Recommendation of OERs shared in social media based-on Social ...

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Each of the media and social networks has its own scheme of operation and different working characteristics, ranging from the length of text that can be used, the ...
Recommendation of OERs shared in social media based-on Social Networks Analysis approach Jorge Lopez-Vargas, Nelson Piedra, Janneth Chicaiza

Edmundo Tovar

Computer Science Research Institute Technical University of Loja Loja, Ecuador jalopez2, nopiedra, jachicaiza@{utpl.edu.ec}

Computer Engineering School Technical University of Madrid Madrid, Spain [email protected]

Abstract—The access to information is essential to learning as much as instruction. The evolution of the Web, from Web 1.0, where we were consumers of information, to a Web 2.0 where now we are producers and consumers of information, has allowed the Web becomes a huge database and in constant expansion. In these days much of the information published on the Web is published on social media, represented through social networks such as Facebook, Twitter, to name only the most prominent. Each of the media and social networks has its own scheme of operation and different working characteristics, ranging from the length of text that can be used, the use of different forms to identify topics until reaching the reciprocity of relationship between the participants. For example Twitter is a social network where millions of daily messages called Tweets are exchanged, within the message can be used labels, called hashtags, to identify the subject of the message, the message also may include links to other resources that expand the original content or showing interesting information and the relationships between users are represented as non reciprocal relationships named "Following". The extraction of information posted on social networks is solved in this research through the use of linked data, that allow retrieving resources and link with other external sources, graphs databases that help represent the working scheme of a social network, and with social network analysis (SNA), technique to discover relevant information that goes beyond the individual properties. The scope of this paper is to use information that is published on Twitter to extract and recommend Open Educational Resources in order to help with the learning process. The results obtained are a set of recommendations on users (identified as experts) and virtual communities (lists of Twitter users) and related events, according to the learning needs described as tags. Keywords—SNA; OER; URLs; Discovery; Twitter; Metrics; Recommender

I.

INTRODUCTION

Web 2.0 refers to the movement towards a more collaborative website where participants are not just content consumers but also are producers. All this was made possible by a set of tools that made the task of publishing HTML content simple, without the need of specialized expertise [1]. What undoubtedly caused and continues to cause, a tremendous growth of content that exists on the Web, but not only in numbers but also in its diversity.

Nowadays millions of digital resources with the form of URLs are sharing on social media networks such as Facebook (1 million links every 20 minutes)1 and Twitter (In 2010, of 90 million tweets per day the 25% contain links)2. These shared resources may be used for different purposes ranging from broadcast news, through advertising to reach more noble goals such as the learning. Another contribution that brings social media networks is the explicit relationships that exist between participants (friends, followers, followees, etc.) and implicit through the different elements - characteristics of each media network (e.g. co-occurrence of hashtags on Twitter). These relationships can be used to find relevant information to extend the analysis of networks beyond simple statistics. The vast majority3 of social media networks have an Application Programming Interface (technically known as API), it can be used to publish information and respecting some policies; query a subset of the information stored these networks, which turn on them in interesting sources data for several studies with different purposes. The work presented in this paper seeks to use social media networks as a source of open educational resources is shown. In the same way, we propose the use of Linked Data, and SNA metrics, order to extend the search of information and identify of influential users through social interactions in the network, respectively. The advantage of this approach is that it searches to capitalize on the collective intelligence that is present in many of these social media networks. In Section II the problem to be resolved arises; while in Section III the main concepts of social network analysis that were used are presented; in the section IV, presents the approach taken that allowed to develop an OER’s recommendation algorithm; in section V the related work is discussed, and finally to conclude with the results, conclusions and future work, sections VI and VII respectively.

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978-1-4799-3922-0/14/$31.00 ©2014 IEEE

http://www.statisticbrain.com/facebook-statistics/ http://techcrunch.com/2010/09/14/twitter-event/ http://www.programmableweb.com/apis/directory/1?apicat=social

II.

THE PROBLEM

Find a group of URLs posted on Twitter that can be used as OERs and that complement the training needs of a person in a particular domain. This definition highlights several elements of the problem as follows: •

The raw materials are the URLs.

•

The URLs will be considered as complementary OERs.

•

Need a mechanism to capture a lot of information because of the specific needs of users is unknown.

•

We cannot use traditional recommendation techniques because the user profile is unknown and as said [4] this techniques would require each URL to have feedback from several users to compute reliable recommendations.

With these features, and restrictions, the solution to the problem is use alternatives techniques such as: i) query expansion, via Link data, as a mechanism to capture a lot of information; ii) social network analysis as a means to get recommendations, but the recommendation takes in to account only the tweets with a valid URL; iii) the influence of users as a mechanism which guarantees the quality of the OERs. III.

different fields of research, to name a few: in search engines [9], in social tagging systems [10], or in recommender systems [11], [12], [13], [14], [15], [16]. A. Basic SNA concepts A social network is formed by a set of actors who are often called nodes, which are interconnected through of one or more types of relationships, if the connections or relationships between nodes have a sense in the network, the network is called directed, otherwise it is a undirected network. The sense of a relationship is important when it comes to finding the shortest path (or geodesic distance) between two nodes. The location of the nodes within the network, permit study the network and understand to their participants (nodes). This is done through a set of measures that are summarized below: •

Density measure describes the level of connection of all nodes present in a network. Dense networks are good for the coordination of activities among participants [7].

•

Centrality measures identify the most prominent nodes, those nodes that are extensively involved in relationships. o

Degree centrality is the number of direct connections that a node has. If you are on a network or directed graph can be divided into grade input (number of relationships that point to the node) and out-degree (number of relationships emerging from the node). In these cases the sum of the two is the degree of a node.

o

Betweenness which is the number of times that a node is present in the shortest path of a different pair of nodes to each other and to the previous. Identifies nodes that play the role of bridges interconnecting separate groups of nodes. In [7] states that it is a measure of popularity, efficiency and power within a network.

o

Closeness, based on the notion of distance, its value is determined by the inverse ratio of the sum of the geodesic distance from one node to the rest. Its value denotes independence or efficiency [7].

o

Eigenvector is a measure that assigns a value of popularity to each node within the network [8], which according to Bonacich in [17] can be understood as the sum of all direct and indirect connections.

o

Clustering coefficient is the tendency to make connections within their neighborhood of nodes, for example, is very likely that a person's friends know each other. Indicates the probability that two nodes within the neighborhood of a node selected at random are connected.

SOCIAL NETWORKS ANALYSIS

One of the techniques of data analysis that has now taken a new impetus is the social network analysis (SNA) [2]. The SNA has extended the analysis because it considers not only the individual properties of the participants in a network but also by the properties that these acquire when participate in a network and the role granted by the relationships [3]. SNA is defined in [5] as a study of human relationships through graph theory; and [6] argues, "It is a tool for analyzing the structure of interpersonal relations." Although these definitions are incomplete because as mentioned [7], the SNA "is not confined to the exclusive study of human social relations." In other definitions, treat social network analysis as a quantitative method of the relations between individuals or organizations [8], as well as an approach to model and analyze the interdependence and hidden patterns that shape to the social network structure. Despite the variety of mentioned definitions that can be found, the relationships are the feedstock of SNA, also known as the context of the social actor. This characterization is the fundamental basis of the SNA and allows it to differentiate from other research using only the properties of individual participants. This can be supplemented with what he says Wetherell in [7]: "Most broadly, social network analysis (1) conceptualizes social structure as a network with ties connecting members and channeling resources, (2) focuses on the characteristics of ties rather than on the characteristics of the individual members...” The key feature of the SNA is to prioritize relationships and their properties. This has allowed to be employed in several

Considering the characteristics of each of the centrality measures, these can be classified according to their scope within the network, at local level: degree and closeness; while that betweenness and eigenvector are considered measures global [8]. IV.

OER DISCOVERY

The OERs discovery process that was used is a combination of several technologies between the Web of Linked Data and social network analysis highlights - SNA; the first one had the goal of making an expansion of the search criteria, while the second was used to find relevant information through the relationships between users, who write posts about the subjects, which are of our interest and we need OERs. In the following paragraphs the discovery process implemented is described.

point is called as raw data and no further processing as described. D. Data normalization With the data collected from Twitter, processes of: harvesting and structuring are executed, in order that the information is ready for discovery tasks. Among the tasks to be performed are:

A. Topic list The process starts with the need to find useful OERs that have been posted on Twitter. This need should be expressed as a list of topics, that list must be generated with the intervention of a human being who must unambiguously describe their information needs. A recommendation for this task is use the concept of keywords to build the list of topics, where each keyword becomes a topic and the union of all topics reflects the need for information. B. Expansion query With the topics identified, the next step was to extend with the aim of finding information of each of them, not only literal way, but to extend through related topics, stepping beyond of the syntax of words to the semantic thereof. This process is beneficial as it let collect much information that in turn allows getting more resources. To accomplish this goal a set of SPARQL queries were built, one for each topic, with the goal of seeking the concepts related to each topic, these queries run against the DBpedia4 allowing find related topics through the predicates related the topic identified as an object within an RDF triple in DBpedia. Once identified the topics along with their related topics, the following steps can be summarized in the collection and exploitation of information to discover OERs. The following describes in detail each of these activities. C. TAW Crawler It is a tool developed previously by the Laboratory Science Data UTPL and allow information recovery through of search using the API that Twitter offers in an open, free of charge way but with certain policies and limits both the number of daily requests and the number of results, but as mentioned in [18] this data are enough. Once registered the search criteria that can be hashtags, words or exact phrases, the crawler begins to gather information and determine, based on the amount of information collected, to get the optimal execution time of each query to comply with the policies imposed by Twitter and to try to capture recent tweets. The information collected on this 4

http://dbpedia.org/About

•

Classification of raw data into multiple tables depending on the nature of the data, and database normalization. Data are classified as tweets, users, hashtags, mentions, retweets and URLs, as well as their relationships: a user posts a tweet, a tweet contains hashtags and URLs; a tweet has mentions and retweets.

•

Extension of shortened URLs, on Twitter is common for all URLs published are shortened by any of the services that exist today (t.co5, bitly.com6, goo.gl7, etc.). The expanding process, takes the URLs, and through of the protocol HTTP executes requests (get) and receive response codes, as well as other data such as MIME type referenced by the URL. This process is important because it allows: i) to determine the status (valid or not) and the type of the resource (MIME type), of the URL; ii) to disambiguate URLs, because the same URL can be shortened by different services and each one producing different shortened URLs iii) to clean the URLs of the tags that are used in marketing campaigns, for example UTM tags are widely used with Google Analytics8 through the sharing buttons that can also cause problems of ambiguity.

E. URL enhanced Once the URLs valid were identified, is necessary to enrich the information available to this moment (state and MIME type). This enrichment is done with those resources whose data type MIME is html or xhtml and try to extract the following information:

5 6 7 8

•

Title of Web page (HTML tag ).

•

And at the level of meta-tags: description, keywords and language , and

http://t.co https://bitly.com http://goo.gl http://www.google.com/analytics/

This information was used for two purposes, to improve the presentation of results and to find resources that meet the search criteria. F. OER discovery After running the above steps, a set of processes are executed to find related hashtags, influential users, and finally OERs to be recommended. Processes that run here have as data sources tweets with valid URLs and are: •

Hashtags graph: The nodes of this graph (undirected) are hashtags published in a tweet and a link between two nodes indicates a co-occurrence of hashtags in a tweet. The end result was a network that displays the topics associated to the topic of search and that gives user an updated overview of the topic. To understand how to the network is build see this example: with the tweet “Project Management expert Dave Campbell helps your #business: http://t.co/WkvcB5GJCN #planning #project #SmallBizChat #smallbiztips”. The metric to find related topics was Betweenness, which is showing those hashtags that are links to other information that the user can use to focus their interests.

•

Network of users: two networks were used here, the first is a directed network with nodes that are members involved in a retweet relationship, the source node is the author of a tweet and the target user is the name of the author who he makes a retweet. The second network is similar to the above but with the difference that the destination node is the name of the user mentioned. An example for the network of retweets is as follows: the user ChiaraBisello74 makes the following retweet: “RT @luigimengato: #lego + #project #management = #experiential #Learning http://t.co/QVGfJrh2Pc”. For the network of mentions, the user BarryHodge writes the following tweet “This is fantastic @SteveNestor1 you inspired me to use @HaikuDeck for project management.” In each of the complete graphs, the Betweenness metric that allows us to find the most influential users in both retweets and mentions networks is applied.

•

Recovery of OERs. With influential users identified, the following information retrieval is performed: for influential users identified in the network of the retweets the tweets that have URLs that were written by an influential author in that network are considered; while in the case of influential users extracted of network of mentions, are considered the tweets that have URL, where that user is mentioned. Thus both groups of influential users are considered; according to the role that everyone plays within their networks, and your influential is a measure of the quality. V.

RELATED WORK

The use of Linked data technologies to enhance the search and discovery of OERs is showed in [19] whilst the potential

de SNA as a tool of learning analytics was studied in [20]; in both approaches the results shows that combining they can maximize the OER discovery process. The majority of works related with Twitter recommendations, speak of recommendation of: followees, followers, hashtags, tweets and retweets; and mostly use traditional techniques of recommendation (collaborative filtering, content-based and hybrid), a few use the information that can be extracted from a social network of users with the goal of modify the traditional techniques [21], [22], [23] [24], [25] and [26]. But on the recommendation of URLs are very few studies [27] and even fewer the works that use information the social network analysis can provide in depth. Although as mentioned “Recently, social network analysis has been used in various applications. However studies on recommender systems using social network analysis are still deficient. Henceforth, we expect that new recommendation approaches using social network analysis will be developed. Therefore, developing the recommendation system research using social network analysis will be an interesting area further research” [28] and "Even if there are several graph based recommender systems, these recommender systems never employ the social network analysis measures in recommendation algorithms" [21]. Within this area, we can find a work [4] that uses network of followers of followees to find URLs to recommend. The difference with the work presented here is that in our proposal the user profile on Twitter is not known. VI.

RESULTS

Once implemented the discovery algorithm, the algorithm was executed on two different data sets, each set belongs to two different areas of knowledge and thus on different user groups. One of them is related to the administrative area, and the second with the technical area. The purpose of this selection is to analyze several behaviors of publication and not biased to one group with some behavior that influence the conclusions. For the first set of data related to the technical area, set a goal to find resources about the MatLab programming language, so it was used as seed of search the hash tag matlab. This label was considered sufficient due to the specificity of the same, ensuring that collected Tweets are closely related to the stated objective. The period of collection was 30 days. While for the second set of data, the goal is to extract resources that discuss project management and seed of search composed "#project #management" was used, which ensures that the collected Tweets contain both hashtags, allowing narrow the results to the group of interest, leaving aside those Tweets too wide that might talk about other issues. Like the previous group the collection period, was 30 days. For both collections of data, the data collection from Twitter was performed a twice daily with a difference of 12 hours between each collection. It should be mentioned that within the set of URLs, some were invalid (HTTP code returned was different from 200) the causes are varied, but can

be classified into two major groups, malformed URL and service unavailable. This feature is important because subsequent calculations are performed considering only the valid resources. An example of this is the calculation of the percentage of Tweets with URL works only with valid URLs. The table I show some stats. TABLE I.

Twitter about matlab and enable to the user select one of them to expanding information. A list of the ten most important tags is presented in the table II. Relevant hashtags for matlab Order 1 2 3 4 5 6 7 8 9 10

DATA SOURCE STATS

Stat Tweets Re-tweets Mentions Users Hashtags URL Valid URLs % Of Tweets with valid URLs % Of valid URLs that are published through a retweet % Of valid URLs that are published with a mention % Of URLs shared between Retweets and mentions % Of Re-tweets with a valid URL % Of mentions with a valid URL

#matlab 2957 598 1752 3128 477 679 590 34.73% 12.03%

#project #management 3666 655 789 2412 851 1421 1235 80.85% 15.63%

16.95%

14.82%

5.59%

5.89%

35.28% 25%

67.64% 48.54%

TABLE II. Order 1 2 3 4 5 6 7 8 9 10

This feature is important because subsequent calculations are performed considering only the valid resources. An example of this is the calculation of the percentage of Tweets with URL works only with valid URLs. You can easily see how the behavior is different in each set of data, for example the number of mentions and hashtags are the biggest differences and show the different behavior with both groups of users on Twitter. Results are presented for each data set and each was analyzed under the same parameters: hashtags, users, user groups and discovered resources. The images related to social network analysis can be viewed at the following Web sites and http://j4loxa.com/sna/ser/matlab http://j4loxa.com/sna/ser/pm, for Matlab and Project Management respectively. After applying the algorithm described above the following results were obtained: A. Matlab • Hashtags: The network that was used was formed through the co-occurrence of hashtags, i.e. when two or more hashtags appear within a Tweet that possesses a valid URL and the union of these sub-networks formed an undirected graph of 213 nodes (the 44.65% of all nodes) representing each hashtag and 727 links, representing the co-occurrence of one or more Tweets. A complete and interactive version can be found in http://j4loxa.com/sna/ser/matlab/, Graph: Hashtags. The metric that is used to find the prominent hashtags is Betweenness that in this particular case is useful, to find those hashtags that are gateway to other issues Matlab. This allows the user to have an overview of the topics of discussion on

Hashtags

Order

•

USERS TOP TO RETWEETS

MATLAB SciPyTip jadebustos enthought trelsco schlosi carangonu Alex__S12 Pybonacci VisionArtif

TABLE III. 1 2 3 4 5 6 7 8 9 10

Hashtags jobs job python R machinelearning Simulink science mooc datascience ipython

USER TOP TEN FOR MENTIONS Hashtags MATLAB plotygraphs KirkDBorne walkingrandomly LaoluAkinola designnews domhnallohanlon KristopheDiaz iversity sagebiel

Users: As already mentioned, we worked with two groups, one formed with users participating in a retweet and other one with users who are mentioned in a Tweet. Like the previous case here working with tweets that have a valid URL and in this case are within a retweet for the first case and a mention for the second. Both graphs that are formed are directed and have as their origin the user as author of the Tweet and other user destination can be an author of a tweet that was retweet or author who is mentioned in a tweet.

The network built has 296 nodes (9.46% of all users) and 243 links. The full interactive version can be found in http://j4loxa.com/sna/ser/matlab/, Graph: Users in a RT. While for those involved in statements the graph generated consists of 287 nodes (9.18% of all users) 308 links as well as in the above cases a full and interactive version can be found at http://j4loxa.com/sna/ser/matlab/, Graph: Users in mentions. The metric that is used to analyze users was Betweenness in this case representing the most influential user within a

network. These users become users to follow and are the base to recommend resources to view. The table III lists the top ten users participating in a re-tweet and table IV shows the users involved in the mentions. Of the total of 3128 users unique, we have been able to detect 81 that may be considered influential, 33 come from the re-tweets and 54 of the mentions, i.e. 6 users appear in both groups. These users are the bases that allow you to discover the resources, written as URLs, which have been published within the data set being analyzed. •

4

http://www.youtube.co m/watch?v=w3M6Pu6tc ic&feature=youtu.be&a

Curso Matlab : Introduccion

5

http://www.youtube.co m/watch?v=VZKILwK QXKw&feature=youtu. be&a

Curso Matlab 2: Operadores con matrices y otras funciones

Resources: The OERs or URLs were selected from Tweets posted by the most influential users obtained in the previous step, both at re-tweets and in mentions, the only difference is when it is a re-tweet is considered URLs that were published in Tweets where the user is the author of the tweet, while for users participating in the mentions shall be deemed the URLs present in the tweets in which users were mention.

TABLE V.

TABLE VI.

Among the results, a sorting algorithm was executed which is still under development and aims to position each URL according to their relation to learning. While it is still a work in progress the preliminary results are encouraging. The table V shows the first 5 URLs extracted and sorted in the previous steps, the complete table can be downloaded from http://j4loxa.com/sna/ser/matlab/ option OER discovered. TABLE IV. 1

2

3

URL https://www.youtube.co m/watch?v=jTS5Zmrrz Ms

https://www.youtube.co m/watch?v=UawhvugB 9qs&feature=youtu.be& a http://www.youtube.co m/watch?v=Q9CaljVR wLQ&feature=youtu.be &a

Order 1 2 3 4 5 6 7 8 9 10

SOME URLS EXTRACTED Title 1. Using MATLAB for the First Time

Curso Matlab 1: Variables matrices tipos de dato Curso Básico de GUI MATLAB // 9. Cuadros de diálogo (msgbox, errordlg, warndl, ...)

Description MIT 18.S997 Introduction to MATLAB Programming Fall 2011 View the complete course: http://ocw.mit.edu/18S997F11 Instructor: Yossi Farjoun License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu Aprenderemos variables formatos, tipos de datos, matrices y otros comandos de utilidad Introducción al uso de cuadros de diálogo en una GUI.

RELEVANT HASHTAGS TO PROJECT MANAGEMENT Order 1 2 3 4 5 6 7 8 9 10

The resources were obtained according to the algorithm described above and is an assembly of 95 (16.1% of all the valid URLs) unique URLs of which 48 are from users who participate in relations of re-tweets, 76 come from mentions and 29 URLs are common to both user groups, these were eliminated.

Videotutoriale 0: Curso Matlab No olvides pasarte por : http://www.facebook.com/ videotutoriale2 Twitter: @videotutoriale2 Aprenderemos operadores lógicos aritméticos y con matrices Twitter: @videotutoriale2 Facebook: http://www.facebook.com/ videotutoriale2

Hashtag project management jobs job projectmanagement PMOT leardership PM startups Books

TOP TEN OF INFLUENTIAL USERS User PRINCE2PROJECT findaseminar MichaelNir Nelsonb businesspeeps RedPressures CheriEssner biepbl Project__agent pmoplanet

B. Project management Like the previous analysis, this section shows the results to another data set, it uses the same metrics the only difference are the data, so the analysis is eliminated and only the statistics of the networks are mentioned •

Hashtags: The network has 598 nodes representing 70.27% of all hashtags (253 hashtags unrelated to others) and 1683 links. To show the network visit http://j4loxa.com/sna/ser/pm/, Graph: Hashtags and the table VI showed the top ten tags with greater Betweenness.

•

Users: The users from retweets have built a network with 552 nodes, representing 22.89% of all users, and 442 links. The complete version is http://j4loxa.com/sna/ser/pm/, Graph: Retweets; and table VII shows the top ten of the most influential users.

For mentions, the network has 534 nodes, the 22.14% of all users, and 463 links, The complete version is

http://j4loxa.com/sna/ser/pm/, Graph: Mentions, while Table VIII shows the top ten of them. TABLE VII.

INFLUENTIAL USER FROM MENTIONS

Order 1 2 3 4 5 6 7 8 9 10

User PMC_2014 youtube Nelsonb mgallops scoopit digitalPMsummit begeland Project DawnReidPM parallelproject

Of the 2412 unique users in total have been detected 112 users that can be considered influential, 54 come from the retweets and 58 of the mentions, i.e. 15 users appear in both groups. •

Resources: is a set consisting of 206 unique URLs (16.68% of all valid URLs) of which 185 are from users who are involved in relationships retweets, 87 from mentions and 66 are common to both URL groups.

Table VIII shows the first 5 URLs extracted and sorted in the previous steps, the complete table can be downloaded from http://j4loxa.com/sna/ser/pm/, link OER discovered. TABLE VIII.

URLS RECOMMENDED

URL 1 http://www.edutopia .org/blog/momwhat-is-projectmanagement-chrishare

Title "Mom, What is Project Management? " | Edutopia

2 http://www.projectmanagementpodcast.com/index.p hp/podcastepisodes/episodedetails/550-episode267-integration-ofprojectmanagement-andongoing-business 3 http://www.edutopia .org/projectlearning-classroommanagement

Episode 267: Integration of Project Management and Ongoing Business (Premium) #PMOT

4 http://www.projectw izards.net/en/merlin/

Project Management Keeps Learning on Track | Edutopia Merlin for Mac ProjectWizar ds presents Merlin Project Management for OS X

Description Guest blogger Chris Hare, project manager and parent, uses a book report assignment to demonstrate how project management is already in place in the classroom, and how easy it would be to fine-tune this practice. Project Management for Beginners and Experts. Are you looking to improve your Project Management Skills? Then listen to The Project Management Podcast, a weekly program that delivers best practices and new developments in the field of project management. Turn pandemonium into productivity by harnessing tech tools and cultivating selfdirected students.

5 http://www.projectm anagementwatch.co m/2013/04/23/online -projectmanagementcertification-examtraining-2013/

TABLE IX.

DATA RESUMEN

Matlab Nodes with at least one link

Hashtags Users retweet Users mentions Resources

Project management

Highlights / Influential

Nodes with at least one link

Highlights / Influential

44.65% 9.46%

6.92% 1.05%

70.27% 22.89%

13.40% 2.24%

9.18%

1.73%

22.14%

2.40%

Not applicable

16.10%

Not applicable

16.98%

The IX table summarizes the analysis, comparing the results obtained by each of the groups. VII. CONCULSIONS AND FUTERE WORK This research has shown the feasibility to obtain OERs from social media networks such as Twitter; through the application of SNA metrics to find influential users whose publications are considered interesting by a group of users that interact in a domain. The amount of resources is variable in time and is subject to user interactions or events that increase the number of publications. In the near future we will be working on two approaches. First continue developing a ranking system that allows to sort the results by the collective intelligence, i.e. favoring the social interactions of people. Secondly, in the validation of the results by users. REFERENCES [1] [2]

[3]

[4] ProjectWizards presents Merlin, the leading Project Management software for Mac OS X. Plan and manage your projects. Work on a Mac with a feature set similar to Microsoft Project on Windows and stay compatible to other project

Online Project Management Certification Exam Training 2013 | Project Management and Templates

management applications on Mac or Windows. Are you looking for project management certification exam study material? Which door is the suitable for online PMP training 2013?

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