Fourth International AAAI Conference on Weblogs and Social Media (ICWSM-10)

***Microblogging 2***

Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment (Tumasjan et al.)

Successful use of social media in las presidential campaign has established twitter as an integral part of political campaign toolbox

Goal: analyze on Twitter: 1. Deliberation, 2. Sentiment, 3. Prediction

Previous work:

Deliberation: Honeycutt and Herring – Twitter not only used for one-way comm, but 31% of all tweets direct a specific addressee. Kroop and Jansen – political internet discussion boards dominated by small # of heavy users

Sentiment: How accurately can Twitter inform us about the electorate’s political sentiment?

Prediction: can Twitter serve as a predictor of the election result?

Data: examined more than 100k tweets and extracted their sentiment using LIWC

Target: German federal election 2009

Results:

1. While Twitter is used as a forum for political deliberation on substantive issues, this forum is dominated by heavy users

Two widely accepted indicators of blog-based deliberation:

-The exchange of substantive issues (31% of all messages contain “@”),

-Equality of participaion: While the distribution of users across groups is almost identical with the one found on internet message boards, we find even less equality of participation for the political debate on Twitter. Additional analyses have shown users to exhibit a party-bias in the volume and sentiment of messages.

2. The online sentiment in tweets reflects nuanced offline differences between the politicians in our sample.

LIWC profiles:

-Leading candidates: Very similar profile for all leading candidates, only polarizing political characters, such as liberal leader and socialist, deviate in line with their roles as opposition leaders. Messages mentioning Steinmeir (coalition leader) are most tentative

3. Similarity of profiles is a plausible reflection of the political proximity between the parties

Key findings: high convergence of leading candidates, more divergence among politicians of governin grand coalition than among those of a potential right wing coalition

4. Activity on Twitter prior to election seems to validly reflect the election outcome (MAE 1.65%), and joint party mentions accurately reflect the political ties between parties.

From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series (Brendan O’Connor)

Measuring public opinion through social media

Old method – query via dialing, asking, etc.

New method – people write their thoughts to social media, query social media to create aggregate text sentiment measure.

Can compare results from new method to old method

Contributions:

-High correlations between very simple sentiment analysis and telephone polls

-Time series smoothing helps

Text Data: Twitter

-Large, public, ll in one place

-Sources: Archiving Twitter Streaming API (“Gardenhose”/”Sample” ~15% public tweets); Scrape earlier messages via API

-Volume ~ .7B tweets

-Poll data: consumer confidence (2008-2009) – index of consumer sentiment (Reuters/Michigan), Gallup daily. 2008 presidential elections (aggregation, pollster.com). 2009 presidential job approval (Gallup daily)

-Message selection via topic keywords

-topic frequencies change rapidly

-Sentiment analysis: word counting.

–Subjectivity Clues lexicon from OpinionFinder / U Pitt (Very simple system!)

Key: don’t need to classify individual messages correctly, just need a sentiment ratio over messages.

-Sentiment Ratio Moving Average: High day-to-day volatility. Average last k days.

-Which leads, poll or text?

–Cross-correlation analysis: between sentiment score for day t, poll for day t+L.

—Results: “jobs” text leading indicator for poll, can be turned into forecasting model

—Reminiscent of Leskovec et al. Blogpulse paper, very nice!

-Keyword message selection:

–15-day windows, no lag. “jobs” r=80%, “job” r=7%. Is stemming always good?

Presidential elections and job approval: sentiment doesn’t correlate, but pure volume does (79% for “obama” 74% for “mccain”)

Conclusions:

-Preliminary results that sentiment analysis on Twitter data can give information similar to traditional opinion polls. But, still not well-understood. Twitter bias? News vs. opinion?

-Issues: Relevant message selection, Time series smoothing

-Replacement for polls? Promising but not quite yet

Information Contagion: an Empirical Study of the Spread of News on Digg and Twitter Social Networks (Lerman et al.)

Information flow on networks

Dynamics of Social Information

-How does infromation spread on online social networks?

–How far and how fast does information flow on networks?

–What factors influence its spread?

–How does network structure affect dynamics of information flow?

–What does this tell us about quality of information?

-Study question through comparative empirical analysis of 2 social news networks – using URLs as markers

Social News: Digg, Twitter + Tweetmeme

-Tweetmeme aggregates all tweets and features most retweeted URLs on its front page

Data Scope:

-3.5K digg stories with time submitted, promoted, votes for each story (time of vote, name of voter). 140k active users who voted for at least one stroy, 71k of them following at least one user. 258k links = fan network

-398 most retweeted stories 6/11/09 – 7/3/09, extracted from tweetmeme. Retweets of each story, up to 1k most recent retweets. Follower network of users who retweeted the stories

Questions:

-Usability of social netws – do people use digg, twitter the same way? what effect do differences in user interface have?

-dynamics of social networks – how far does info spread, how fast does it spread, and what are the effects of net strucutre?

Basic terms:

-Submitter  = user who submitted link to story, or user who tweeted link to a story

-Vote = vote on Digg or retweet on Twitter

-Fan = fan on Digg or follower on Twitter

User activity: distribution of fans (Power law on Digg with up to 1e5, power law with bump ~ 10 on Twitter with up to 1e7 users)

User activity: distribution of voting: Power law on Digg and Twitter (with different slopes)

Dynamics of stories: both digg and twitter show exponential growth, but for Digg it is preceded by slow period before story is on front page, both show vote saturation

Popularity distribution of stories shows lognormal fit

Information flow on networks: information spreads on a network as fans (followers) vote for (retweet) stories their friends submit or vote for.

Dynamics of information spread on networks looks very similar to overall dynamics of information spread (evolution of fan votes qualitatively similar to evolution of all votes)

BUT distribution of popularity is different, now shows normal fit. “Inequality of popularity” no longer observed (social influence accounted for?). News spreads farther on Twitter than on Digg.

How far does information spread among submitter’s fans?

-On digg many stories get voted by submitter’s fans, opposite case on Twitter

How fast does info spread on networks?

-Two distinct phases on digg: stories spread faster through network before promotion than afterwards.

-On Twitter, info spreads at constant rate.

Network structure differences: Digg network is denser, more inter-connected than Twitter’s

Summary of results:

-Network structure and info flow

–Digg’s network is denser than Twitter’s: News spreads faster initially through Digg’s network, but it does not spread as far as on Twitter

–Twitter’s network is sparse: Fans unconnected to submitter help spread story

-User interface and information flow:

–Before promotion, Digg stories spread mainly through network (and do so faster)

–No equivalent of promotion on Twitter

Tweeting from the Town Square: measuring Geographic Local Networks (Yardi and boyd)

Two geographically bounded events: Wichita shooting and Altanta parking garage collapse

Methods: two crawls and a poll

RQ1: Do geographically local topics have more dense Twitter networks than non-local topics?

Why this is important? People living in close geo proximity may share characteristics. Connecting similar people can help them form ties, foster community

Spread of News

Spread of News Online – ongoing discussion vs. spikes of short-term high-density discussions around real-world events

Distance

Methods: searched key terms about each evenet, stored user info, crawled first degree net of users. Polled users who had tweeted twice or more about church shooting in first 24 hours after it was announced. Administered poll 3-5 days after event. Sent out 800 requests, received 164 responses.

RQ2: Are people who are central in twitter network more geographically central in physical world?

Sarita Yardi gives shout-out to NodeXL, asks for more scale!

RQ3: What sources do people go to for local news events?

Twitter maps show high level of locality to event, slow spread outward

News Sources – go to locals

News Seekers – also go to locals, then to MSM

Practical applications:

-Utilize local short paths for disseminating information. Schools have long used an “emergency phone tree” with specified # of branches and leaves

-Timely notification of unexpected events

Invited Panel: US Government and Social Media

Macon Phillips, Director of New Media for the Obama White House

Moving from Elections to Governance

Wants academics to build tools that show effect of using social media on user behavior

WH new media director Macon Phillips asks for tools that allow thousands of people to communicate with the President (thanks @sadatshami !)

Don Burke, CIA Directorate of Science and Technology, Intellipedia Project

Haym Hirsh, Director, Division of Information and Intelligent Systems

Social Media and the Federal Government

NSF

US Gov’t early crowdsourcing project – National Weather Service Cooperative Observer Program (1890)

-Experimentation:

–CIA Intellipedia

–NASA Clickworkers

–PeerToPatent

–DARPA Balloon Challenge

–EPA Greenversations

–Over 100 gov’t blogs

-Policy implications and clarifications

–70% of Airmen use YouTube

Challenges:

-Legal and Policy

–Terms of Service: Indemnification, etc.

–Advertising (e.g. alongside gov’t content)

–Procurement: Free = Gift? No competition? Charges imposed after lock-in

Additional Challenges:

-Colbert “attacks”

-Open Government Dialogue

The Open Dialogue Top 5:

1. Concerns about Obama’s Birth Certificate

2. Government spending

3. Marijuana

4. Marijuana

5. Birth Certificate

Additional Opportunities: “No matter who you are, most of the smartest people work for someone else.”

Implications:

-Foster experimentation and innovation w/in federal government

-Provide data for innovation outside the def

-align legal and policy with aspirations

-research

Question about contribution quality: do people feel their contributions are worthwhile? How do we make the value and implications of contribution clear?

What do “votes” for questions mean? Who is the right person to say that legalization of marijuana is not a big question? What questions are “big enough to matter”? The “pothole problem” – should questions about fixing potholes be crowdsourced?

Few poorly worded questions about marijuana, people will speak eloquently and argue for the issue, so it’s not just spam

Don Burke – not EVERY system has to be based on socialmedia

Questions: How do you get recognized by gov’t? Answers: open access, publishing where you’ll be noticed

Question about Intellipedia and procedures for aggregating information. Answer: without the wiki, there was no way to share tacit knowledge. But want to go beyond wiki and to the larger web

Jure Leskovec about developing APIs for gov’t data. Answer: no APIs yet, but government is collecting data in one place that’s publicly visible. Want to see scientific community analyzing datasets and finding results, government may not necessarily know what’s a “good” dataset.

***Analysis of Social Network Usage***

Governance in Social Media: A Case Study of the Wikipedia Promotion Process (Leskovec et al.)

Wikipedia promotion process

3 important features:

-deliberative process yielding a single decision

-is publicly recorder

-consequential for the community

Similarity to offline world: people evaluate other people

We study perspective of voters:

-Burke & Kraut examine candidate’s perspective

-How voters evaluate candidate?

-How do evaluations change over time?

Main findings: Relative assessment

-Voter’s evaluation of the candidate reflects different types of relative assessment

–Let voter V vote on candidate C

–we find that vote of V heavily depends on relationship and relative merit of V and C:

—past interaction

—Number of edits

—Number of “barnstars”

–Response function of vote V:

—Prob. V votes given that x other people have voted

Dataset: Wikipedia voting

-Votes are time stamped and signed by users

–2.8k elections sept ’04 – Jan ’08. 44.6% success rate: Successful: 94.7% support. Failed: 31% support votes

–114K votes (78% support). Each vote can get commented: Support votes: 7% get discussed. Oppose votes: 82% get discussed

User characteristcs

-8.3K users voted

–7.5K voters

–2.5k candidates (some go for promotion multiple times)

-Relative merit:

–How do properties of voter V and candidate C affect V’s vote?

–Two natural (but competing) hypotheses:

H1. Prob. that C receives positive vote depends primarily on characteristics of C, there are objective criteria for user to become admin

H2. Prob. that C receives positive vote depends on relationship between characteristics of C and V

Merit (level of contribution):

-Two ways to quantify merit: total #edits, total #barnstars

-Relative merit: How does prob of V voting positively depend on diff in merit of C and V?

Relative merit hypothesis: if V has higher merit than C then he is less likely to vote

Observations: V is especially unlikely to vote for candidates of the same merit (total edits or barnstars)

Direct V-C interaction: Prob of positive vote as function of prior interactions of V and C.

Observation = prior interaction increases probability of a positive vote (with diminishing returns)

Thresholds and diversity of voters:

-Aggregate response function:

–How does prob. of voting positively depend on frac. of positive votes so far?

-Aggregate response function: baseline: if voter were to flip a coin then f(x)=x

-Observation: voters more inclined to express opinion when it goes against prevailing opinion

-Personal response functions: How does prob. of voter V voting positively depend on frac. of positive votes so far?

-Enough data that we can build models of individuals

-Average is close to baseline but individual variation in shape of response function is large

-Over time voters become more conservative, response functions shift downward and to the left

Elections over time:

–Elections unfold over time: Sequence of pairs (s(t),o(t))

—Very negative elections end ealry

—Failed elections are “top-heavy” = start very positive and slowly get negative.

—Successful elections get more positive over time

—Order of early votes doesn’t matter

–False hypotheses: Candidate’s friends vote early, Herding behavior (excessive influence of first votes)

Activity Lifespan: an Analysis of User Survival Patterns in Online Knowledge Sharing Communities (Yang et al.)

-User survival analysis to show that participation patterns and performance factors can account for a considerable amount of variance in predicting user lifespan

-Compare 3 major Q&A sites: Yahoo! Answers, Baidu Knows, and Naver Knowledge-iN

-Discuss how systems might sustain users

-Characteristics of Q&A sites we studied: in Yahoo Answers, earn points at flat rate per answer / best answer, Pay flat rate in points. In Baidu and Naver, earn points at flat rate per answer + points per best answer, and asker can offer additional points

-In Yahoo, significantly more questions / answer

Method: survival analysis

Defining “death” in online communities: period of inactivity exceeding 100 days. Found model prediction not sensitive to different cutoffs (50-150 days)

General comparison: 30-70% users leave after first day, afterwards curves for all 3 sites flatten. YA users more likely to remain than users of other two sites.

Answering life on average longer than asking life across all sites.

Preference between answering and asking (A/R ratio) can account for considerable amount of variance in predicting user lifespan

Initial interaction:

Obtaining more answers to your first question, writing longer question correlated with longer lifespan on Yahoo and Baidu

Winning best answer also correlated with longer lifespan

First 30 days:

More activity, asking more questions, obtaining more answers per question positively correlated with lifespan on all 3 sites

A/R ratio negatively correlated with lifespan on Yahoo but positively correlated with lifespan on Baidu and Naivr

Winning (best answer) also positively correlated with lifespan on all three sites

Analysis: community evolution

All three sites presented a decline in survival rate from year 1 to year 2, especially for Yahoo Answers

Naivr suffered more difficulty in sustaining users in 2nd year as almost no users stayed after 250 days

Conversational vs. Informational: There is a significant and consistence difference in survival patterns between conversational categories and informational categories: more conversational categories survive for longer

*with the exception* of “computer/internet” on Baidu only (cultural difference?)

Analysis: why do YA users stay longer

by chris.corwin

– - 

We have several data import providers (spigots) in NodeXL that query popular sources of social media for information that can be processed into a network graph.  User and search term networks from Twitter, YouTube, and flickr have been implemented for a while along with a connector to email reply networks through the Windows Search Index.  NodeXL also imports data from several popular network analysis file formats, opening up data sets and sample libraries used in many courses.

Recently, we added a 3rd party provider for web hyperlink networks through the VOSON service from Prof. Robert Ackland at the Australian National University.

We plan a number of additional data import spigots and would like to know about additional sources of network data structures that would be of interest.

The following data sources are possible targets for a new data provider.  Which matter most to you? SharePoint? Exchange? Active Directory? LinkedIn? Media Wikis?

NodeXL has had a rudimentary flickr tag network data spigot for some time but we have just added a number of features to this data importer that makes it much more useful.

You can now select the number of network levels to include, an optional sample image file can be included for each tag, and the dialog now provides feedback as it requests the various parts of the network from Flickr.

The tag network generates maps like the following set of connections among terms related to “sociology”:

What social media data most interest you?  We are considering integration with web and wiki crawlers, and support for YouTube, delicious, and enterprise data sources like Active Directory (LDAP), SharePoint, and Exchange.

This release also improves support for images, particularly those pulled from URLS, like twitter or facebook profile photos!

Here, for example, is a map of the connections among twitter accounts that tweeted the “WIN09″ tag that was used in the recent Social Networks Summit at NYU (http://winworkshop.net/) The map illustrates the way the summit brought together previously separate clusters of people from the various disciplines that have been attracted to the study of networks in general and social networks in particular.  Size of the image equals the number of tweets that person created.

A refined version adds Edge Labels and color to highlight the different tie types in the graph: “follows” relationships and “replies to” and “mentions” and now scaled by “Followers”.

In both views, the high betweenness role of one twitter account is clear.

Release details below the fold….

1.0.1.95 (2009/09/28)

• When an email network is analyzed (NodeXL, Data, Import, From Email Network), the resulting graph is now directed. This means that the relationships (John,Mary) and (Mary,John) are no longer combined into a single edge with an edge weight of 2; instead, they are considered unique edges.

1.0.1.94 (2009/09/28)

• When importing a Twitter search network (NodeXL, Data, Import, From Twitter Search Network), you can now add a Tweet column to the Vertices worksheet.
• When importing a Twitter user network (NodeXL, Data, Import, From Twitter User Network), you now have some options for how edges get added to the graph. You can add an edge for each followed/following relationship (which was always done in previous versions), each “replies-to” relationship in the people’s latest tweets, and each “mentions” relationship in the latest tweets.
• Ditto for the Twitter search network, although in this case the “replies-to” and “mentions” relationships apply to the tweets that satisfied the search criteria, which aren’t necessarily the people’s latest tweets.
• Twitter search networks and Twitter user networks now add an Image URL column to the Vertices worksheet. The images are those of the people who wrote the tweets.
• Bug fix: In the Import from Twitter Search Network dialog box, the word “latest” was removed from the text, “Search for people whose latest tweets contain…” Reason: The tweets returned by the search aren’t necessarily the people’s latest tweets.
• Bug fix: When importing a Twitter search network, a tweet posted by a person with “protected” status in Twitter would bring the search to a halt with a “There is no Twitter user with that screen name” error. Now, such people are skipped.
• Bug fix: Using any of the NodeXL, Data, Import items in the ribbon failed to clear the Images worksheet before importing the new data.

1.0.1.93 (2009/9/18)

• You can now import a Twitter network of users who have tweeted a specified term. For example, you can create a graph with a vertex for each person who has included the hashtag “#chi2010″ in a tweet, with an edge between the people who follow each other. In the Excel Ribbon, go to NodeXL, Data, Import, From Twitter Search Network.
• When including an image in a graph, you can now specify an URL to an image on the Internet.
• When an image file isn’t available, an error message is no longer displayed. Instead, a small red X is shown in the graph in place of the missing image.
• Images can now be resized using the Size column on the Vertices worksheet. There are new options in the Options dialog box for setting the default image size. (Known bug: Changing the default image size doesn’t update the graph pane until the workbook is refreshed.)
• When importing from a Twitter user’s network (NodeXL, Data, Import, From Twitter User’s Network), a Relationship column is added to the Edges worksheet. This gets set to Followed or Follower.
• The Twitter dialog boxes now provide feedback on what they’re doing as they assemble the requested network.
• Bug fix: Attempting to get a Twitter user network that included someone who “protected” her Twitter identity would cause a failure. Now, that user is skipped and the rest of the network is obtained.
• Bug fix: There was a rounding error with very small numbers (on the order of 1.0E-22) that could cause some vertices or edges to always be filtered out by Dynamic Filters, even if the filters were reset to their entire range.

1.0.1.92 (2009/9/4)

• In the Autofill Columns dialog box (NodeXL, Visual Properties, Autofill Columns), you can now specify a logarithmic mapping instead of a linear mapping when autofilling Edge Color, Edge Width, Edge Opacity, Vertex Color, Vertex Size, Vertex Opacity, Vertex Primary Label Fill Color, Vertex Layout Order, Vertex X, Vertex Y, Vertex R, or Vertex Polar Angle. Click one of the Options buttons, then check “use a logarithmic mapping.”
• You can now import a network of Flickr tags related to a specified tag. Use NodeXL, Import, From Network of Related Flickr Tags. You will need what Flickr calls an “API key.” There is a link in the Import dialog box for requesting a key from Flickr.
• The Twitter import feature has been expanded. (It’s at NodeXL, Import, From Twitter User’s Network.) You can now import the network of people followed by a user, people following a user, or both. There is a new option for selecting a 1, 1.5, or 2-level network, and you can limit the network to a specified number of people. New columns are added to the Vertices worksheet: Followed, Followers, Tweets, and (optionally) Latest Tweet. Also, you can right-click a vertex in the graph pane and select the new Open Twitter Page for This Person menu item.
• Bug fix: When using dynamic filters, a filtered edge’s label obscured what was under it even though the edge itself was hidden.
• For programmers only: The IGraphDataProvider interface used by data provider plug-ins for the Excel Template has changed. The GetGraphData() method is now called TryGetGraphData() and it now returns a Boolean. (The original design failed to accommodate failures while getting graph data.)

1.0.1.91 (2009/8/19)

• You can now label edges by filling in a new Label column on the Edges worksheet. The Label column is hidden by default. To make it visible, use NodeXL, Show/Hide, Workbook Columns, Labels in the Excel Ribbon.
• You can now import a graph from a GraphML file. GraphML is an XML-based file format used by a variety of graph applications and libraries, including Pajek, “R,” and JUNG. NodeXL will import all edge and vertex attributes in a GraphML file, including those that correspond to standard NodeXL columns such as Edge Color and Vertex Size. Use NodeXL, Data, Import, From GraphML File.
• Importing from a Twitter network (NodeXL, Data, Import, From Twitter Network) is now more reliable, thanks to automatic retries that will occur if a request to Twitter fails.
• Of possible interest to developers: NodeXL now supports custom “plug-in” .NET assemblies that will import graphs from custom data sources. For details, see the IGraphDataProvider Interface topic in the NodeXLApi.chm help file and the sample implementation in SampleGraphDataProvider.cs.
• The graph legend is now more compact. (To show the legend, use NodeXL, Show/Hide, Graph Elements, Legend.)
• Bug fix: When importing from an open edge workbook (NodeXL, Data, Import, From Open Edge Workbook), columns in the open edge workbook that have the same name as standard NodeXL columns will be copied to those NodeXL columns. Before, a column named “Color” was copied to “Color 2,” for example.
• Bug fix: Your settings, such as those entered in the Options dialog box, are now stored in a single file in your local Windows profile. Before, each named NodeXL workbook got its own settings file.

1.0.1.90 (2009/7/24)

• When you import data into the workbook (NodeXL, Data, Import), you now have the option to append the imported data to the workbook’s contents instead of clearing the workbook first. Check or uncheck the NodeXL, Data, Import, Clear NodeXL Workbook First checkbox to control this.
• The Closeness Centrality graph metric (NodeXL, Analysis, Graph Metrics) is now computed much more quickly. For example, with a graph containing about 1,000 vertices and 1,000 edges, the computation time went from 31 seconds to 3 seconds, and with a larger graph containing 5,000 vertices and 8,000 edges, the time went from 63 minutes to 2 minutes.
• The graph legend is now hidden by default.
• Your settings for showing or hiding the graph legend and axes (NodeXL, Show/Hide, Graph Elements) are now saved along with the rest of your settings.
• If you autofill the X and Y columns in the Vertices worksheet (NodeXL, Visual Properties, Autofill Columns), the Locked column is no longer automatically set to Yes. Instead, the Layout (NodeXL, Graph, Layout) is set to None, which achieves the same effect but is easier to undo. If you no longer want the autofilled X and Y values, just set the Layout to something else.
• In the options dialog boxes within the Autofill Columns dialog box, there is now a “Swap” button that will quickly swap the colors or numbers you are autofilling.
• You can now change the font used for the graph axes. In the graph pane, go to Options, Axis Font.
• The Auto Refresh checkbox that used to be in the NodeXL, Visual Properties Ribbon group is now in the Options dialog box, reachable from the graph pane.
• Bug fix: In the Import From Twitter Network feature, the screen name and password were not being correctly sent to Twitter. This caused Twitter rate limiting to kick in even if your rate limit had been lifted by Twitter.

1.0.1.89 (2009/7/9)

• There are now X and Y axes in the graph pane. To show them, check the NodeXL, Show/Hide, Graph Elements, Axes checkbox in the Excel Ribbon. If you autofill the X and Y columns in the Vertices worksheet (NodeXL, Visual Properties, Autofill Columns), the axes will show the range of autofilled values. Otherwise, the axes simply show NodeXL’s full range of coordinate values (0 to 9,999).
• You can now export the NodeXL workbook’s edges to a UCINET file. The file format is what UCINET calls “full matrix DL.” Go to NodeXL, Data, Export, To UCINET Full Matrix DL File in the Ribbon.
• You can also import a UCINET full matrix DL file. Go to NodeXL, Data, Import, From UCINET Full Matrix DL File in the Ribbon. If you have a file in a different UCINET format, you will need to use the UCINET program to convert it to full matrix DL. Click on the “What if my file is not in full matrix DL format?” link in the Import dialog box for instructions.
• You can now export the NodeXL workbook’s edges to a Pajek file. Go to NodeXL, Data, Export, To Pajek File in the Ribbon. The vertex coordinates are exported, but no other edge or vertex attributes are.