![A HMM POS Tagger for Micro-Blogging Type Textsin trying to understand the structure of micro-blogging texts, (eg., [14,4,7]), as well as computational viewpoint in trying to extract](https://static.fdocuments.in/doc/165x107/5f24330180a15612a1549d5a/a-hmm-pos-tagger-for-micro-blogging-type-texts-in-trying-to-understand-the-structure.jpg)
Key Events in Australian (Micro-)Blogging during 2010
17
Key Events in Australian (Micro-)Blogging during 2010 Axel Bruns / Jean Burgess ARC Centre of Excellence for Creative Industries and Innovation, Brisbane [email protected] – @ snurb_dot_info [email protected] – @ jeanburgess http://mappingonlinepublics.net – http://cci.edu.au/ Thomas Nicolai / Lars Kirchhoff Sociomantic Labs, Berlin [email protected] / [email protected] http://sociomantic.com/
-
Upload
axel-bruns -
Category
Education
-
view
1.914 -
download
0
description
Paper presented at ECREA 2010, Hamburg, 15 Oct. 2010.
Transcript of Key Events in Australian (Micro-)Blogging during 2010
Key Events in Australian (Micro-)Blogging
during 2010
Axel Bruns / Jean BurgessARC Centre of Excellence for Creative Industries and Innovation, [email protected] – @[email protected] – @jeanburgesshttp://mappingonlinepublics.net – http://cci.edu.au/
Thomas Nicolai / Lars KirchhoffSociomantic Labs, [email protected] / [email protected] http://sociomantic.com/
Project: New Media and Public Communication
• ARC Discovery (2010-12) – A$410.000• Axel Bruns (CI), Jean Burgess (SRF) – QUT, Brisbane• Lars Kirchhoff, Thomas Nicolai (PIs) – Sociomantic Labs, Berlin• Project blog: http://mappingonlinepublics.net/
Year 1 Year 2 Year 3
Social network sources:
· YouTube· Flickr· Twitter· blogs
Research tools:
· network crawler· content scraper· content analysis· network analysis
Research tool development and baseline data
Baseline information:
· data extraction· content creation statistics· patterns in terms and themes· baseline social networking map· interconnections between social network spaces
Content creation patterns
Changes over time:
· short-term statistics· regular / seasonal patterns
Cluster profiling:
· common themes / patterns· lead users
Focus on specific events
Cultural dynamics:
· rapid spread of new ideas· communication across clusters· thematic discourse analysis· relationship with main- stream media coverage
Methodology – Blogs
Analysis
Capture
Identification
Known Blogger
Population / Blog
Directories
Post Statistics and
Embedded Links
Patterns of Activity over
Time
Networks of Interlinkage (short/long
term)
Post Texts
Thematic Clusters and
Keyword Mapping
Analysis – Blogs
•Volume over time
•Comparison across clusters
Patterns of Activity over Time
•Interlinkage between known blogs
•Outlinks to external sources
Networks of Interlinkage (short/long term)
•Keyword analysis by cluster
•Keyword co-occurrence maps
Thematic Clusters and Keyword Mapping
0
200
400
600
800
1000
1200
1400
1600
1800
2000
5.11.2007 12.11.2007 19.11.2007 26.11.2007 3.12.2007 10.12.2007 17.12.2007 24.12.2007 31.12.2007 7.01.2008 14.01.2008 21.01.2008
Posts
Outgoing Links
Blog Network (between known blogs only)(~8500 blogs / 17 July to 25 Aug. 2010 / All page links / Node size: Indegree)
politics food
parenting
arts & crafts
design and style
2010
-Jun-
23 0
0
2010
-Jun-
23 0
1
2010
-Jun-
23 0
2
2010
-Jun-
23 0
3
2010
-Jun-
23 0
4
2010
-Jun-
23 0
5
2010
-Jun-
23 0
6
2010
-Jun-
23 0
7
2010
-Jun-
23 0
8
2010
-Jun-
23 0
9
2010
-Jun-
23 1
0
2010
-Jun-
23 1
1
2010
-Jun-
23 1
2
2010
-Jun-
23 1
3
2010
-Jun-
23 1
4
2010
-Jun-
23 1
5
2010
-Jun-
23 1
6
2010
-Jun-
23 1
7
2010
-Jun-
23 1
8
2010
-Jun-
23 1
9
2010
-Jun-
23 2
0
2010
-Jun-
23 2
1
2010
-Jun-
23 2
2
2010
-Jun-
23 2
3
2010
-Jun-
24 0
0
2010
-Jun-
24 0
2
2010
-Jun-
24 0
3
2010
-Jun-
24 0
4
2010
-Jun-
24 0
5
2010
-Jun-
24 0
6
2010
-Jun-
24 0
7
2010
-Jun-
24 0
8
2010
-Jun-
24 0
9
2010
-Jun-
24 1
0
2010
-Jun-
24 1
1
2010
-Jun-
24 1
2
2010
-Jun-
24 1
3
2010
-Jun-
24 1
4
2010
-Jun-
24 1
5
2010
-Jun-
24 1
6
2010
-Jun-
24 1
8
2010
-Jun-
24 1
9
2010
-Jun-
24 2
0
2010
-Jun-
24 2
1
2010
-Jun-
24 2
2
2010
-Jun-
24 2
3
2010
-Jun-
25 0
0
0
1000
2000
3000
4000
5000
6000
7000
Total
Change of Plans: Leadership #spill and #ausvotes Election
23 June, 18:00-00:00:
Speculation
24 June, 07:00-15:00:
Party Vote & Aftermath
Methodology – Twapperkeeper
Analysis
Capture
Identification
#Hashtag Archive
Tweet Statistics and
@Replies
Patterns of Activity over
Time
Networks of @Replies
(short/long term)
Tweet Texts
Keyword /Key PhraseMapping
Analysis – Twapperkeeper
•Volume over time
•Keyword frequencies
Patterns of Activity over Time
•Conversation vs. follower network
•Dissemination of RTs vs. @replies
Networks of @Replies(short/long term)
•Keyword analysis over time
•Keyword co-occurrence maps
Keyword / Key Phrase Mapping
#spill Discussion Network
#ausvotes: Overall Activity (17 July – 24 Aug. 2010)
Keyword Co-Occurrence
#ausvotes: Mentions of the Leaders
#ausvotes: Mentions of the Leaders (cumulative)
#ausvotes: Key Themes
#ausvotes: Key Themes
#ausvotes: Discussion Network17 July to 25 Aug. 2010 / All @replies / Node size: Indegree / Node colours: betweenness centrality)
Notes and Limitations
• Twapperkeeper relies on #hashtags• Problem if #hashtags are inconsistent/unclear• Follow-on @replies and retweets may not continue to use
#hashtags• Casual commenters may not use #hashtags in the first place• May miss early developments – e.g. #hashtag standardisation
• Twitter as a subset of society:• Broadband policy and Internet filter over-, asylum seekers
underrepresented• #hashtag use is a further sign of self-selection• Need to look to Twitter firehose for more comprehensive picture• Need to track baseline activity to understand how exceptional
#ausvotes was
• See more at mappingonlinepublics.net – up next: time-based animations...• Or find us at @snurb_dot_info and @jeanburgess
