A brief introduction to a Twitter dashboard, that helps in searching Twitter for any keywords you want. The tool provides an easy way to get thousands of tweets, filtering them, visualizing them, and exporting the raw data. https://www.dashboardom.com/twitterdash

1. Twitter Dashboard
Elias Dabbas

2. Overview
• Search Twitter for the latest tweets containing any keyword
• Specify the number of tweets
• Specify the language

3. • Set the number of tweets (defaults to 100), up to single-digit
thousands (depends on availability)
• How to search:
Operator Finds Tweets…
watching now containing both “watching” and “now”. This is the default operator.
“happy hour” containing the exact phrase “happy hour”.
love OR hate containing either “love” or “hate” (or both).
beer -root containing “beer” but not “root”.
#haiku containing the hashtag “haiku”.
More about search operators

4. Example: @apple
• 1,000 tweets containing “@apple” (mentioning apple’s
account) in English
• Tweets happened on May 23, 2019

5. Counting Words
• The first thing to do to understand a text list (corpus of
documents), is to count the words it contains, and see what
topics are the most dominant
• Imagine two people tweeting:
• “I like chocolate”
• “I like ice-cream”
• Counting the words, we see that chocolate and ice-cream each
form 50% of the words in the data set (excluding the stop-words)

6. Counting Words
• Adding some more data (number of followers of tweeters):
• “I like chocolate”: 1,000 followers
• “I like ice-cream”: 99,000 followers
• Counting the words by number of followers (impact), we see that
chocolate potentially appears to 1k people vs 99k people for ice-
cream.
• Absolute frequency: 50:50
• Weighted frequency: 1:99

7. • words in tweets, weighted by followers count
• two different counts, absolute and weighted

8. • Do the same for user descriptions, to see how those
people describe themselves

9. What are “words”?
• A string of characters between two spaces (excluding
punctuation)
• But there are other entities in social media that can be interesting
• emoji
• mentions
• hashtags
• 2-word phrases

14. • Learn more about the users’ different attributes

15. Raw Data
• Export the data set for further analysis
• Filter the data online

16. Add/remove columns
• Select the columns that you want to see only
• 40+ columns available
• “Tweet …” columns are about tweets
• “User …” columns have data about users

17. Filter By Certain Columns
• example: filtering the by the user’s language
• Get the summary of how many use that language and the
percentage of the tweets that satisfy that condition

18. Other Filters
search text columns:
filter numeric columns by setting ranges:
boolean columns:
date ranges:

19. Try other queries
• search for certain #hashtags
• search for the same keywords in different languages
• search for “multi word phrases”
• be creative!
• Try it out now

20. advertools
• Python package for online marketing productivity and analysis
• Get the package
• Explore other dashboards/tools
• Feedback @eliasdabbas

21. Thank you