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graph

November 3 – 5, 2014 – Restaurant Executive Summit – Mapping restaurant networks in social media

25OctMay 7, 2015 By Marc Smith

2014-Restaurant Executive Summit Banner
2014-Restaurant Executive Summit Banner 2

The 2014 Restaurant Executive Summit will be held on November 3 – 5, 2014 at the Ritz-Carlton in Ft. Lauderdale, Florida.

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 Twitter NodeXL SNA Map and Report for Friday, 24 October 2014 at 18:36 UTC
DunkinDonuts

McDonalds Twitter NodeXL SNA Map and Report for Friday, 24 October 2014 at 17:45 UTC
McDonalds

Chipotle Twitter NodeXL SNA Map and Report for Friday, 24 October 2014 at 17:28 UTC
Chipotle

@olivegarden OR "Olive Garden" Twitter NodeXL SNA Map and Report for Friday, 24 October 2014 at 17:2
@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.

 

Posted in 2014, All posts, Conference, Data Mining, Foundation, Measuring social media, Metrics, NodeXL, Presentation, Research, SMRF, SNA, Social Interaction, Social Media, Social Media Research Foundation, Social network, Social Network Analysis, Social Theories and concepts, Sociology, Talks, Visualization Tagged 2014, Event, Ft. Lauderdale, graph, Map, network, NodeXL, Restaurant, SNA, Social Media, Summit, Talk

29-30 September at the University of Melbourne – Talk about social media networks

07SepMay 7, 2015 By Marc Smith

15217892619_a17e2d79ca_z

2014-UniMelb Logo    2014-Carlton Connect Initiative

2014-Center for Advancing Journalism

2014-AURIN

Best Practice in Data Journalism Workshop

PROGRAM
29-30 September 2014

Terrace Lounge, Level 1, Walter Boas Building, Parkville Campus

(Campus map at http://maps.unimelb.edu.au/parkville)

MONDAY 29 SEPTEMBER

9-9.30AM REGISTRATION AND WELCOME
9.30-9.45am 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.
2.30-3pm NodeXL – Exploring the potential – Presentation by Marc Smith, Director, Social Media Research Foundation
3-3.30pm AFTERNOON TEA
3.30PM-5PM 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).

You can download NodeXL in advance from: http://nodexl.codeplex.com/.

Map and Building: 

http://maps.unimelb.edu.au/parkville/building/149#.VCTinmS1Zlo

Download instructions:

 http://nodexl.codeplex.com/releases/view/117659

Posted in 2014, All posts, Collective Action, Common Goods, Community, Foundation, NodeXL, SMRF, SNA, Social Interaction, Social Media, Social Media Research Foundation, Social network, Social Network Analysis, Social Roles, Social Theories and concepts, Sociology, Talk, University, Visualization, Workshop Tagged Australia, Chart, graph, Map, Melbourne, network, NodeXL, SNA, Social Media, UniMelb, Universty

NEW: NodeXL updates Twitter user and list importer

06JunMay 7, 2015 By Marc Smith

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.

2014-NodeXL-Data-Import-From Twitter Users Network

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.

Posted in 2014, All posts, Network data providers (spigots), NodeXL, Social network Tagged 2014, Access, API, Collection, Connections, Data, Edge, Follower, Follows, graph, Importer, Information, Links, March, network, NodeXL, Ties, Tweets, Twitter, update, Upgrade 11 Comments

VIDEO: SNA for fundraising and development webinar hosted by the Prospect Research Institute

05JunMay 7, 2015 By Marc Smith

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:

2014 TheNextWeb-Mapping connections with NodeXL from Marc Smith

Analyzing Social Media Networks with NodeXL: Insights from a Connected World

Posted in 2014, All posts, Foundation, Measuring social media, Metrics, NodeXL, Presentation, Session, SMRF, SNA, Social Interaction, Social Media, Social Media Research Foundation, Social network, Social Network Analysis, Social Theories and concepts, Sociology, Talk, Talks, Training, Video, Visualization, Workshop Tagged Chart, graph, Influence, Map, network, NodeXL, Prospect Research, Prospect Research Institute, SNA, Structure, Video

May 15, 2014: Keynote at Sam and Irene Black School of Business at Penn State University

07MayMay 7, 2015 By Marc Smith

2014-Penn State logo

I will speak at the Sam and Irene Black School of Business at Penn State University on Thursday, May 15, 2014.

I will talk about the themes Thinking across Boundaries, Learning by Doing, and Innovating through Collaboration in the context of the work of the Social Media Research Foundation to deliver an end-user friendly, free and open tool for social media network analysis.

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.

We have learned a great deal as the NodeXL development team has released hundreds of updates to the application, guided by the feedback of our growing user community.

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.

 

Posted in 2014, All posts, Collective Action, Foundation, Measuring social media, NodeXL, Presentation, Research, SMRF, SNA, Social Interaction, Social Media, Social Media Research Foundation, Social Network Analysis, Social Theories and concepts, Sociology, Talks, Visualization Tagged 2014, Chart, graph, Lecture, Map, Marc Smith, May, network, NodeXL, Penn State, Presentation, SNA, Social Media, Talk, Visualization

Radio: OnTheMedia – Twitter Cartography – with Lee Rainie from Pew Internet

15MarMay 7, 2015 By Marc Smith

20140314-OnTheMedia-Twitter Cartography-Lee Rainie

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.

http://www.connectedaction.net/wp-content/uploads/2014/03/20140314-OnTheMedia-Twitter-Cartography-with-Lee-Rainie.mp3
Posted in 2014, All posts, Measuring social media, NodeXL, Pew Internet, Presentation, Research, SMRF, Social Interaction, Social Media, Social Media Research Foundation, Social Network Analysis, Social Theories and concepts, Sociology, Talk, Talks, Visualization Tagged 2014, Audio, Chart, graph, Image, Interview, Lee Rainie, Map, March, network, NPR, OnTheMedia, Pew, Pew Internet, Podcast, Radio, Research, SNA, Social Media, Twitter

April 1, 2014 – NodeXL SNA of social media talk at Federal Big Data Working Group

06MarMay 7, 2015 By Marc Smith

2014-Federal Big Data Working Group-Banner

I will present a talk about social media network at the April 1st Federal Big Data Working Group at 6:30pm.

Talk details are on the SemanticCommunity.info site.

The Federal Big Data Working Group supports the Federal Big Data Initiative and the Federal Digital Government Strategy.

See: http://www.meetup.com/Federal-Big-Data-Working-Group/

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

Top URLs in Tweet in Entire Graph:
http://web.mit.edu/bigdata-priv/webcast.html
http://www.commerce.gov/news/secretary-speeches/2014/03/03/us-secretary-commerce-penny-pritzker-delivers-remarks-mit
http://web.mit.edu/bigdata-priv/agenda.html
http://www.whitehouse.gov/blog/2014/02/24/privacy-workshop-explore-big-data-opportunities-challenges
http://www.nytimes.com/glogin?mobile=1&URI=http%3A%2F%2Fmobile.nytimes.com%2F2014%2F03%2F03%2Ftechnology%2Fwhen-start-ups-dont-lock-the-doors.html
http://www.techrepublic.com/article/privacy-concerns-about-data-collection-may-lead-to-dumbing-down-smart-devices/
http://m.technologyreview.com/news/525131/intel-designs-a-safe-meeting-place-for-private-data/
http://thedatamap.org
http://www.foreignaffairs.com/articles/140741/craig-mundie/privacy-pragmatism
http://www.cs.ucdavis.edu/~franklin/ecs289/2010/dwork_2008.pdf

Posted in 2014, All posts, Conference, Foundation, Measuring social media, Metrics, NodeXL, Pew Internet, Presentation, Research, Session, SMRF, SNA, Social Interaction, Social Media, Social Media Research Foundation, Social network, Social Network Analysis, Social Roles, Social Theories and concepts, Sociology, Talks, Visualization Tagged 2014, April, Big Data, Chart, Federal, graph, Map, Marc Smith, network, NodeXL, SNA, Social network, Visualization

Social media network maps and reports covered in the press

05MarMay 7, 2015 By Marc Smith

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.

20140314-OnTheMedia-Twitter Cartography-Lee Rainie

Lee Rainie, director of the Pew Internet Research Center was interviewed by Bob Garfield on OnTheMedia.
http://www.connectedaction.net/wp-content/uploads/2014/03/20140314-OnTheMedia-Twitter-Cartography-with-Lee-Rainie.mp3


20140220-WaPo-Pew-SMRF-6 Kinds of Twitter networks

Washington Post: The six types of conversations on Twitter


20140220-SFGate-Pew-SMRF-6 Kinds of Twitter networks

San Francisco Chronicle: The six ways we interact on Twitter


20140305-WVXU-Cincinnati-Pew-SMRF-6 Kinds of Twitter networks

http://www.connectedaction.net/wp-content/uploads/2014/03/20140305-WVXU-focus_PEW-SMRF-twitter_study-141.mp3

RADIO WVXU Cincinnati – Ann Thompson


20140228-MyFoxNY-Pew-SMRF-6 Kinds of Twitter networks

Fox News New York

 


20140220-AJAM-Pew-SMRF-6 Kinds of Twitter networks

Al Jazeera: Study maps Twitter’s information ecosystem

 


20140220-PBS Newshour Rundown-Pew-SMRF-6 Kinds of Twitter networks

PBS NewsHour: Study uncovers six basic types of Twitter conversations

 


20140220-DesMoines Register-Pew-SMRF-6 Kinds of Twitter networks

Des Moines Register: Twitter talk fits into 6 patterns, study finds

 


20140228-USAToday-Pew-SMRF-6 Kinds of Twitter networks

USAToday: Twitter talk fits into 6 patterns, study finds

 


20140220-NBCNews-Pew-SMRF-6 Kinds of Twitter networks

NBC: Liberals, Conservatives Tweet in Partisan Bubbles, Study Says

 


20140220-CNet-Pew-SMRF-6 Kinds of Twitter networks

CNET: Red state, blue state? On Twitter, never the twain shall meet

 


20140220-Time Entertainment-Pew-SMRF-6 Kinds of Twitter networks

TIME: Who Are TV’s Biggest Fans? New Research Names Twitter Users With the Most Influence

 


20140220-Quartz-Pew-SMRF-6 Kinds of Twitter networks

Quartz: Turns out Twitter is even more politically polarized than you thought

 


20140220-Forbes-Pew-SMRF-6 Kinds of Twitter networks

Forbes: These Charts Show Why Political Debate On Twitter Is Pointless

 


20140220-VatorNews-Pew-SMRF-6 Kinds of Twitter networks

Vator: Pew report: how we communicate on Twitter

 


20140220-GlobalNews CA-Pew-SMRF-6 Kinds of Twitter networks

Global News Canada: Study reveals six different types of conversations on Twitter

 


20140220-LiveScience-Pew-SMRF-6 Kinds of Twitter networks

Live Science: The 6 types of Twitter conversations revealed

 


20140220-SeattlePI-Pew-SMRF-6 Kinds of Twitter networks

Seattle PI: The six ways we interact on Twitter

 


20140220-AP-Pew-SMRF-6 Kinds of Twitter networks

Associated Press: Pew maps Twitter chatter in new type of study, finds 6 types of conversations

 


20140220-Redeye-Pew-SMRF-6 Kinds of Twitter networks

Chicago Tribune: The 5 cliques of Twitter

 


20140220-Mashable-Pew-SMRF-6 Kinds of Twitter networks

Mashable: Your Twitter Conversations Fall Into One of These Six Categories

 


20140220-PCMag-Pew-SMRF-6 Kinds of Twitter networks

PC Magazine: Which Type of Twitter Conversationalist Are You? In a recent report, Pew Researchers explain the six regularly observed types of conversation on Twitter

 


20140220-NPR-Pew-SMRF-6 Kinds of Twitter networks

NPR: Study: Conservatives And Liberals Rarely Debate On Twitter


20140220-DailyMail-Pew-SMRF-6 Kinds of Twitter networks

Daily Mail: What type of tweeter are you? Researchers reveal there are just SIX types of tweet

 


20140220-Diamondback-UMD-Pew-SMRF-6 Kinds of Twitter networks

The Diamond Back: Professor helps map social media connections

 


20140228-Forbes-Pew-SMRF-6 Kinds of Twitter networks

Your Social Media Conversation Is Like A Topographic Map

 


20140228-MediaPost-Pew-SMRF-6 Kinds of Twitter networks

Media Post

 


20140228-Politico-Pew-SMRF-6 Kinds of Twitter networks

Politico: How Twitter Works

 


20140228-UMD-Pew-SMRF-6 Kinds of Twitter networks

University of Maryland: New Map of Twitterverse finds 6 types of networks

 


20140228-UGA-Pew-SMRF-6 Kinds of Twitter networks

University of Georgia


2014-GovTech-6 Kinds of social media networks

GovTech


20140228-WiredIT-Pew-SMRF-6 Kinds of Twitter networks

Wired.IT


Posted in 2014, All posts, Foundation, NodeXL, Papers, Pew Internet, Research, SMRF, SNA, Social Interaction, Social Media, Social Media Research Foundation, Social network, Social Network Analysis, Social Roles, Sociology, Twitter Tagged 2014, 6 Types, Chart, graph, Information Visualization, Infovis, Map, network, NodeXL, Pew, Report, SMRF, Social Media, Social network, Social Structure, Twitter

Think link: network patterns in social media

24NovMay 7, 2015 By Marc Smith

2014-Pew-SMRF-NodeXL-6 Kinds of social media network patterns - Animated

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/

 

Posted in 2014, All posts, Data Mining, Foundation, Measuring social media, Metrics, NodeXL, Presentation, Research, SMRF, SNA, Social Media Research Foundation, Social network, Social Network Analysis, Social Roles, Social Theories and concepts, Sociology, Talks, Visualization Tagged Analysis, Data, graph, Motifs, network, NodeXL, Patterns, SMRF, SNA, Social Media, Social Media Research Foundation, Social network, Structures 1 Comment

October 24th, 2013: NodeXL Social Media Network Analysis Workshop at Arizona State University

13OctMay 7, 2015 By Marc Smith

asu_logo

I will speak about social media networks on October 24th, 2013 at the department of Computer Science at the Arizona State University.

Invited-Talk-Marc-A.-Smith-pdf-copy-662x1024

 

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

 

Posted in 2013, All posts, Foundation, Measuring social media, Metrics, NodeXL, Research, SMRF, Social Interaction, Social Media, Social Media Research Foundation, Social network, Social Network Analysis, Social Theories and concepts, Sociology, Talk, Talks, Technology, University, Visualization, Workshop Tagged Arizona, ASU, Chart, graph, Lecture, Map, network, NodeXL, SMRF, SNA, Social Media, Social Media Research Foundation, Talk, Training, University, Visualization, workshop

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Transparency in Social Media

2015-07-30-Transparency in Social Media-Structures of Twitter Crowds and COnversations
Transparency in Social Media
Sorin Adam Matei, Martha G. Russell, Elisa Bertino

CÓMO ENCONTRAR LOS HASHTAGS MÁS POTENTES: Para convertir LEADS a VENTAS (SEOHashtag nº 1) (Spanish Edition)

Apply NodeXL in espanol!

CÓMO ENCONTRAR LOS HASHTAGS MÁS POTENTES - Para convertir LEADS a VENTAS (SEOHashtag nº 1) (Spanish Edition)
By: Vivian Francos from #SEOHashtag Comparto algunas de las mejores formas de elegir los hashtags más poderosos y
que puedan generar tráfico a tus redes sociales para aprovechar el poder del
hashtag.
Si quieres aumentar tus interacciones, debes aprender a utilizar los hashtags como herramienta.

https://amzn.to/305Hpsv

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Networked By Lee Rainie and Barry Wellman

Social Media in the Public Sector

2015-07-31Social Media in the Public Sector-Cover
Ines Mergel

Ways of Knowing in HCI

2014-Ways of Knowing in HCI - Olson and Kellogg

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Virtual Community

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The Evolution of Cooperation

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