The theme of the event is “How to Feed Consumers with a #Digital @ppetite”
I will speak about the ways that restaurants and dining experiences are discussed in social media. I will show network maps that visualize the relationships among people who talk about restaurants created with the free and open NodeXL social media network analysis and visualization application.
Here are some recent NodeXL social media network maps for mentions of major chain restaurants featured in the NodeXL Graph Gallery: DunkinDonuts
McDonalds
Chipotle
@olivegarden
These maps illustrate the shape of the crowd that gathers around the names of major chain restaurants. A few Twitter user accounts occupy key positions in these network crowds, these are the influential voices that are repeated widely by others.
Closer inspection (click through for details) reveals smaller groups or clusters which form as a smaller set of people interact with one another more than with the larger population. These groups have distinct topics of interest which are summarized in the content report associated with each visualization.
The network and content report can reveal the topics of interest to various groups in the discussion as well as the key people within each group.
WELCOME AND INTRODUCTIONS- DR MARGARET SIMONS AND CARLTON CONNECT
9.45am-11
Presentations and Q and A from journalists: Marc Moncrieff and Craig Butt – Fairfax Media; Lisa Cornish – Red Cross (formerly News Corp); Harrison Polites – Business Spectator.
11-11.30
MORNING TEA
11.30-12.30
Presentations by Journalists (continued): Ed Tadros – Australian Financial Review; Matt Liddy, ABC; Nick Evershed – The Guardian in Australia.
12.30-1PM
ROUNDTABLE DISCUSSION AND IDENTIFICATION OF COMMON THEMES AND CHALLENGES
1PM-2PM
LUNCH
2PM-2.30pm
AURIN – Exploring the potential – Presentation by Professor Richard Sinnott, University of Melbourne.
Panel Session – Big Data. What Next? With Craig Thomler (Delib), Professor Paul Jensen (Faculty of Business and Economics, University of Melbourne); Jodie McVernon, (School of Population and Global Health, University of Melbourne), Scott Ewing, (World Internet Project, Swinburne Institute for Social Research.)
There will be a 3 hour session introducing NodeXL on Tuesday from 2-5pm 30th September at the main Parkville campus of UniMelb. The event is open to the public and is free.
It will be in the Old Arts Building Lecture Theatre B.
The main session will run from 2-4pm and there will be an additional hour for those that want to stop on for further training, finishing at 5pm
If you want to use NodeXL in the session, you will need a Windows laptop, and the Windows version of Excel (2007/2010/2013).
NodeXL has new updates to its importers for Twitter users and lists.
We have released an updated version of NodeXL that simplifies and merges the previously separate User and List importers.
The new, streamlined importer treats an individual user as a list of one.
Lists can be defined by pointing to an existing Twitter List or simply entering a list of delimited user names into the text box.
The updated importer now collects many more tweets per person and parses these messages to generate reply and mention edges.
You can now define a group of Twitter users and find out how much they reply and mention one another.
You can even pull in the followers of each person, to see if they reply or mention people they also follow.
But ever since June 11, 2013, Twitter has made access to the “follows” edge data very difficult (its just very slow). Designed and implemented prior to the update that restricted access to the follower network, the original NodeXL Twitter list importers relied mostly on queries that are now impractically slow for all but the smallest lists of users who have small collections of followers.
The update to these User and List importer is partially an adaptation to these changes. The importer shifts away from the follower network to focus on the communication interaction data in the content of Tweets. Since Twitter offers more generous access to Tweets than to information about who follows who, we are obliged to make better use of what they do offer.
The results are insightful! Here is a map of the connections among the members of the United States Congress.
I participated in a webinar hosted by the Prospect Research Institute. We discussed the ways that NodeXL can simplify the task of collecting social media and social network data. The tool generates easy to understand reports that highlight insights into connected structures.
The slides associated with the talk can be found here:
The NodeXL project from the Social Media Research Foundation has crossed many boundaries, notably bridging the divide between the social sciences and the computer sciences.
The Social Media Research Foundation team has innovated at multiple levels: organizationally we are a modern, virtual, distributed group of collaborators. Technically, we have focused our project on ease of use and automation rather than scale and sophistication, our users are not programmers. We have implemented many innovative network analysis and visualization techniques because we have been driven by a need to serve a diverse user population. The contributors to the project are themselves from a diverse range of disciplinary backgrounds, making it easier to shape the tool for the broadest audience.
Lee Rainie, director of the Pew Internet Research Center was interviewed by Bob Garfield on OnTheMedia this week about the recently released report on mapping Twitter topic networks. The report found six distinct patterns of social media networks in Twitter: divided, unified, fragmented, clustered, and in and out hub and spoke patterns. They discuss the prospects for overcoming polarization in social media and the hopeful signs that many other forms of social network structures exist in addition to the divided network pattern.
The talk will focus on the easy to follow steps needed to create social media network maps and reports automatically from services like Twitter, Facebook, YouTube, Flickr, email, blogs, wikis, and the WWW. Here is a sample network map of the term #bigdataprivacy:
The graph represents a network of 248 Twitter users whose recent tweets contained “#bigdataprivacy”, or who were replied to or mentioned in those tweets. The tweets in the network were tweeted over the 6-day, 10-hour, 29-minute period from Tuesday, 25 February 2014 at 14:36 UTC to Tuesday, 04 March 2014 at 01:06 UTC. There is an edge for each “replies-to” relationship in a tweet. There is an edge for each “mentions” relationship in a tweet. There is a self-loop edge for each tweet that is not a “replies-to” or “mentions”.
The graph’s vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm.
The graph was laid out using the Harel-Koren Fast Multiscale layout algorithm.
The edge colors are based on edge weight values. The edge widths are based on edge weight values. The edge opacities are based on edge weight values. The vertex sizes are based on followers values. The vertex opacities are based on followers values.
Top 10 Vertices, Ranked by Betweenness Centrality:
@whitehouseostp, @mit, @mit_csail, @steve_lockstep, @aureliepols, @dbarthjones, @digiphile, @stannenb, @djweitzner, @mikaelf
Coverage of our report on the six basic types of social media network structures created with the Pew Internet Research Center has been extensive. Here is a round up of the articles we have found about the study.
Network analysis is a way of looking at the world that focuses on the shape and structure of collections of relationships.
In a network perspective the world is not primarily composed of individuals (“nodes”, “vertices”, “entities”). Instead, a network approach focuses on relationships between individuals (“edges”, “ties”, “connections”, “links”).
When collections of connections are analyzed, network patterns emerge. Networks have a variety of shapes and within them people occupy a variety of locations within each network. Some people are highly connected, while most people have just a few connections, for example.
Network theory provides a big collection of math that enables the measurement of these shapes and structures.
Using these measures, network analysis can identify key people in important locations in the network (for example: hubs, bridges, and islands). Network metrics allow for the network as a whole to be measured in terms of size and shape. Networks have many basic shapes and we have found six shapes to be common in internet and enterprise social media: divided, unified, fragmented, clustered, outward hub and spoke, inward hub and spoke. These shapes are created when people make individual decisions about who to reply to, link to, and like.
Divided networks are created when two groups of people talk about a controversial topic – but do not connect to people in the “other” group. Unified networks are formed by small to medium sized groups that are obscure or professional topics, conference hashtags are a good example. Fragmented networks have few connections among the people in them: these are often people talking about a brand or popular topic or event. Clusters sometimes grow among the people talking about a brand, indicating a existence of a brand “community”. Broadcast networks are formed when a prominent media person is widely repeated by many audience members, forming a hub-and-spoke pattern with the spokes pointed inward at the hub. The final pattern is the opposite, hub-and-spoke patterns with the hub linking out to a number of spokes. This pattern is generated by technical and customer support accounts like those for computer and airline companies. Additional patterns may exist, but these patterns are prominent in many social media network data sets.
When applied to external conversations, social media networks help identify the “mayor” of a hashtag or topic: these are the people at the center of the network. Network maps can be compared to the six basic types of networks to understand the nature of the topic community. We can look for examples of successful social media efforts and map those topic networks. Social media managers can contrast their topics with those of their aspirational targets and measure the difference between where they are and where they want to be.
When applied to enterprise conversations and connections, network analysis can reveal the experts who answer many people’s questions and “brokers” who bridge otherwise disconnected groups as well as the “structural holes” that show where a bridge or link is needed.
These insights can be useful in mergers, HR evaluation of group and manager performance, and identifying internal subject matter experts.
Research performed using NodeXL shows that work teams that have higher levels of internal connection (which is called “network density”) have higher levels of performance and profit. See:
The impact of intragroup social network topology on group performance: understanding intra-organizational knowledge transfer through a social capital framework Wise, Sean Evan (2013) The impact of intragroup social network topology on group performance: understanding intra-organizational knowledge transfer through a social capital framework. PhD thesis, University of Glasgow.
Full text available as: PDF Download (2499Kb) | Preview http://theses.gla.ac.uk/3793/
I will speak about social media networks on October 24th, 2013 at the department of Computer Science at the Arizona State University.
The graph represents a network of 712 Twitter users whose recent tweets contained “@ASU”, taken from a data set limited to a maximum of 10,000 tweets. The network was obtained from Twitter on Sunday, 13 October 2013 at 19:56 UTC.
The tweets in the network were tweeted over the 4-day, 21-hour, 47-minute period from Tuesday, 08 October 2013 at 21:48 UTC to Sunday, 13 October 2013 at 19:35 UTC.
There is an edge for each “replies-to” relationship in a tweet. There is an edge for each “mentions” relationship in a tweet. There is a self-loop edge for each tweet that is not a “replies-to” or “mentions”.
The graph is directed.
The graph’s vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm.
The graph was laid out using the Harel-Koren Fast Multiscale layout algorithm.
The edge colors are based on edge weight values. The edge widths are based on edge weight values. The edge opacities are based on edge weight values. The vertex sizes are based on followers values. The vertex opacities are based on followers values
