I was able to gather a small sample of 2,492 tweets from the hashtags #RIPKobe and #GirlDad to see what people were saying on Twitter. I expected these hashtags to contain more positive remarks about Kobe Bryant because of the context in which they would be used, #RIPKobe with tributes and #GirlDad mentioning Bryant as a father figure.

Some of the more popular words that appeared in this WordCloud were love, Kobe, dad and daughter. Obviously from looking at the results of these hashtags we are seeing more results that are talking about his life off the basketball court.

You can also very clearly see the influence of the hashtag #GirlDad on the WordCloud. With a large number of words in the visualization mentioning Bryant as a father, it becomes evident that this is an important part of the person that he was.

In this WordCloud that was generated using more positive reactions, I was a little surprised to see a lack of his basketball performance mentioned. Going into this project, I expected to see more conversation about Bryant as a basketball player with terms like All-Star or MVP in the positive visualization.

Below, you can see a table with the frequencies for the top ten 10 words that appeared in the Twitter data. Since this data was pulled from the #GirlDad, it seems fitting that most of the words reference Bryant as a father figure.

I wanted to see what people on Twitter had to say about Kobe Bryant without skewing the conversation one way or another. I ended up using the keyword “Kobe” and left my laptop to scrape all the tweets from the week of March 30-April 6. The result was 93,549 tweets mentioning Bryant that I split up into 6,171,161 words.

In my WordCloud, some of the most popular words that were used to describe Kobe Bryant were Hall of Fame, basketball, miss, RIP, love and the names of other basketball stars.

The large majority of the words that were used to describe Bryant had a very positive connotation. It seems that from these tweets most people were remembering him as a basketball player and Hall of Famer. We can infer that his career on the hardwood may be the way that Bryant will best be remembered.

There was also a large number of tweets that showed sympathy when mentioning Bryant, even though he passed away months ago. For these terms to show up so prominently in his WordCloud demonstrates the impact that he had on thousands of lives.

One reason that I suspect so many mentions of “Hall of Fame” and other popular basketball players is because on April 4, Kobe Bryant was voted into the 2020 Hall of Fame class along with Tim Duncan and Kevin Garnett (these were two names that appeared frequently). Having the names of these other great players shows that he is being mentioned in conversation with them. This is evidence that Bryant was one of the best to play the game.

Since Twitter only allows you to search the past seven days, I was unable to access tweets from late January when he passed away like I had originally hoped. Still, I continued to analyze the close to 100,000 tweets that I had gathered to see if there was any sort of negativity.

Either way, after seeing an overwhelmingly positive WordCloud about the life and career of Bryant, I returned to my original python script and scraped Twitter again. This time I searched for Bryant’s name mentioned with the keyword “sexual assault” and “rape” to see if any of these tweets were critical of Bryant.

I was shocked when only one single tweet was returned:

With the social network twitter, usually there is an audience that has different feelings, so to see such a uniform and positive response to Bryant being honored and placed in the Hall of Fame was interesting. Twitter is known as one of the more negative social media platforms. On any given tweet or hashtag, there are typically multiple people who are critical or disagreeing, so the fact that only one person seemed to object to Bryant being inducted into the Hall of Fame is very surprising.

Below, you can see a table with the frequencies for the top ten 10 words that appeared in the Twitter data. His name was obviously the most used term in these tweets but seeing adjectives like “Hall of Fame” and “best” appear tell us that his basketball playing abilities will definitely contribute to his legacy.

In each attempt to scrape negative tweets about Kobe Bryant within the past seven day, I was unable to come up with a sufficient amount of data, and since part of my research relied on examining the successes and conflict in Bryant’s life, I decided to gather data from 10 different articles that discussed his sexual assault case in 2004.

The sources that I gathered ranged from The New York Times to ESPN. From these articles, I created a dataset containing 49,778 words that were used to talk about Bryant and his case.

The most popular words in the WordCloud visualization included Bryant, Colorado, sexual assault, rape, case, woman and accuser. It was interesting to see that none of the more common words mentioned his status as a basketball player. Instead, it seemed more focused on the charges brought against him.

I noticed that of the words that tended to appear more frequently in the visualization, a large number of them had to do with the facts of the case rather than criticizing Bryant for his actions. Interestingly, not once in the WordCloud did the words guilty or innocent appear. I went back and searched the ten articles and found that guilty appeared six times, while innocent appeared only four. When I set out to find these articles, I had hypothesized that the resulting WordCloud would be more negative towards Bryant.

Overall, this WordCloud was significantly more negative than the others that I created, but not quite to the degree that I had thought. This could be because in 2004, the platform to speak out against sexual assault wasn’t quite established. However, now, because of the #MeToo movement, there is a much larger audience that is fighting for justice on these issues, so it is more common to see articles that call out celebrities who are accused of these acts.

Below, you can see a table with the frequencies for the top ten 10 words that appeared in the articles.