Visualizing Twitter Network with a Chord Diagram

In this workflow we will: - Read the dataset listing tweets with hashtag #KNIME around the week of July 12, 2018 - Row filter and GroupBy to get the number of retweets between every pair of users in both directions - Cross Joiner and Pivoting to create a weighted adjacency matrix listing each pair of users in two different cells, one for each retweet direction - Visualize the connections in a chord diagram created by the d3 script in the Generic JavaScript View node

Analyzing Twitter Data

This workflow examines a sample of tweets from the days surrounding the scottisch referendum for independence in 2014 [1]. After reading the data from a local database, basic text processing is used to extract hashtags from the dataset and term frequencies calculated and used to build a tag cloud. Subsequently, hashtag trending is examined over time, with a notable post election surge in the #the45 movement. Additionally, network analysis is performed in order to look at the most influential social graph surrounding this issue.

Collecting data from Twitter

This workflow is designed to collect and store a sample of tweets on a particular search term. With each execution of the workflow, tweets are collected, favorite and retweet numbers are updated in existing records in a SQLite Database and new tweets are written to that same table.

For longitudinal data collection, the workflow may be periodically executed manually, run via command line using the batch executor or optimally scheduled for regular execution via the KNIME Server (https://www.knime.org/knime-server)

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