Earthquake Start of a project to bring in information from a website with an Application Programming Interface (API). We are going to use a modified version of "Project: Fetching Current Weather Data" from "Automate the boring stuff with Python" by Al Sweigart What is going on below? We import libraries to dela with json, reuest from the server and the pandas library. In [1]: import json , requests import pandas as pd from pandas import json_normalize In this section we creating a string made up of the URL. Requesting the information from the site with the URL we created and pass back the information. Data comes from the US Geological survey https://www.usgs.gov/about/about-us/who-we-are and one of their earthquake feeds. Then print out what was returned. In [2]: url = 'https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/all_hour.geojson' response = requests . get ( url ) response . raise_for_status () Now we need load the data which i
Get link
Facebook
Twitter
Pinterest
Email
Other Apps
Social Visualisation using TAGS
Get link
Facebook
Twitter
Pinterest
Email
Other Apps
In a previous post I discussed using a twitter social analysis tool. In this post I am going to discuss another fantastic tool TAGS.
Developed by Martin Hawksey (@mhawksey) this is a great free tool for visualising tweets; using a Google Spreadsheet to be the front end to link into Google Visualization API.
I am not going to go into a description of how to set-up it here; I don't need to the best place to go for this is to follow the link here. In this post, I am mainly going through my (probably bit random) thoughts, an example and experiences with it.
So the example was a twitter chat #caschat or #CASchat held on the 13th December 2016. The conversation is between teachers of computing as well as those who support or have an interest in supporting computing in schools.
Figure 1
I find the visualisation of twitter chats interesting; seeing the links between people, groups that form, but also seeing the way people engage. So I set up the spreadsheet following the instructions (it is relatively simple to), linked to twitter, etc and entered the hashtag I was interested in. The setup is shown in figure 1.
Ran the scripts and then press the TAGExplorer button on the sheet and get the clickable URL to the actual graph. One of the nice features, is you can make the graphs shareable. An example of one of the graphs is shown in figure 2.
Figure 2
Breaking these in two; figures 3 and 4 for clarity.
Figure 3
Figure 4
Figure 3 shows the number of tweets per a particular Tweeter. The graph in figure 4 is the main attraction as well showing the links and indicating the size of their contribution, it is interactive; click on a node and you get some of the individual's history of tweets, for this hashtag, as well as replies and mentions (see Figure 5 for an example). The solid lines in figure 4 indicate conversations - it is quite a 'chatty' group with a lot of mentions of others.
Figure 5
The tool actual allows you to replot the conversation based on mentions or retweets (see figure 6); the links, mentions are there still as well as the retweets. The only criticism I have of the software is I find difficult to see, from the key at the bottom, which are retweets and which are mentions.
Figure 6.
This is a fantastic (and free) tool which can be used to dig down into the conversations. I still have a lot to learn but it is good fun to play with. It was mentioned at a recent conference:
Originally posted in https://computingnorthampton.blogspot.co.uk/2016/01/mini-projectsocial-network-analysis-fun.html figure 1. #StarWars 30/12/15 Playing with Socioviz ( socioviz.net ) - a free online tool for looking at influence on twitter. The image to left show connections between tweeters using the hashtag #StarWars on the 30th December 2015 up to 6pm (GMT). Figure 2 shows the most active tweeters for this hashtag and the most influential based on Retweets and Mentions - the four greatest influencers are picked out in the video below, showing the map evolving (speed-up 20 times). figure 2 To experiment with this a bit more +The Royal Institution has a long traditional of holding a series of Christmas Lectures which are now televised, Dr Kevin Fong presented this years. I was curious about who the biggest influencers on twitter were for the hashtag #xmaslectures over the three days of the show. The three biggest influencers came out as @Kevin_Fong @Ri_Sci
Recorded with https://screencast-o-matic.com Video looks at the using NodeXL https://nodexl.com/ to look at tweets. The professional version was used but a free version is available. Uploaded the graph produced to the NodeXL Graph Gallery and so the hidden gems other than the graph that is produced. The links for downloading settings, etc used in a particular graph are at the bottom of the page linked out from the gallery. All views are the authors and do not reflect the views of any organisations the author is associated with. Twitter: @scottturneruon
Comments
Post a Comment