Transcript of "Actualization of a Course Library through Influential Twitter Knowledge"
11th IEEE International Conference on Ubiquitous Computing and Communications (IUCC-2012) Liverpool, UK, 25-27 June, 2012 Actualization of aCourse Library through Influential Twitter Knowledge Malinka Ivanova, Tatyana Ivanova Technical University of Sofia College of Energy and Electronics
AimTo explore how thecollective intelligence ofTwitter users couldsupport a course librarylist updating andpersonalized learning
Content• Microblogging, influential users• Related work: Twitter use and influence• Mechanisms for knowledge gathering and library list updating – Using keywords – Exploration of hashtags – Tracking the favorite messages – Utilization of Twitter lists• Discussion and conclusion
Internet Technologies Problem definition static immutable current course library listcover main topics How do I reach the latest cover few additional problems and novel information? I have specific questions! I have to solve a complex problem! How do I personalize my course library list? How do I find valuable and relevant information sources?
Problem definition new internet technologies the latest solutions emerging tools and applicationsdynamic course library list with streaming knowledge relevant learning resource sharing, information observation, discussions good practices spreading, events tracking, following shared announcing content, keeping messages as favorite Whether and how Twitter posts may be used to facilitate the learning process?
The hypothesis The influential users and trending microcontent according to analytical Twitter tools could be very powerful sources of useful and engaging knowledge for organizing an updated library list
• Influential Twitter users possess “personal attributes like credibility, expertise, enthusiasm and network attributes such as connectivity or centrality”. Bakshy, Hofman, Mason and Watts, 2011 They influence their followers directly or indirectly through re-twitting cascades
Research questions• “How can Twitter messages be mined for receiving suitable learning material from influential users?”• “How could educators be supported in library lists updating?”• “Are the most influential Twitter participants the heralds of novel and engaging information?”
Related Work “We found that the social aspect appears to be predominant in motivating users’ interactions”. Sousa, Sarmento and Rodrigues, 2010Twitter tool “Eddi” for easy andquick finding of popular topics bysearching, or navigating a tagcloud, timeline or categoriesBernstein, Suh, Hong, Chen, Kairam andChi, 2010
Related Work• Several studies about Twitter use for tracking various topics as: – Public health - Ailment Topic Aspect Model for Twitter is created to connect symptoms, treatments and general words with diseases Paul and Dredze, 2011 – Earthquake shakes – algorithm monitoring tweets is presented and an earthquake reporting system in Japan is developed Sakaki, Okazaki and Matsuo, 2010Domain and tack-sensitive filtering is used to eliminate false-positive tweets, a lot of tested techniques are domain-dependent
Related Work• Hashtags usefulness in Twitter messages - Carter, Tsagkias and Weerkamp (2011) study multilingual hashtag characteristics and derived translation rules• De Choudhury, Counts and Czerwinski (2011) define the important attributes for characterizing social media content on a given topic: • diffusion property • responsive nature • presence of external information • temporal relevance • thematic association of the tweet within a set of broadly defined categories • geographic dimension • authority dimension of the tweet creator • degree of activity of the tweet creator
MethodA literature list is created for the Internet Technologiescourse following the next procedural steps: 1. Formation of search queries throughthe most suitable keywords that describethe course focus – html, css, web design. 2. Extraction of the relevant and influential Twitter users that could be sources of suitable learning content. 3. Applying analytical and measurement tools to narrow down the most expedient Twitter users for identification of novel and emerging information and knowledge.
Experimentation The library list is tX seen as a Twitter H tY tZ user uL, forming an ego-centric network searching TwittertX users through tX tY tZ uL keywords K, tY tZ hashtags H, favorite messages F and K Twitter lists T F tX T tY tZ
Utilization of Keywords in a Search Query • searching for Twitter posts with relevant information Internet pointing valuable sources using “html”, “css” and Technologies “web design” keywords course • TweetLevel tool (http://tweetlevel.edelma n.com/) - used to gather the 100 most influential Twitter users and the most tweeted words related to “html”
Utilization of Keywords in a Search Query The 100 influential Twitter users – extraction and selection of useful users• Mining the tweeted microcontent about related to “html” words: html 5, html tags, html form, design, html email, html code, psd file, web designer, basic html• The experiment is repeated – 20 participants - the intersection between two different sets responding to the keywords “html & html5” – 11 users tweet content relevant to keywords “html & html code” – 1 user combines “html & basic html” keywords Keywords html & html5 Keywords html & html Keywords html & basic code html
Utilization of Keywords in a Search Query The 100 influential Twitter users – extraction and selection of useful users• In the category “web design” - the most shared links are to web sites with resources, tools and tutorials, news web sites, blogs, and above found web sites in category “html”• The frequently used keywords related to “web design” - web template, design process, website design, web designer, search engine marketing• 15 users - the intersection between “web design” and “web template” Keywords web design & web template
Utilization of Keywords in a Search Query The 100 influential Twitter users – extraction and selection of useful users• 31 participants who tweet about “html & css”• 14 Twitter users who frequently weave into their messages all three keywords “html”, “css” and “web design” – Excluded – 2 of them frequently post messages about new jobs and positions for web designers/programmers and 1 often tweets in languages other than English Keywords html & css & web design Keywords html & css
Utilization of Keywords in a Search Query The 100 influential Twitter users – extraction and selection of useful users •Further explorations - about the volume and relevant value of the tweeted content •They post an average amount of 440 messages per week – 84% tweets and 16% re-tweets 8 499 3 659 followers 1 web followers 1 graphic designer designer 17 471 16 097 followers followers 1 web 2 web and developer 35 084 graphic followers designers 60 128 3 news and followers online 40 125 technology followers 25 198 journals 553 822 followers Academics and educators teaching web 3 representa- 7 474 followersprogramming and design are not in the final tives of IT followerslist of the most influential Twitter users companies 3 967 followers
Utilization of Keywords in a Search Query Extraction and selection of useful educators• Twitter search tool - utilized to find relevant profiles of twitting professors and tutors Influence, popularity and engagement of educational society
24 Exploration of hashtags hours hashtags #html, #css and #webdesign - the Hash Tracking 858 tool (http://www.hashtracking.com/) is utilized tweets 274 479 followers #html• 4 users are selected as distributors of valuable and meaningful info – 3 web designers/programmers 85% 97% 76% 15% 24% tweets tweets 3% re- tweets re- re- tweets tweets tweets 80 256 308 1 1 1 tweets tweets tweets week week week – 1 writer about technology 100% tweets 359 1 tweets week
Exploration of hashtags 24 hours 1353• 5 interesting persons are selected tweets 1 104• 2 of them - the same distributors of knowledge who include in 597 their messages hashtag #html followers #css• 1 of them = the most influential Twitter user when a keyword search is performed• 2 new users – 1 technology interested person 51% 49% tweets re- tweets 91 1 – 1 web designer/developer tweets week 97% tweets 3% re- tweets 500 1 tweets week• an average of 37% of content includes links to blog posts and resources published on web sites• irrelevant messages are an average of 51%
Exploration of hashtags 24 hours• 4 unique twitting users in the area of web design 1500 are chosen and examined tweets 1 043 – 1 aggregation service collecting the resources 080 related to web design from blogs #web followers design 19% 81% tweets re- 75% tweets 25% tweets 500 re- 1 tweets tweets week 396 95% 1 tweets tweets 5% re- week – 1 online magazine for web design tweets – 2 company representatives 500 1 tweets• 64% of all their tweeted messages are useful links week to interesting web resources and 3% to tips and tutorials 88%• irrelevant messages are 33% tweets 12% re-• the often used hashtags are: #design, tweets #socialmedia, #photoshop 499 1 tweets week
Tracking the favorite messages• Tracking the favorite messages -FavStar (http://favstar.fm/) tool 1st• 5 new users are extracted as valuable informative sources 74%• among the most favorite messages - links to tutorials, to articles, and tweets 26% to web sites with useful stuff and resources re-• 7 messages of the first Twitter user are among the most favorite – tweets 499 the first message is favored 173 times, the rest of the messages are 1 tweets favored as follows: 26 times, 25 times, 25 times, 20 times, 19 times week and 18 times. 2nd 3rd Twitter user 1st 2nd 3rd 4th 5th 93% 82% tweets 7% re- 18% tweets tweets re- number of 173, 7, 17 72, 17, 8,6,5, 3, 3, tweets 90 71 1 favorite 26, 18 5,4,3, 3, 3, 1 tweets tweets week messages 25, 3,3,3, 3, 3, week for 3 25, 3,3,3, 3 months 20, 3,3,3 4th 5th 19, 18 49% 92% 51% followers 31 4 275 3 627 3 084 965 tweets tweets 8% re- re- 149 tweets tweets 53 13twe 1 1 tweets ets week weekThe number of favorite messages by followersfor a period of 3 months
Discovering users from Twitter lists• the most listed users - utilizing Listorious (http://listorious.com/) tool• 3 lists are selected as most suitable, respectively with 11 062, 2 405, 1 707 followers• 4 users are chosen• the average usefulness and relevance of content in tweeted messages is 14.75% and it is the lowest value in comparison with the previous experiments № 1st 2nd 3rd 4th tweets/week 219 64 17 58 tweets, % 90 78 82 84 re-tweets, % 10 22 18 16 followers 21 190 24 451 552 2 682 useful content, % 21 15 10 13 position Online Informer Web Internet technology about designer and journal web and social design teacher media strategist
DISCUSSION• The latest layout of the course library list includes informative and knowledgeable resources tweeted by 36 users – 11 are in the top most influential people – 6 are active twitting educators found through keywords searching queries – 10 are chosen among the most impressionable persons weaving hashtags in their messages – 5 possess many favorite messages by their followers – 4 are found as useful and interesting for the course searching into categorized Twitter lists Legend: users selected by keywords educators selected by keywords users selected by hashtags users selected by favorite messages users selected from Twitter lists
DISCUSSION• An ego-centric network of the course library list is created - the weight of any edge is a summary value of Twitter users’ influence, taking in consideration coefficients of influence, popularity and engagement (according to TweetLevel tool) • The influence - the number and authority of followers, the frequency of usernames mentioning and messages re-tweeted by others Legend: • The popularity - number of followers, users selected by following people and the lists number • Engagement - users’ activity, number of keywords followers, number of username mentions and educators selected by ratio broadcasting/engagement keywords• With the highest value of influence are: users selected by hashtags the most influential people + users in Twitter lists > users selected by users with many favored messages + users adding favorite messages users selected from hashtags > twitting educators found through keywords Twitter lists
DISCUSSION• The professions of the most valuable users for the course• The main stream of learning materials comes from: – people practicing web design/programming, – following by IT companies’ representatives, educators’ guild and journalists• a new profession is forming of informers striving to discover news and to spread them among the best possible large audience
DISCUSSION• Among the found valuable resources are:tutorials blog posts sharing opinion and giving ideas novelty articles useful codes and templatestips for accomplishing small tasks and dealingwith tricky issues new tools and explanations of contemporary and emerging technologies• Irrelevant messages vary between 7% and 59%• The users who are most often placed in lists are among the influential persons, but they are with the lowest meaning of content for our library list
Conclusion• Searching through keywords queries is still the most effective mechanism for gathering the needed information• A small part of influential and impressionable Twitter users weave hashtags in their messages and in this way they stay invisible in searching through hashtags• The combined search through keywords and hashtags does not give effective results• Through the search via usernames and user profiles are achieved only partial results, because of not well formed self-description by Twitter users• Very strong Twitter users for the course library list are found among the most influential users, the most favorite users and educators staying in the long tail of the Twitter stream Actualization the resources for learning through the proposed approach gives a possibility of forming a technology oriented and social media based library gathering the useful and the latest achievements in the examined area context
Conclusion• Both tag-based and keyword-based search results include many irrelevant posts and it is necessary that some filtering of returned results or recommendations for searching be proposed to the educators• This preliminary study could be used for developing such a filtering and recommendation system for making the process of searching of learning resources in the microblogging systems easier and more effective• The findings point the importance of well formed profile of social web users, facts about their activities and the existence of the appropriate searching strategies for successful resources finding• Consequently, a clear and explicit knowledge-based model of the users, as well as of the learning resources’ structure has to be created as a basis for building a searching and recommendation system
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