Andres

Why this graph ?

To analyze the tweets, we devised a controversial score. This score was formulated under the premise that a higher number of replies compared to likes indicates a more controversial tweet, while a higher number of likes relative to replies suggests greater agreement on the topic. Hence, we utilized the following formula to provide a clearer representation of the data.

\[ \text{Controversial Score} = \frac{\text{replies\_count}}{\text{likes\_count} + \text{retweets\_count} + 1}\] Therefore:

• As we move towards the right, the tweet have more likes.
• As we move towards the top, the tweet becomes increasingly controversial.

Limitation:

• Tweets with a low number of likes were excluded due to challenges in calculating scores accurately.
• The use of log visualization could potentially lead to misinterpretation.

Visual representation

df <- df %>% 
  mutate( tweet = str_replace_all(tweet, "https:\\/\\/t.co\\/[a-zA-Z0-9]+", "" )) %>% 
    filter( likes_count >= 10) 

p <- df %>% 
  ggplot(aes(likes_count, score, color = score, 
             text = paste("score:",round(score,2),"\n",
                          "Likes:",round(likes_count,2),"\n",
                          "replies:",round(replies_count,2),"\n",
                          tweet )
  )
  ) + 
  geom_point() +
  scale_color_gradient(low = 'green',high = "red",transform = "log10") +
  scale_x_log10() + 
  scale_y_log10() + 
  labs(x = "Number of likes", 
       y = "Controversial Score")

ggplotly(p, tooltip ="text")

Figure 1: Visual representation of tweets

Tabular representation

table_recap <- df %>% 
  select(score,likes_count,replies_count,tweet) %>% 
  dplyr::arrange(desc(score)) 

DT::datatable(table_recap,
              colnames = c("score","likes_count","replies_count","tweet"),
          options = list(paging = T, 
                         pageLength = 20,
                         dom = "t",
                         scrollX = TRUE),
          rownames = FALSE,
          filter = 'top',
          escape = F)

Table