ICWSM12 Brief Review
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ICWSM12 Brief Review

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

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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ICWSM12 Brief Review ICWSM12 Brief Review Presentation Transcript

  • 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.  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.
  • 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.
  • 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.
  • 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.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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.
  • 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, Twitter(+位置情報)だけ から予測しよう.
  • 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…
  • 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 Self-training
  • 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 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
  • 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.
  • おしまい