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Is Cristiano Ronaldo lost his interest in twitter? A fun analysis of tweets of Cristiano Ronaldo using R

Arun Gopinath / 2021-11-21

A detailed analysis of tweets of famous footballer Cristiano Ronaldo in R

In this post we are going to have a quick roundup of Cristiano Ronaldo’s tweets. For this mission various powerful tools of ‘R’ are used.


Get Ronaldo’s tweets timeline

Due to limitations of twitter new policy, we can only retrieve last 3200 tweets of a user. In our case, as of writing this article his total tweets are just 3733. So we are covering most of his twitter journey here.

Ronaldo <- get_timeline("@cristiano", n= 3200)

Let’s dive into it.

Plotting tweets per months over the time period

As the first user case lets see how he tweets over these years.

ggplot(data = Ronaldo,
       aes(month(created_at, label=TRUE, abbr=TRUE),
           group=factor(year(created_at)), color=factor(year(created_at))))+
  geom_line(stat= "count") +
  geom_point(stat="count") +
  labs(x="Month", colour="Year", xlab = "Month", ylab = "Number of tweets",
       title = "Cristiano Ronaldo's tweets over the time period", 
       caption = "Created by Arun Gopinath") +

It’s quite difficult to obtain a trend here. But one thing is sure, his interest in twitter is just fading away.

Bar plot to get more insight

ggplot(data = Ronaldo, aes(x = year(created_at))) +
  geom_bar(aes(fill = ..count..)) +
  xlab("Year") + ylab("Number of tweets") + 
  labs(title = "Cristiano Ronaldo's tweets over the time period", 
       subtitle = "2013 & 2014 been his prime time in twitter",
       caption = "Created by Arun Gopinath")+
  scale_x_continuous (breaks = c(2010:2021)) +
  theme_minimal() +
  scale_fill_gradient(low = "yellow", high = "darkgreen")

Is there any pattern over the months ?

ggplot(data = Ronaldo, aes(x = month(created_at, label = TRUE))) +
  geom_bar(aes(fill = ..count..)) +
  xlab("Month") + ylab("Number of tweets") +
  labs(title = "Cristiano Ronaldo's tweets - Monthwise", 
       subtitle = "It's december",
       caption = "Created by Arun Gopinath")+
  theme_minimal() +
  scale_fill_gradient(low = "yellow", high = "red")

What about over the days ?

ggplot(data = Ronaldo, aes(x = wday(created_at, label = TRUE))) +
  geom_bar(aes(fill = ..count..)) +
  xlab("Day of the week") + ylab("Number of tweets") + 
  labs(title = "Do you know which day Cristiano Ronaldo pick to tweet least ?", 
       subtitle = "Weekends are holidays",
       caption = "Created by Arun Gopinath")+
  theme_minimal() +
  scale_fill_gradient(low = "turquoise3", high = "darkgreen")

Sundays are usually his least tweeted day so far. While Thursdays are more engaged one.

Tweets over the time

Let’s look another factor which may influence his tweet pattern - Time during a day.

But our date and time are in combined form so clean it up using hms and scales packages as given below.

## Get hour, minute and seconds from tweets

Ronaldo$time <- hms::hms(second(Ronaldo$created_at),

## Converting to `POSIXct` as ggplot isn’t compatible with `hms`
Ronaldo$time <- as.POSIXct(Ronaldo$time)

Our data is ready to plot. Any guess ?

ggplot(data = Ronaldo)+
  geom_density(aes(x = time, y = ..scaled..),
               fill="steelblue", alpha=0.3) +
  xlab("Time") + ylab("Density") +
  labs(title = "Cristiano Ronaldo tweet pattern", 
       subtitle = "His sleeping pattern is quite good !",
       caption = "Created by Arun Gopinath")+
  scale_x_datetime(breaks = date_breaks("2 hours"),
                   labels = date_format("%H:%M")) +

As expected from a super player like Ronaldo, his twitter usage is negligible between 12 am and 6 am. Another reason for his super powers on the field. Also he spends more time online during evening section.

Retweets v/s Original tweets

Do Ronaldo retweet more nowadays?

ggplot(data = Ronaldo, aes(x = created_at, fill = is_retweet)) +
  geom_histogram(bins=30) +
  xlab("Time") + ylab("Number of tweets") +
  labs(title = "Cristiano Ronaldo's Retweets v/s Original tweets", 
       subtitle = "Almost stopped retweeting after 2015 !",
       caption = "Created by Arun Gopinath")+
  theme_minimal() +
  scale_fill_manual(values = c("darkolivegreen3", "darkolivegreen4"), name = "Retweet")

No not at all !!

Text mining - Let’s deep dive into the tweet data

What about Hashtags frequency ?

What are the top 10 hashtags he tweeted ?

Ronaldo %>% 
  unnest_tokens(hashtag, text, "tweets", to_lower = FALSE) %>%
  filter(str_detect(hashtag, "^#")) %>%
  count(hashtag, sort = TRUE) %>%
  top_n(10) %>% 
  ggplot(aes(x = reorder(hashtag, -n), y =n))+
  geom_segment( aes(xend=hashtag, yend=0)) +
  geom_point( size=4, color="red") +
  theme_bw() +
  labs(title = "Cristiano Ronaldo's top 10 twitter #hashtags", 
       subtitle = "",
       caption = "Created by Arun Gopinath")+

Most of them are sports related, especially about teams he represented and his personal milestones.

Most retweeted tweet

Which tweet is the most retweeted tweet ?

Ronaldo %>% 
  arrange(-retweet_count) %>%
  slice(1) %>% 
  select(created_at, screen_name, text, retweet_count, status_id)

Most liked tweet

Ronaldo %>% 
  arrange(-favorite_count) %>%
  top_n(5, favorite_count) %>% 
  select(created_at, screen_name, text, favorite_count)

Top mentions

Ronaldo %>% 
  unnest_tokens(mentions, text, "tweets", to_lower = FALSE) %>%
  filter(str_detect(mentions, "^@")) %>%  
  count(mentions, sort = TRUE) %>%
## # A tibble: 10 x 2
##    mentions             n
##    <chr>            <int>
##  1 @Cristiano         177
##  2 @nikefootball       54
##  3 @GAMEbyRonaldo      37
##  4 @realmadrid         25
##  5 @VivaRonaldo        25
##  6 @cristiano          18
##  7 @TAGHeuer           17
##  8 @Herbalife          16
##  9 @SavetheChildren    16
## 10 @HeadsUp            13

Create a wordcloud using the words used in his tweets so far.

Find top words

Here we want to remove white spaces, symbols, signs etc. Also remove stop words, words which are frequently used by everyone, from the list.And finally sort it 1.

words <- Ronaldo %>%
  mutate(text = str_remove_all(text, "&amp;|&lt;|&gt;"),
         text = str_remove_all(text, "\\s?(f|ht)(tp)(s?)(://)([^\\.]*)[\\.|/](\\S*)"),
         text = str_remove_all(text, "[^\x01-\x7F]")) %>% 
  unnest_tokens(word, text, token = "tweets") %>%
  filter(!word %in% stop_words$word,
         !word %in% str_remove_all(stop_words$word, "'"),
         str_detect(word, "[a-z]"),
         !str_detect(word, "^#"),         
         !str_detect(word, "@\\S+")) %>%
  count(word, sort = TRUE)


Now plot a wordcloud from what we got.


words %>% 
  with(wordcloud(word, n, random.order = FALSE, max.words = 150, 
                 scale=c(2.6,0.25),colors=brewer.pal(8, "Dark2")))

Sentiment analysis

Sentiment analysis (or opinion mining) is a natural language processing (NLP) technique used to determine whether data is positive, negative or neutral. Sentiment analysis is often performed on textual data to help businesses monitor brand and product sentiment in customer feedback, and understand customer needs2.

Here we analyse 10 emotions from positive to disgust.

  • First convert text to ASCII to tackle strange characters like what we done above.
tweet_text <- iconv(words, from="UTF-8", to="ASCII", sub="")
  • Since we are playing with tweets of Ronaldo ignore retweets.
Tweet_text <- gsub("(RT|via)((?:\\b\\w*@\\w+)+)","",tweet_text)
  • Also remove mentions
Tweet_text <- gsub("@\\w+","",tweet_text)
  • Get sentiment score using ‘get_nrc_sentiment’ function.
Ron_sentiment <- get_nrc_sentiment((tweet_text))
  • To display it in ggplot we want to convert it into a data frame.
Sentimentscores <- data.frame(colSums(Ron_sentiment[,]))
  • For better understanding of data frame better headers are assigned.
names(Sentimentscores) <- "Score"
Sentimentscores <- cbind("sentiment"=rownames(Sentimentscores),Sentimentscores)
rownames(Sentimentscores) <- NULL

Sentiment plot

Finally our sentiment plot of Cristiano Ronaldo’s tweets 3.

  geom_bar(aes(fill=sentiment),stat = "identity")+
  labs(title = "Total sentiment of Cristiano Ronaldo's tweets based on scores",
       subtitle = "Positive vibes")+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

  • Positive vibes overall.


Cristiano Ronaldo is shifting his gears with new age social media like Instagram. Tweet frequency is dramatically getting lower over the years.Further analysis can be done based to mine more and more intersting details. Happy mining.

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