Geocode your Twitter network with NodeXL

19DecMay 7, 2015 By Marc Smith

As mobile devices become a major method for authoring and consuming social media, location data is increasingly a part of many posts, tweets, check-ins, and messages. Many Twitter clients, for example, can add the user’s current latitude and longitude to the metadata associated with a tweet. Other systems like Facebook Places, Google Latitude and Foursquare encourage users to declare where they are to the world, often passing the information to other social media sites.

Using this location data in network analysis opens up a range of new opportunities. Instead of a person – to – person social network, location data allows people to be linked to places and, by extension, places can be linked to other places based on the patterns of connection people create when located in a particular place. A convergence of network analysis and Geographic Information Systems in underway. A great example of this can be found in this wonderful video from the BBC which demonstrates the idea by mapping the flow of telephone calls, texts, and data around the UK and the wider world.

Link on the BBC

Even better is this video from the SensibleCity group at MIT:

Now, NodeXL (v.156) has the first of a series of features that will start to approximate the experience displayed in the video by supporting the import of location data about networks and plotting networks onto maps.

For now, we have started importing latitude and longitude data that Twitter makes available. If you check “Add a Tweet column to the Vertices worksheet” in NodeXL, Data, Import, From Twitter Search Network or From Twitter User Network, the Twitter user’s geographical coordinates will be added to the Vertices worksheet when they are available.

Note that not every tweet has a latitude and longitude, in fact many do not (yet). Further, note that not every latitude and longitude is accurate, many are not.

We need to implement more features for better location data support in a NodeXL workbook, but this is a start. We are interested in exploring geospatial networks and Twitter is a great data source. With this data in place we may look into features that emit KML files for exploration in other packages like Google Earth. A nifty Google Earth/Spreadsheet importer can take small sets (400) of location data points in a spreadsheet and export them to a KML file, something we could implement in the future as well. In addition we may be able to connect directly with services like Bing Maps and Google Maps to display connections between nodes with known locations. Metrics that calculate the distance between nodes seem sensible as well.

Location coordinates are the key to a cornucopia of related data about a place. Given a latitude and longitude it is possible to find the name of the city it is located in, look up data about that location in official records as well as resources like Wikipedia. Income, education, property values, weather, photos, and more can be pulled together from just a simple lat/long. All of these attributes could be used to cluster or illustrate the network visualization.

NodeXL TwitterScope: social media science in a bucket

23MayMay 7, 2015 By Marc Smith

by Dru!

Can useful observations be made by studying the social media sea one bucket at a time?

NodeXL has data import “spigots” for pulling social networks out of several social media systems including Twitter, YouTube, flickr, and email. Twitter networks of follows and followers, reply and mentions can be extracted based on either a user name or a search string “seed”. There are additional networks inside Twitter: a tie is created whenever two people tweet the same URL, for example, or are connected by tweeting from the same general location. For now, the NodeXL Twitter Data Importer is starting with these three initial twitter “tie-types”.

NodeXL queries are not exhaustive collections of Twitter data, we provide a more modest approach, grabbing a slice of recent content and analyzing that. Twitter has a sea of data, NodeXL is importing something like a study of buckets of ocean water. A recent scientific voyage to the Great Pacific Garbage Gyre, for example, collected hundreds of samples of ocean water as they sailed to the central location of the gyre. Each bucket revealed details about the larger state of the ocean (which does not look good). Simlarly, NodeXL is puling buckets of social media network data from the ocean of twitter and, despite the lack of scale, can do some useful science. In part this is a virtue imposed by necessity – constraints imposed by Twitter (even with a rate limit lifted “whitelisted” account) impose significant limits on what can be squeezed out of the Twitter API. For those who lack access to large data center resources, there are scale limits imposed by the capacities of a desktop/laptop device.

Access to large data sets is certainly a hallmark of the “new era of science” that generates observations not from samples but from exhaustive surveys of data terrains. Small samples miss important phenomena it is argued. The counter argument is that many important phenomena appear in most samples, even small ones.

Using the existing features in NodeXL, I can extract the twitter social network for a small group of user accounts. I can provide the names or ask twitter search to deliver them. Alternatively, a keyword can be used to collect all the users and their connections who recently tweeted containing that term. From this selected sample, several observations can be made:

> Not every keyword is equally connected

> Not every twitter user is equally connected nor are their neighbors

> Selected data extractions can be useful in the absence of a global view

Visualizing the connections among twitter users who tweet “oil spill” with NodeXL

06MayMay 7, 2015 By Marc Smith

The oil spill disaster in the Gulf of Mexico is a topic of great concern to many people in twitter.

This is the map of connections among people who tweeted the term “oil spill”. There are lots of isolated authors, people who tweeted “oil spill” but are not connected to anyone else who said the same phrase. The “giant component” is relatively sparse, there is no dense core of “oil spill” people yet. This is in contrast with many topics where a large, highly interconnected cluster of people defines the “center” of the discussion. This term remains highly diffuse.

This is the map of connections among people who tweeted the term “BP”.

The BP map also has a large population of isolates, people who are not part of a “BP” related conversation but have said the term. A small core of highly interconnected users is forming in the “BP” hashtag space but in contrast to a term like “solar”, there is still little cohesion and density in its core.

This is the map of connections among people who tweeted the term “solar”.

This is a topic with a more dense core of highly interconnected Twitter users who share the use of the string “solar” in their tweets. Ranks of isolates are also present in this topic space but the giant component is more internall connected and cohesive.

Workshop December 15th: Create your own twitter social network map

28NovMay 7, 2015 By Marc Smith

On December 15th in Mountain View, California join me for lunch and a workshop on creating social media network diagrams! We will provide a hands-on guide to creating maps of the collections of connections among people who tweet about various brands, topics, events, and concepts. Bring your own Windows/Office 2007 machine and we will set you up with the free and open NodeXL and help you create a map yourself of the topic of your choice.

Registration: http://smanalysistwitter.eventbrite.com/

For example, this is a map of the population of people who recently mentioned “NodeXL” in Twitter.

I see a large cluster of French speaking people who recently picked up on a tweet:

seekoeur: [Blog] Identifier des communautés et cartographier des relations sur Twitter avec NodeXL http://www.seekoeur.com/?p=854

This is a very distinct group from the second cluster around my own account and the recently created NodeXL twitter account.

In contrast, this is the map of all the people who mentioned the keyword “SharePoint” recently in Twitter.

This is clearly a much larger population and one with a dense core of highly connected individuals. A number of peripheral groups surround the core. Drilling in can reveal who the center of the center of the network is, a potentially highly influential person.

Video: Using NodeXL to map the “digg” mentioning Twitter population

11NovMay 7, 2015 By Marc Smith

This is a brief demo video about NodeXL analyzing Twitter social network connections among a group of users who all mentioned the term “digg”.

2009 – November – NodeXL – Demo – Mapping Twitter Social Networks “Digg” from Marc Smith on Vimeo.

This is a good demonstration of several features in NodeXL: social media network importers (in this case from Twitter), the use of a variety of layouts, auto-fill column mappings, and dynamic filters to reveal some important structures, groups and people in the graph.

Here is the source data in a NodeXL workbook: 2009 – November – NodeXL – Twitter Digg 3

Note! Excel has issues with security and workbooks. The easiest way to use that file is to open a blank NodeXL template and use the “Import from older workbook ” feature to pull the data into a workbook that your copy of Excel *can* trust.

Paper (using NodeXL!): Tweet the Debates: Understanding Community Annotation of Uncollected Sources

25SepMay 7, 2015 By Marc Smith

A recent paper makes use of NodeXL to create illustrations of data from connections among twitter users drawn from the United States presidential debates in October 2008. One illustration highlights the major clusters in the network.

Tweet the Debates: Understanding Community Annotation of Uncollected Sources

Authors: Shamma, D.A.; Kennedy, L.; Churchill, E.F.
Source: ACM Multimedia, ACM, Beijing, China (2009)
URL: http://www.acmmm09.org/
Keywords: Twitter; Debates; TV; community; multimedia; social; centrality; network

Abstract:
We investigate the practice of sharing short messages (microblogging) around live media events. Our focus is on Twitter and its usage during the 2008 Presidential Debates. We find that analysis of Twitter usage patterns around this media event can yield significant insights into the semantic structure and content of the media object. Specifically, we find that the level of Twitter activity serves as a predictor of changes in topics in the media event. Further we find that conversational cues can identify the key players in the media object and that the content of the Twitter posts can somewhat reflect the topics of discussion in the media object, but are mostly evaluative, in that they express the poster’s reaction to the media. The key contribution of this work is an analysis of the practice of microblogging live events and the core metrics that can leveraged to evaluate and analyze this activity. Finally, we offer suggestions on how our model of segmentation and node identification could apply towards any live, real-time arbitrary event.

Download: wsm01a-shamma.pdf

At the core of the network are the entities that have the most “eigenvector centrality” which turn out to be the major participants in the debate.

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