 2003-2004, where the author metadata is mostly missing due to errors of digitization.
 #| fig-width: 9.5
-missing_years = c(1987:1994,2003:2004)
 
+library(cowplot)
+missing_years = c(1987:1994,2003:2004)
+  
 d = filter(data,sexe_prenom %in% c("Women","Men"))
 d = d %>% group_by(year,sexe_prenom) %>% summarise(
   mentions_men = sum(mentions_men,na.rm=T),
@@ -82,17 +97,53 @@ d = d %>% group_by(year,sexe_prenom) %>% summarise(
   citations_women = sum(citations_women,na.rm=T)
 ) 
 d$mentions_total = d$mentions_men + d$mentions_women
-a = ggplot(d %>% filter(mentions_total > 100,!year %in% missing_years),aes(year,mentions_men/mentions_total,shape=sexe_prenom)) + geom_point() + ylab("Masculinity rate of mentions") + labs(shape="Journalist gender") + xlab("Date") + scale_shape_manual(values = c("Women" = 2, "Men" = 16))+ theme(legend.position = "right")
+d$citations_total = d$citations_men + d$citations_women
 
+# Create the plots
+a <- ggplot(d %>% filter(mentions_total > 100, !year %in% missing_years), 
+            aes(year, mentions_men/mentions_total, shape = sexe_prenom)) +
+  geom_point() +
+  ylab("Mentions masculinity rate") +
+  labs(shape = "Journalist gender") +
+  xlab("Date") +
+  scale_shape_manual(values = c("Women" = 2, "Men" = 16))
+
+b <- ggplot(d %>% filter(citations_total > 100, !year %in% missing_years),
+            aes(year, citations_men/citations_total, shape = sexe_prenom)) +
+  geom_point() +
+  ylab("Quotes masculinity rate") +
+  labs(shape = "Journalist gender") +
+  xlab("Date") +
+  scale_shape_manual(values = c("Women" = 2, "Men" = 16))
+# Create the legend separately
+legend <- get_legend(a)
 
-d$citations_total = d$citations_men + d$citations_women
-b = ggplot(d %>% filter(citations_total > 100,!year %in% missing_years),aes(year,citations_men/citations_total,shape=sexe_prenom)) + geom_point() + ylab("Masculinity rate of quotes") + labs(shape="Journalist gender") + xlab("Date") + scale_shape_manual(values = c("Women" = 2, "Men" = 16)) + theme(legend.position = "right")
+# Combine the plots and legend
+plots <- plot_grid(a + theme(legend.position = "none"),
+                   b + theme(legend.position = "none"),
+                   nrow=2,rel_heights = c(.5,.5))
 
+fig <- plot_grid(plots, legend, ncol = 2, rel_widths = c(1, 0.15))
 
-library(cowplot)
-legend <- get_legend(a)
-plots = plot_grid(a + theme(legend.position = "none"),b + theme(legend.position = "none"))
-plot_grid(plots,legend,ncol = 2,rel_widths = c(1, 0.15))
+
+
+d$mmr = d$mentions_men/d$mentions_total
+z = d$mentions_total < 100
+d$mmr[z] = NA
+d$qmr = d$citations_men/d$citations_total
+z = d$citations_total < 100
+d$qmr[z] = NA
+dd = gather(d %>% select("year","sexe_prenom","qmr","mmr"),"key","value",-year,-sexe_prenom)
+
+dd$key  = dd$key %>% recode(mmr = "Mentions",qmr = "Quotes")
+ggplot(dd %>% filter(!year %in% missing_years),
+            aes(year, value, shape = sexe_prenom)) +
+  geom_point() +
+  ylab("Masculinity rate") +
+  labs(shape = "Journalist gender") +
+  xlab("Date") +
+  scale_shape_manual(values = c("Women" = 2, "Men" = 16)) + facet_wrap(.~key,nrow = 2)
+ggsave("../figures/measures_journalist_gender.png", dpi=320, bg="white", width=7, height=5)
 
 ```
 
@@ -101,7 +152,7 @@ plot_grid(plots,legend,ncol = 2,rel_widths = c(1, 0.15))
 
 library(dplyr)
 
-d = filter(data,sexe_prenom %in% c("Women","Men"),!rubrique %in% c("inclassable","sport"))
+d = filter(data,sexe_prenom %in% c("Women","Men"),!rubrique %in% c("Unclassifiable"))
 d = d %>% group_by(year,sexe_prenom,rubrique) %>% summarise(
   mentions_men = sum(mentions_men,na.rm=T),
   mentions_women = sum(mentions_women,na.rm=T),
@@ -111,17 +162,22 @@ d = d %>% group_by(year,sexe_prenom,rubrique) %>% summarise(
 d$mentions_total = d$mentions_men + d$mentions_women
 d$citations_total = d$citations_men + d$citations_women
 
-ggplot(d %>% filter(citations_women > 10),aes(year,citations_women/citations_total,shape=sexe_prenom)) + geom_point() + ylab("Masculinity rate of quotes") + facet_wrap(.~rubrique,scales="free_y") + labs(shape="Journalist gender") + theme(
+ggplot(d %>% filter(citations_women > 10),aes(year,citations_women/citations_total,shape=sexe_prenom)) + geom_point() + geom_smooth(se=F) + ylab("Masculinity rate of quotes") + facet_wrap(.~rubrique,scales="free_y") + labs(shape="Journalist gender") + theme(
   strip.text = element_text(size = 10, family = "EB Garamond"),
   axis.text.x = element_text(size = 7)) + xlab("Date") + scale_shape_manual(values = c("Women" = 2, "Men" = 16))
+
+ggsave("../figures/measures_journalist_gender_sections.png", dpi=320, bg="white", width=7, height=5)
+
 ```
 
 ```{r}
-#| fig-cap: Masculinity rates of quotes, depending on the journalist gender, by news section (omitting sports and unclassifiable articles).
+#| fig-cap: Masculinity rates of mentions, depending on the journalist gender, by news section (omitting unclassifiable articles).
 ggplot(d %>% filter(mentions_women > 100),aes(year,mentions_women/mentions_total,shape=sexe_prenom)) + geom_point() + ylab("Masculinity rate of mentions") + facet_wrap(.~rubrique,scales="free_y") + labs(shape="Journalist gender") + theme(
   strip.text = element_text(size = 10, family = "EB Garamond"),
   axis.text.x = element_text(size = 7)
 ) + scale_shape_manual(values = c("Women" = 2, "Men" = 16)) + xlab("Date")
+ggsave("../figures/mentions_journalist_gender_sections.png", dpi=320, bg="white", width=7, height=5)
+
 ```
 
 ```{r}
@@ -138,8 +194,6 @@ d$citations_total = d$citations_men + d$citations_women
 d$feminity_mentions = d$mentions_women/d$mentions_total
 d$feminity_citations = d$citations_women/d$citations_total
 
-#ggplot(d %>% filter(mentions_total > 200),aes(year,mentions_women/mentions_total,color=sexe_prenom)) + geom_point() + ylab("Masculinité")
-
 d_bis = d %>% filter(citations_total > 100) %>% select(year,sexe_prenom,feminity_mentions,feminity_citations) %>% pivot_wider(names_from = sexe_prenom, values_from = c(feminity_mentions,feminity_citations), id_cols = c( year))
 
 ggplot(d_bis,aes(year,feminity_citations_Women-feminity_citations_Men,color="Citations")) + geom_point() + geom_smooth(se=F)  + geom_point(aes(year,feminity_mentions_Women-feminity_mentions_Men,color="Mentions")) + geom_smooth(aes(year,feminity_mentions_Women-feminity_mentions_Men,color="Mentions"),se=F) + ylab("Gap between women and men journalists") + labs(color="Measure") + xlab("Date")
@@ -158,4 +212,307 @@ d_bis = d %>% pivot_wider(names_from = sexe_prenom, values_from = n)
 d_bis$feminity = d_bis$Men/(d_bis$Women+d_bis$Men)
 
 ggplot(d_bis,aes(year,feminity)) + geom_line(na.rm=T) + ylab("Masculinity of articles signatures") + labs(color="Journalist gender") +xlab("Date")
+ggsave("../figures/signatures.png",dpi=320,bg="white",width=7, height=5)
+
+```
+
+```{r}
+d = filter(data,sexe_prenom %in% c("Women","Men"), rubrique != "Unclassifiable")
+
+
+d = d %>% group_by(year,sexe_prenom,rubrique) %>% summarise(
+  n = n()
+)
+d[d$year %in% missing_years,"n"] = NA
+
+d_bis = d %>% pivot_wider(names_from = sexe_prenom, values_from = n)
+d_bis = d_bis %>% mutate(Women = replace_na(Women, 0))
+
+d_bis$feminity = d_bis$Men/(d_bis$Women+d_bis$Men)
+
+
+ggplot(d_bis,aes(year,feminity)) + geom_line(na.rm=T) + ylab("Masculinity of articles signatures") + labs(color="Journalist gender") +xlab("Date") + facet_wrap(.~rubrique)
+ggsave("../figures/signatures_rubriques.png",dpi=320,bg="white",width=7, height=5)
+
+```
+
+```{r}
+z = data %>% group_by(year) %>% summarise(nwords = sum(nwords))
+ggplot(z,aes(year,nwords)) + geom_area(stat = 'identity') + xlab("Date") + ylab("Words per year")
+ggsave("../figures/nwords.png",dpi=320,bg="white",width=7, height=5)
+
+```
+
+```{r}
+z = data %>% filter(!rubrique %in% c("Unclassifiable","Science/tech")) %>% group_by(year,rubrique) %>% summarise(nwords = sum(nwords))
+z = z %>%  group_by(year) %>% mutate(total_nwords_per_year = sum(nwords),   
+         normalized_nwords = nwords / total_nwords_per_year) %>%
+  ungroup()
+ggplot(z,aes(year,normalized_nwords)) + geom_area(stat = 'identity') + xlab("Date") + ylab("Words per year in the corpus") + facet_wrap(.~rubrique, ncol = 3, nrow = 2)
+
+ggsave("../figures/nwords_rubrique.png",dpi=320,bg="white",width=7, height=5)
+
+```
+
+```{r}
+library(glue)
+library(tidyr)
+mean_male = vector()
+mean_female = vector()
+for(i in 1945:2022){
+  a = read.csv(glue("~/Downloads/verbs_nonagg/quotes_verbs_{i}.csv"),sep = "\t")
+  mean_male = c(mean_male,mean(a[a$speaker_gender=="Male",]$n_tok))
+  mean_female = c(mean_female,mean(a[a$speaker_gender=="Female",]$n_tok))
+  }
+
+result = data.frame(year = 1945:2022,Male = mean_male,Female = mean_female)
+result_gather = gather(result,"Gender","value",-year)
+
+ggplot(result_gather,aes(year)) + 
+  geom_ribbon(data = result, 
+              aes(ymin = pmin(Male, Female), 
+                  ymax = pmax(Male, Female),
+                  fill = Male > Female,
+              group = cumsum(c(0, diff(Male > Female) != 0))),
+alpha = .3,show.legend = FALSE) + geom_line(aes(y=value,linetype=Gender)) +
+  xlab("Date") + ylab("Mean quote length (words)")+ 
+  scale_fill_manual(values = c("TRUE" = "lightblue", "FALSE" = "pink"),
+                   labels = c("TRUE" = "Male > Female", "FALSE" = "Female > Male"),
+                   name = "Difference")
+
+ggsave("../figures/length_quotes.png",dpi=320,bg="white",width=7, height=5)
+ggplot(result, aes(year,Male/Female)) + geom_point() + geom_smooth(color="black",se=F)
+
+```
+
+```{r}
+#Stereotypes data preparation
+files = read.csv("~/Downloads/citations_by_article.csv",
+        col.names = c("filename","n_men","n_women","verbs_men",
+        "verbs_men_lemmatized","verbs_women","verbs_women_lemmatized"))
+z = duplicated(files$filename)
+files = files[!z,]
+library(stringr)
+files$filename = str_c(files$filename,".txt")
+z = duplicated(data$filename)
+verbs_data = data[!z,c("filename","sexe_prenom","year","rubrique")] %>% 
+  right_join(files,by="filename")
+#verbs_data = filter(verbs_data,sexe_prenom =="Men")
+verbs = unique(read.csv("https://raw.githubusercontent.com/gillesbastin/old_fashion_nlp/refs/heads/main/cues_all.csv")$lemmatized_cue)
+```
+
+```{r}
+##Compute odds ratios
+threshold = 30
+men = verbs_data %>% group_by(year) %>% mutate(
+    verbs_men_lemmatized = strsplit(verbs_men_lemmatized, ";")) %>% 
+    unnest(verbs_men_lemmatized) %>% 
+    count(verbs_men_lemmatized) %>% ungroup() %>%
+    complete(verbs_men_lemmatized = verbs,fill = list(n = 0,year = 1945)) %>%
+    arrange(verbs_men_lemmatized)
+  
+men <- men %>%
+  pivot_wider(
+    names_from = verbs_men_lemmatized, 
+    values_from = n, 
+    values_fill = 0
+  )
+men = dplyr::filter(men,year %in% 1945:2024)
+men$decade = (men$year-1945) %/% 10
+men = men %>% dplyr::select(-year) %>% 
+  group_by(decade) %>% summarise(across(everything(),sum))
+
+
+
+women = verbs_data %>% group_by(year) %>% mutate(
+  verbs_women_lemmatized = strsplit(verbs_women_lemmatized, ";")) %>% 
+  unnest(verbs_women_lemmatized) %>% 
+  count(verbs_women_lemmatized) %>% ungroup() %>%
+  complete(verbs_women_lemmatized = verbs,fill = list(n = 0,year = 1945)) %>%
+  arrange(verbs_women_lemmatized)
+
+women <- women %>%
+  pivot_wider(
+    names_from = verbs_women_lemmatized, 
+    values_from = n, 
+    values_fill = 0
+  )
+women = dplyr::filter(women,year %in% 1945:2024)
+women$decade = (women$year-1945) %/% 10
+women = women %>% dplyr::select(-year) %>% 
+  group_by(decade) %>% summarise(across(everything(),sum))
+
+
+total_men = rowSums(men)
+total_women = rowSums(women)
+odds_ratios = (men/total_men)/(women/total_women)
+log_odds_ratios = odds_ratios %>% log2()
+log_odds_ratios = data.frame(log_odds_ratios)
+log_odds_ratios[log_odds_ratios == Inf | log_odds_ratios == -Inf] <- NA
+
+vars = 1/men + 1/women
+#log_odds_ratios[vars > .025] = NA
+log_odds_ratios[men+women < threshold] = NA
+vars[men+women < threshold] = NA
+#vars[vars > .01] = NA
+```
+
+```{r}
+##Plot degree of stereotype
+v = is.na(log_odds_ratios[1,])
+  #stereotypes = apply(log_odds_ratios[,!v], 1, function(x) 1/sum(!is.na(x))*sum(x^2, na.rm = TRUE))
+  stereotypes = apply(log_odds_ratios[,!v], 1, function(x) sd(x,na.rm = TRUE))
+
+  
+  years <- 1940 + (1:8) * 10
+  stereotypes_df <- data.frame(
+    Year = years,
+    stereotypes = stereotypes
+  )
+plot1 = ggplot(stereotypes_df, aes(x = Year, y = stereotypes)) +
+    geom_line() + ylab("Standard deviation among verbs") +
+    scale_x_continuous(breaks = years) + xlab("Date")
+
+z = log_odds_ratios
+z[men + women < 100] = NA
+v = is.na(z[1,])
+z = z[,!v]
+z$decade = 1940 + (1:8)*10
+z = gather(z,"key","value",-decade)
+total = colSums(men[,!as.vector(v)] + women[,!as.vector(v)],na.rm=T) %>% data.frame()
+total$key = row.names(total)
+z = left_join(z,total,by="key")
+z = filter(z,!key %in% excl)
+colnames(z)[4] = "s"
+plot2 = ggplot(z,aes(decade,value,group=key)) + 
+                 geom_line(aes(alpha =log(s)),linewidth=.4) + 
+  scale_alpha_continuous(guide = "none") +  scale_x_continuous(breaks = years) +
+  ylab("Verbs log odd-ratio") + xlab("") 
+plot_grid(plot2,plot1,nrow=2,labels = c("A","B"),align="v",label_size = 50,label_x = .1)
+ggsave("../figures/spaghetti.png",dpi=320,bg="white",width=9, height=7)
+  
+
+#ggsave("../figures/stereoptype_over_time.png",dpi=320,bg="white",width=7, height=5)
+```
+
+```{r}
+stereotypes_verbs = colMeans(log_odds_ratios/(stereotypes/mean(stereotypes)),na.rm=T)
+  stereotypes_verbs[colSums(men) + colSums(women) < 100] = NA
+  stereotypes_verbs = stereotypes_verbs %>% data.frame()
+  colnames(stereotypes_verbs) = "Masculinity"
+  stereotypes_verbs$var = colMeans(vars,na.rm=T)/colSums(!is.na(vars))
+  stereotypes_verbs$Verb = colnames(men)
+  stereotypes_verbs = stereotypes_verbs %>% filter(!is.na(Masculinity)) %>%
+    arrange(Masculinity)
+  
+excl = c("naître","falloir","aimer","consacrer","savoir","demeurer","sembler","rester","mettre","devenir","devoir","diriger","créer","convier","connaître","attendre","aller",'organiser')
+stereotypes_verbs = filter(stereotypes_verbs,!Verb %in% excl)
+  
+n = 20
+gaps <- data.frame(
+  Masculinity = rep(0, 3),  # No bar for the gap
+  var = rep(0, 3),         # No error bar for the gap
+  Verb = c("...", "...", "...") # Multiple placeholders,
+  
+)
+
+verbs_plot_with_gap = rbind(head(stereotypes_verbs,15),gaps,tail(stereotypes_verbs,15))
+verbs_plot_with_gap$Verb_english = dplyr::recode(verbs_plot_with_gap$Verb,murmurer="Whisper",raconter="Recount",sourire="Smile",hurler="Scream",traduire="Translate",souvenir="Remember",soupirer="Sigh",témoigner='Testify',lire='Read',avouer='Avow','(s)enthousiasmer'='Enthuse',relater='Relate',réaliser="Realize",enchaîner="Continue",finir="End",estimer='Estimate',avertir='Warn',reprocher="Reproach",accuser="Accuse",calculer ="Calculate",arguer="Argue",indiquer="Indicate",menacer="Threaten",ajouter='Add',inviter="Invite",mentionner='Mention',tonner="Thunder",déclarer="Declare",promettre="Promise",prédire="Predict",'(se)désoler'='Lament',glisser='Slip', railler="Mock",exprimer="Express")
+verbs_plot_with_gap$Verb = dplyr::recode(verbs_plot_with_gap$Verb,souvenir="(se)souvenir")
+
+breaks <- c(log2(0.25), log2(0.5), 0, log2(2), log2(4), log2(8))
+labels <- c("0.25x", "0.5x", "Same", "2x", "4x", "8x")  
+ggplot(verbs_plot_with_gap, aes(x = -Masculinity, y = reorder(Verb, -Masculinity))) +
+    geom_bar(stat = "identity", fill = "black",
+             alpha = 0.4, width = 0.3) +
+    geom_errorbar(aes(
+      xmin = -Masculinity - sqrt(var), 
+      xmax = -Masculinity + sqrt(var)
+    ), width = 0.2) +
+    geom_vline(xintercept = 0, linetype = "dashed", color = "gray50") +
+    ylab("Verb") +
+    theme(
+      panel.grid.major.y = element_blank(),
+      panel.grid.minor.x = element_blank(),
+    ) +
+  scale_x_continuous(
+    breaks = breaks,
+    labels = labels,limits=c(NA,2.4)
+) + xlab("Relative appearance for women vs men") + geom_text(aes(x=2.2,y = reorder(Verb, -Masculinity),label=tolower(Verb_english)),hjust=0,family = 'EB Garamond',size=14,alpha=.7)
+
+ggsave("../figures/most_gendered_verbs.png",dpi=320,bg="white",width=9, height=5)
+
+```
+
+```{r}
+##Segregation mentions
+dd = data%>% filter(mentions_women > .995,rubrique!= "Uncl assifiable",year > 1945) %>% group_by(year,rubrique) %>% summarise(##Measuring sex-ratio experienced by women
+  citations_men = sum(citations_men,na.rm=T),
+  citations_women = sum(citations_women-1,na.rm=T),
+  mentions_men = sum(mentions_men,na.rm=T),
+  mentions_women = sum(mentions_women-.995,na.rm=T),
+  nwords = sum(nwords)
+  #entities = sum(entities)
+) 
+d = data %>% filter(year > 1945,rubrique!= "Unclassifiable") %>% group_by(year,rubrique) %>% summarise(##General sex ratio
+  citations_men = sum(citations_men,na.rm=T),
+  citations_women = sum(citations_women,na.rm=T),
+  mentions_men = sum(mentions_men,na.rm=T),
+  mentions_women = sum(mentions_women,na.rm=T),
+  nwords = sum(nwords)
+  #entities = sum(entities)
+)
+
+dd$women_ratio = dd$mentions_women/(dd$mentions_men+dd$mentions_women)
+d$women_ratio = d$mentions_women/(d$mentions_men+d$mentions_women)
+dd = dd %>% merge(d,by = c("year","rubrique"))
+dd$exposure_index = dd$women_ratio.x/dd$women_ratio.y
+ggplot(filter(dd,mentions_women.y > 100),aes(year,exposure_index)) + geom_line() + 
+  facet_wrap(.~rubrique) + xlab("Date") + ylab("Women overexposure to women mentions") + scale_y_continuous(trans="log2")
+ggsave("../figures/segregation_mentions.png",dpi=320,bg="white",width=9, height=5)
+```
+
+```{r}
+dd = data%>% filter(citations_women > .995,rubrique!= "Unclassifiable",year > 1945) %>% group_by(year,rubrique) %>% summarise(
+  citations_men = sum(citations_men,na.rm=T),
+  citations_women = sum(citations_women-1,na.rm=T),
+  mentions_men = sum(mentions_men,na.rm=T),
+  mentions_women = sum(mentions_women-.995,na.rm=T),
+  nwords = sum(nwords)
+  #entities = sum(entities)
+) 
+
+dd$women_ratio = dd$citations_women/(dd$citations_men+dd$citations_women)
+d$women_ratio = d$citations_women/(d$citations_men+d$citations_women)
+dd = dd %>% merge(d,by = c("year","rubrique"))
+dd$exposure_index = dd$women_ratio.x/dd$women_ratio.y
+ggplot(filter(dd,citations_women.y > 100),aes(year,exposure_index)) + geom_line() + ylim(c(.8,NA)) + 
+  facet_wrap(.~rubrique) + xlab("Date") + ylab("Women overexposure to women citations") + scale_y_continuous(trans="log2")
+ggsave("../figures/segregation_citations.png",dpi=320,bg="white",width=9, height=5)
+```
+
+```{r}
+#quotes length data preparation
+files = read.csv("~/Downloads/citations_length_by_article.csv",
+        col.names = c("filename","n_men","n_women","length_men",
+        "length_women"))
+z = duplicated(files$filename)
+files = files[!z,]
+library(stringr)
+files$filename = str_c(files$filename,".txt")
+z = duplicated(data$filename)
+length_data = data[!z,c("filename","sexe_prenom","year","rubrique")] %>% 
+  right_join(files,by="filename")
+
+z = length_data %>% group_by(year) %>% mutate(
+    length_men = strsplit(length_men, ";") %>% lapply(as.integer),
+    length_women = strsplit(length_women, ";") %>% lapply(as.integer)) 
+
+z_men = z %>% unnest(length_men)%>% group_by(year) %>%
+  summarise(mean_quote_length=mean(length_men))
+z_women = z %>% unnest(length_women) %>% group_by(year) %>%
+  summarise(mean_quote_length=mean(length_women))
+z_men$ratio_length = z_men$mean_quote_length/z_women$mean_quote_length
+ggplot(z_men %>% filter(year > 1944),aes(year,ratio_length)) + geom_point() + geom_smooth(color='black',se=F) + xlab("Date") + ylab("Male-to-female quote length ratio")
+ggsave("../figures/length.png",dpi=320,bg="white",width=9, height=5)
 ```