Challenge #89: Analyzing Social Data

[AnaisComyn]

Finish !

[troyeb4]

Got the basics covered. I created additional flags as some hashtags were embedded with other words, e.g. "SDGS".

Spoiler

[JethroChen]

Spoiler

[Samanthaj_hughes]

I would look into the Hashtags, and the top 5 hashtags used. I have separated them out and cleaned the data. I will probs use Tableau.

[DataHabanero]

Top 10 most retweeted Tweets!

[DelHansen]

After some basic cleanup and duplicate removal (or what I considered duplicates) I did a bit of sentiment analysis to see if there were trends on positive v. negative tweets... Also fooled around with Alteryx reporting as an output!

[Normster]

I took a step back and looked at the data in the various files before starting.  After determining that they had similar structures, I joined the files, removed redundancies, and then grouped the data into three streams.  I was interested to see who the most active users were (by number of tweets, retweets, followers, etc.); the topics that garnered the most interest; and the most active locations (tying topics to geography).

I pared down the data to include only the records with multiple actions.  Although the data could have been regrouped to create a single file with the relevant data, I left the data in their relevant group.  Also, for the locations, I intentionally did not spend a lot of time on cleanup.  (That's a whole other project).

In retrospect, I could probably have done more cleanup on the joined files, minimizing downstream work.

[Qiu]

I considered it done when I can get all the data from 10 files.

Spoiler

[ArnavS]

fun one

[Tony_Castillo]

I went the route of two fold.

First, i wanted to find the Top influencers to see if there were any heavy influencers, In the end, I settled with the Top 10 influencers as shown in the below spoiler.

Second: In the spirit of our current election forces, I wanted check for any twitter bots that fed the messages. Unfortunately, user join date was not included as that would be one sign. Instead, I calculated the % of a user's total tweets were consumed by these topics. Anything > 50% of a user's total tweets dedicated to this topic was filtered out as a 'bot'. 137 users found

Spoiler

 

Next steps for visualization could be to create tables and render output.