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TWITTER ANALYSIS

Seeing the high level of online activism and engagement on Twitter, we analyzed what Twitter users were talking about, categorizing their sentiments about the Black Lives Matter movement.

Sentiments

Twitter Sentiment Around the Black Lives Matter Movement

Average Twitter Sentiment Around the Black Lives Matter Movement

Methodology:

1. Extract 10k tweets tagging with #blm or #blacklivesmatter each day. (Tweets extracted are mostly posted in the night).

2. Sentiment analyzer is built based on Flair Model which is trained with data sets from SemEval. The accuracy of trained Flair is about 70% on test set.

3. Average Sentiments = (#tweets with positive sentiment - # tweets with negative sentiment)/#tweets. It indicates the "net sentiment ratio".

sentiments

Twitter sentiment around the Black Lives Matter movement
from May 25, 2020 to June 25, 2020

Negative tweets were highly prevalent, especially at the beginning stage of this protest life-cycle. 

Our analysis split tweets into three sentiment categories: positive, negative, and neutral. Positive tweets included terms that have more positive connotations; Negative tweets included more terms that have more negative connotations. Neutral tweets did not use particularly positively or negatively connotative language. Positive/Negative/Neutral very narrowly defines the emotional state ("sentiments") in which online users were discussing issues around BLM.

Positive_Netural_Negative Table - Sheet1

Note:

Sentiments do NOT refer to attitudes towards a specific topic, but rather refers to the how the topics are being discussed.

Examples were edited to censor crude language and specific usernames.

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Most of the tweets were negative, especially in the first couple of days following George Floyd’s murder. Later on, as the movement transitioned into its peak stage, sentiments became more stable. Still, 60% of tweets were classified into the "negative" category. (The only major exception to this trend was seen on Juneteenth.)

Hashtag Topics

hashtag topics

Topics around the Black Lives Matter movement from May 25, 2020 to June 25, 2020 based on trending Twitter hashtags

Trending topics evolved from focusing on the individual tragedy around George Floyd’s death to focus more broadly on long-term actions (e.g. defund police).

Top Hashtags on Twitter around Black Lives Matter

# hashtags

Methodology:

1. Extract 10k tweets tagging with #blm or #blacklivesmatter each day. (Tweets extracted are mostly posted in the night)

2. Exclude hashtags similar to "#blacklivesmatter"

3. Select top 15 hashtags for each day and map them to a certain category.

Learn more about the specific terminology we use in our Glossary.

Growing Stage: In the early growing stage, hashtags were focused around George Floyd’s murder, (e.g.  #georgefloyd, #icantbreathe, #justiceforgeorgefloyd). At the end of May, protest-related hashtags were emerging, which was followed by hashtags associated with specific activities, such as #blackouttuesday.

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Peak Stage: #blackouttuesday was at the peak stage of this protest life cycle. Around that time, Breonna Taylor's murder gained online traction, as well as criticism around the police system. Approaching the end of this stage, #defundpolice stepped into the top ten trending hashtags for the first time, along with a growing number of unrelated hashtags.

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Aging Stage: The stage is the most complex. Topics around other related events, and holidays (#juneteenth) came and went. Tangentially related topics (e.g. antifa) were drawing the public's attention. Nevertheless, #defundthepolice was stable within the list of top ten trending hashtags throughout the rest of June. At the end of June, #georgefloyd was no longer the top related hashtags to BLM, but #defundthepolice was.

next: Topics

References (order of appearance)

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Lee, J. (2020). Search interest in George Floyd  [Visualization]. Flourish. https://public.flourish.studio/visualisation/2700383/​

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Google. (n.d.). FAQ about Google Trends data. Trends Help. https://support.google.com/trends/answer/4365533?hl=en&ref_topic=6248052

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