The document discusses using Twitter to update a course library list through influential users. It explores using keywords, hashtags, favorite messages, and lists to identify relevant Twitter accounts. 36 informative users were identified through these methods to include in an updated course library list, with some users identified through multiple exploration techniques. The library list aims to provide engaging, novel, and personalized learning resources through Twitter content.
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Actualization of a Course Library through Influential Twitter Knowledge
1. 11th IEEE International Conference on Ubiquitous
Computing and Communications (IUCC-2012)
Liverpool, UK, 25-27 June, 2012
Actualization of a
Course Library through
Influential Twitter
Knowledge
Malinka Ivanova, Tatyana Ivanova
Technical University of Sofia
College of Energy and Electronics
2. Aim
To explore how the
collective intelligence of
Twitter users could
support a course library
list updating and
personalized learning
3. 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
4. Internet Technologies
Problem definition
static
immutable
current course library list
cover 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?
5. Problem definition
new internet technologies
the latest solutions
emerging tools and
applications
dynamic 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?
6. 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
7. • 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
8. 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?”
9. Related Work
“We found that the social aspect appears to be predominant
in motivating users’ interactions”.
Sousa, Sarmento and Rodrigues, 2010
Twitter tool “Eddi” for easy and
quick finding of popular topics by
searching, or navigating a tag
cloud, timeline or categories
Bernstein, Suh, Hong, Chen, Kairam and
Chi, 2010
10. 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, 2010
Domain and tack-sensitive filtering is used to eliminate
false-positive tweets, a lot of tested techniques are
domain-dependent
11. 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
12. Method
A literature list is created for the Internet Technologies
course following the next procedural steps:
1. Formation of search queries through
the most suitable keywords that describe
the 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.
13. Experimentation
The library list is
tX seen as a Twitter
H
tY tZ user uL, forming an
ego-centric network
searching Twitter
tX 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
14. 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”
15. 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
16. Utilization of Keywords in a Search Query
The 100 influential Twitter users – extraction and selection of useful users
• using the keyword “css” - the most shared links are to web
designers/programmers sites with design elements, resources and
tutorials, online news sites and developer tools
• Related words to “css” that are part of tweeted microcontent are: html,
css framework, design, css animation, javascript, web design
• The selected keywords for further explorations: “css framework” and “css
animation”
– 6 influential Twitter users who use the keywords “css & css framework”
– 6 influential Twitter users who include “css & css animation” keywords in their
messages
Keywords css & css framework Keywords css & css animation
17. 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
18. 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
19. 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 followers
programming and design are not in the final tives of IT followers
list of the most influential Twitter users companies 3 967
followers
20. 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
21. 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
22. Exploration of hashtags
Twitter 1st – 1724 2nd – 2934 3rd - 1327 4th - 26 902
user/messages followers followers followers followers
content
Links to tutorials 30% 17% - 5%
and courses
Links to tips and 5% 3% - 2%
codes
Blog and web sites 57% 33% 38% 73%
resources
News and events 1% 4% 3% -
Irrelevant 7% 43% 59% 20%
messages to the
course
Other hashtags #html5, #css, #javascript, #wordpress, #cms,
#css3, #programmers, #template, #wordpress,
#template, #courses, #design, #css #design, #css,
#wordpress, #web, #cms, #template,
#website, #developer, #flash
#webdesign #software,
#beginners,
#website,
#dreamweaver
23. 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%
24. 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
25. 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 week
The number of favorite messages by followers
for a period of 3 months
26. 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
27. 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
28. 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
29. 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
30. DISCUSSION
• Among the found valuable resources are:
tutorials blog posts sharing opinion and giving ideas
novelty articles
useful codes and templates
tips for accomplishing small tasks and dealing
with 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
31. 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
32. 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
33. Thank you for your attention!
For contacts:
m_ivanova@tu-sofia.bg
tiv72@abv.bg
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