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Akisato Kimura ( @_akisato )ICWSM2012 BRIEF REVIEW
Conference venue Trinity College Dublin, Ireland Famous place : Old Library   Actual venue : Bio. Med. Bld. (not in campus)
Reception venue Guinness Storehouse, west Dublin                      300-deg view, 7-th floor at west Dublin
What’s ICWSM? International AAAI Conference on Weblogs  and Social Media   Annual conference, 6th for this year.   Seem...
Symbolic panel discussions I Want to (Net)work With You, But I Dont  Know What/Where/Who You Are   Panelists from Cisco,...
Basic statistics                                           Only single track                                              ...
General overview Computer science << sociology   Data collecting, analyses & discussions    > results > performance > te...
Interesting events & efforts Town hall meeting   Discussing future directions of the conference    with all the particip...
Resources All the papers presented in the main  conference can be freely accessible from   http://www.aaai.org/Library/I...
Our presentation Creating Stories : Social Curation of Twitter  Messages   Curated lists = supervised corpora for analyz...
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...
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!                 ...
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...
で,結果は… Webを見た方が早いと思います.  http://livehoods.org/maps
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, ...
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/   ...
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
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   ...
結果 これもwebを見た方が早いと思います.  http://health.scenedipity.com/pollution
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 anal...
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...
おしまい
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ICWSM12 Brief Review

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Briefly reviews International Conference on Weblogs and Social Media (ICWSM12) from my perspective.

The latter part written in Japanese, sorry for that.

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Transcript of "ICWSM12 Brief Review"

  1. 1. Akisato Kimura ( @_akisato )ICWSM2012 BRIEF REVIEW
  2. 2. Conference venue Trinity College Dublin, Ireland Famous place : Old Library Actual venue : Bio. Med. Bld. (not in campus)
  3. 3. Reception venue Guinness Storehouse, west Dublin 300-deg view, 7-th floor at west Dublin
  4. 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. 5. Symbolic panel discussions I Want to (Net)work With You, But I Dont 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. 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. 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. 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. 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. 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. 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. 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. 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. 14. で,結果は… Webを見た方が早いと思います. http://livehoods.org/maps
  15. 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. 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. 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. 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. 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. 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. 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. 22. おしまい
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