Data Analysis

Information Flow During Crises

What happens when hell breaks loose on Twitter?  Here is what the data shows. 

The Retweet Curve
Someone always breaks the bad news and he gets retweeted over time, then he gets copied and these clones get lesser retweets over time.  Then there is a tweet that injects new information in the crises that gets a higher number of retweets as evident when Sheikh Makaburi was shot.

#Makaburi

time_series

The first curve breaks the news:
@MohaJichoPevu: “Unconfirmed reports: shooting incident involving Abubakar Makaburi in Bamburi”

The second longer curve adds humor
@RobertAlai: Ati bullet left #BabyOsinya’s brain for #Makaburi’s head. Damn!!!!

#ChaseBank

chase

Chase Bank held an event and folks were retweeting  the conversations. An interesting retweeting pattern is shown by a tweet in the light blue line (a momentary pause is retweeting that picks -up later). It is the tweet shown below.

When the banks post a tweet on their closure, the retweet pattern picked up on . A red straight line on the graph emerges which announces the closure and get retweeted at an exponential rate – note it is the same time that the earlier tweet ‘the jungle has its surprises’ picks up the retweets. Once again like #Makaburi the humorous tweet has the longest retweet lifetime. 

Gaining Followers
The most influential people during a crisis gain a lot of followers. During #Makaburi Robert Alai gained 147 followers in 15 hours. @GitobuImanyara gained 83 followers and @jamessmat gained 85 followers.

alai

Good or humorous information during crises get’s you more followers.

Sentiment Analysis
After an event break out of Twitter, the sentiment goes to the devil. Visualizing the simple moving average of sentiment values most indicate an increased use of curse words.

#ChaseBank

chase_sentiment

#MpeketoniAttack
mpeketoni

 

 

Chase Bank data provided by Nendo

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