Briefly reviews International Conference on Weblogs and Social Media (ICWSM12) from my perspective. The latter part written in Japanese, sorry for that.

1. Akisato Kimura ( @_akisato )
ICWSM2012 BRIEF REVIEW

2. Conference venue
 Trinity College Dublin, Ireland
Famous place : Old Library Actual venue : Bio. Med. Bld. (not in campus)

3. Reception venue
 Guinness Storehouse, west Dublin
300-deg view, 7-th floor at west Dublin

4. What’s ICWSM?
 International AAAI Conference on Weblogs
and Social Media
 Annual conference, 6th for this year.
 Seems to be a conference on Twitter & other
social media, few papers as to weblogs.
 A lot of participants from companies and labs
about SNS, mass media, ads, and marketing.
 A major cluster = sociologists,
a unique conference hosted by AAAI.

5. Symbolic panel discussions
 I Want to (Net)work With You, But I Don't
Know What/Where/Who You Are
 Panelists from Cisco, IBM, LinkedIn & Datahug
 News Generation and Consumption Through
Social Media
 Panelists from Storyful, Newswhip, Irish Times,
C-SPAN & Guardian
 Machine learning accounts for a small portion.

6. Basic statistics
Only single track
Not high quality
as the rate indicates
Our presentation (can’t see any other JPN pres.)
Attendees: over 330 in advanced registration (x3 of papers),
half of them from USA, only 5 from Japan.

7. General overview
 Computer science << sociology
 Data collecting, analyses & discussions
> results > performance > technical novelty
 Most oral presentations with high quality
 Especially in terms of analysis and discussions.
 Don’t mind theoretical soundness and novelty.
 2 giants: Twitter & Facebook
 But, we should not rely only on the giants.
 The direction includes cross platform analysis.

8. Interesting events & efforts
 Town hall meeting
 Discussing future directions of the conference
with all the participants, not only PC members.
 Industrial panel
 With powerful debaters from various industries
 Dataset sharing service
 Provides new datasets used by papers.
 All datasets released as openly available
community resources. http://icwsm.cs.mcgill.ca

9. Resources
 All the papers presented in the main
conference can be freely accessible from
 http://www.aaai.org/Library/ICWSM/icwsm12contents.php
 All the workshop papers are also free :
 http://www.aaai.org/Library/Workshops/workshops-library.php
 I gathered most tweets as to ICWSM 12,
freely accessible from
 http://togetter.com/id/_akisato

10. Our presentation
 Creating Stories : Social Curation of Twitter
Messages
 Curated lists = supervised corpora for analyzing
microblog messages
http://www.brl.ntt.co.jp/people/akisato/socialweb1.html

11. 面白かった発表 １
 The Livehoods Project: Utilizing Social Media
to Understand the Dynamics of a City
 Won the Best Paper Award
 Twitterタイムラインから取れる
位置情報（tweets with geotags, 4sq etc.）から，
かなり局所的な地域の特性の変化が掴める．
URL: http://livehoods.org
Twitter ID: @livehoods

12. Livehoods project
［解析のポイント］
人々の日々の行動パターンから
場所の特性を明らかにしよう！
［データ収集］
11M of 別研究のデータ，
7M from Twitter TL.
論文で使われているのは，
40K check-ins (4K人, 5K箇所)
［解析方法］
位置をnodeとするspectral clustering.
各nodeの素性構築が重要．
http://livehoods.org/research

13. クラスタリングの方法
［素性のポイント］
各位置でBo”CheckIns”を計算，
人数と同数次元のベクトル．
［素性解析のポイント］
Bo”CheckIns”の類似性を
2位置間の類似性と見なす．
＝ 同一人物が同じくらい2位置
にいれば，その2位置は仲間．
［クラスタリング］
Spectral clustering.
物理的距離の遠い2位置は
無関係と見なすことにする．

14. で，結果は…
 Webを見た方が早いと思います．
http://livehoods.org/maps

15. 面白かった発表 ２
 Modeling Spread of Disease from Social
Interactions
 Best paper award candidates
 感染症がどのように拡散
していくか，を，
Twitter（＋位置情報）だけ
から予測しよう．

16. 何が，なぜできてなかったのか？
 情報源は病院しかなかった．
→ Global aggregationsしか取れなかった．
 Google Flu Trends: http://www.google.org/flutrends/
 CDC Statistics: http://www.cdc.gov/datastatistics/
 国立感染研情報センタ: http://idsc.nih.go.jp/disease/influenza/
 でも，本当に必要な情報は，
いつ，どこで，誰が感染しているか？
 だって，感染したくないし…

17. 感染源は誰だ？
 Twitterのfollow関係だけで感染する…
わけがない！ （それは映画の世界…
 同じ時間に同じ場所にいることが大事
 位置情報と時刻の共起を軸に考える

18. 感染したことを知るには？
 [Uni, bi, tri]-gram（+多量の後処理）を素性とした
半教師付きSVM cascadeで識別．
教師なし大量コーパス
(不)完全教師付
少量コーパス
Self-training

19. 結果
 これもwebを見た方が早いと思います．
http://health.scenedipity.com/pollution

20. 面白かった発表 その他羅列１
 Crossing Media Streams with Sentiment:
Domain Adaptation in Blogs, Reviews and
Twitter
 Sentiment analysisをTwitterだけでやるの
無理だから，reviewやblogを教師に使う．
 Exploring Social-Historical Ties on Location-
Based Social Networks
 Foursquareもの．トピックと位置，両方使う．
 階層Pitman-Yor過程によるモデル化

21. 面白かった発表 その他羅列２
 The Emergence of Conventions in Online
Social Networks
 Won the Best Paper Award
 Twitterにおける「文法」らしきものは，基本
的にボトムアップにできあがってきたもの．
それを網羅的に検証．

22. おしまい