ID Web SNS (Social Networking Service) python numpy *2 scipy * (item) (samples) (mean)

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1 Vol.23-DPS-56 No. Vol.23-GN-89 No. Vol.23-EIP-6 No. 23/9/,a) 2,,, An Analysis of Video Meta-data and Thier Folksonomy Tags Using Niconico Dataset KUROSE Hiroshi,a) YAMADA Shigeki 2 Abstract: Viode meta-information of Nicovideo has provided by DWANGO Co., Ltd. and National Institute of Informatics (NII). This paper shows the analysis results of the statistical data of the meta-information of the videos and attached folksonomy tags on their video contents. Keywords: Video meta-data, Folksonomies, Tag based routing, Niconico dataset. [] * 8 Academic Foundations Programs, Kanazawa Institute of Technology 2 Principles of Informatics Research Division, National Institute of Informatics a) * GB 3GB [2] ID ID URL c 23 Information Processing Society of Japan

2 ID Web SNS (Social Networking Service) python numpy *2 scipy * (item) (samples) (mean) (stdev) (min) (max) (median) (mode) (kurtosis) (skewness). file size (high) file size (low) MB kb data rate (high) data rate (low) length MB kb/s (title length) (description length) (tag length) UTF8 distinct tag attached tag 2 mp4 MPEG swf Small Web Format flv Flash Video :4: distinct tag category tag distinct tag category locked tag lock lock&category. category lock 3 &, >,, < HTML &, >, ", <, 534, 236, 64, Vol.23-DPS-56 No. Vol.23-GN-89 No. Vol.23-EIP-6 No. 23/9/ days np osted, sp osted [TB] (), (2) *2 *3 c 23 Information Processing Society of Japan 2

3 Vol.23-DPS-56 No. Vol.23-GN-89 No. Vol.23-EIP-6 No. 23/9/ ( =8,35,696) Table statitics of video meta data(number of videos is 8,35,696) item samples mean stdev min max median mode kurtosis skewness length [sec] number of video posted/day amount of posted [TB/day] number of views number of comments number of mylists tags per content title length description length distinct tag length attached tag length file size (high) [MB] e file size (low) [MB] e data rate (high) [kb/s] e datarate (low) [kb/s] e Table 2 Tag usage situation(number of tags is 5,328,34) item samples mean stdev min max median mode kurtosis skewness distinct tag category tag locked tag lock&category np osted = 2.455days () sp osted =.5days (2) y 3.4 bins % 4 25TB 5 length % MB 9% KB/s MB/s 8 [3] c 23 Information Processing Society of Japan 3

4 number of videos posted on the day n=268, solidline:simple moving average of 7days days since data size of posted video on the day in TB 4 n=267, solid line: simple moving average of 7days Vol.23-DPS-56 No. Vol.23-GN-89 No. Vol.23-EIP-6 No. 23/9/ days since Fig. number of video posted on each day Fig. 2 2 total size of posted files on each day n=268, bins=.e number of videos posted on each days n=268, bins=.e posted data size in TB on the day Fig. 3 3 of posted videos number Fig. 4 4 of posited videos size. n=83558, bins=.e file size (low) file size (high). - 2 play time in minute Fig. 5 5 of play time -4 n=835459, bins=.e data size in MB Fig. 6 6 of video file size 9 UTF8 c 23 Information Processing Society of Japan 4

5 Vol.23-DPS-56 No. Vol.23-GN-89 No. Vol.23-EIP-6 No. 23/9/ 情報処理学会研究報告. n=83528, bins=.e+5.6 data rate (low).4.2 Comment 6 frequency of appearance.8 7 data rate (high) View 5 4 MyList data rate in kb/s 3 n=835696, bins=.e count 図 7 データ転送レートの累積確率分布 図 8 再生数 コメント数 マイリスト数の出現頻度分布 Fig. 7 of data rete Fig. 8 appearence of counts of views, comments, mylists ある タグはタグ種類別 (distinct tag) と動画に付与され distinct tag は種類別である たタグ (attached tag) の 2 つを集計している タイトルは 3 字程度が 動画説明文は 4 字以内が多い タグは 種 類別では平均 8.3 字 動画付与では平均 6. 字になってい 表 3 タグ割当状況 (動画数=8,35,696) Table 3 Attached tag(# of videos is 8,35,696) ることから 語句の文字長が短いタグが動画に多く付与さ category lock number of attached distinct tag れている タグ付与第 位は ゲーム でこの場合は 3 字 26,28,93 4,939,8 2,88,86 86,57 38,937,7 5,328, ,836,39 3 6,836,26 3 画あたりのタグ数は平均 5.5 であった タグが付与されな 5,328,32 4,939,8 い動画は全体の 2%存在した 頻度では 動画に付与され 9,644,225 86,93 45,773,378 5,328,34 である 3.5 タグ情報の出現頻度分布 図 に動画に付与されたタグ数の頻度分布を示す 動 たタグ数は 4 か 5 が多いが両方合わせても全体の約 3%で ある 図 にタグが付与された動画の多い順から並べたタグ ランキングの累積分布を示す カテゴリタグは種類も少な く第 位で 6%を占め上位 2 位まででカテゴリタグが付 与された動画の大多数を網羅する 全タグを対象とした場 合は 第 4 位を超えないとタグが付与された動画の過半 図 2 にカテゴリ指定されたタグの動画への付与数を示 す 横軸は 付与数の多いタグ順に並べている 動画への 付与数は 3 位を超えると急速に減少する カテゴリ指定のタグは 3 種類あるが タグの語句を見 ると VOCALOID と vocaloid のように表記ゆれを同 一とすると 4 種となる さらに 日記 と tagebuch な 数を網羅できない 最小二乗法による指数近似 abx では ランキング順位 k k に対してタグ ロック指定タグ カテゴリ指定タ グの出現数は それぞれ式 (3), (4), (5) である ど意味が同じタグを同一視すると 33 種に集約できる こ のうち動画に付与された数が 2 以上のものは 3 種であっ た なお ニコニコ動画のタグ検索機能では 検索文字の 大文字 小文字を同一視している T ag = exp( 3.56e 7 k) (3) 次に あるタグが付与された動画の総再生数とタグの出 T aglock = 3.5 exp( 4.349e 6 k) (4) 現数の関係を見る 図 3 にタグによる再生数ランキング T agcategory =.552e+5 exp(.475 k) (5) 同一タグであってもカテゴリまたはロックが指定されて 分布を示す タグ出現数のランキングを上位 位まので 各タグにおいて そのタグが付与された動画の総再生数を 求め 再生数の多い順に並べ直している いる場合 値が と指定されていない場合があるため 状 同様にタグ出現順位を 位ずつ増やして 8 位までの 況について確認する カテゴリ ロックの値を変えてタグ 分布を描く 第 位までの最低再生総数は 7 程度であ 数を調べた結果を表 3 に示す は値が任意であること るが 第 2 位での最低再生総数は 5 5 程度になる を表す number of attached は動画に付与されたタグで これは 動画に付与される数が多いタグでも再生数が低い c 23 Information Processing Society of Japan 5

6 frequency of appearance distinct tag: n= attached tag: n= title: n= description: n= text length generation probablity Vol.23-DPS-56 No. Vol.23-GN-89 No. Vol.23-EIP-6 No. 23/9/ n= e number of tags attached to content 9 Fig. 9 appearence of text length Fig. number of tags attached to a content category tag n=3 locked tag n=8693 all tag n= tag ranking frequency of appearance n= tag ranking Fig. of attached tag 2 Fig. 2 number of videos using category tag 6 2 n nv iews nv nt ags tag j (6) n C n,view C n,view = n nv i= j= nv iews j,tag i tag j nv i= nv iews (6) i nt ags i C n,commnet C n,mylist 3.7% % view comment mylist % 3% (7), (8), (9) C 2 5,view =.883 exp(.248e 5 k) (7) C 2 5,comment =.89 exp(.83e 5 k) (8) C 2 5,mylist =.726 exp(.376e 5 k) (9) c 23 Information Processing Society of Japan 6

7 9 Vol.23-DPS-56 No. Vol.23-GN-89 No. Vol.23-EIP-6 No. 23/9/ resorted view count coverage= set of tag ranking resorted comment count coverage= set of tag ranking 3 Fig. 3 total view count by tag ranking 4 Fig. 4 total comment count by tag ranking 38% % [4] [5] [6] Web [7]. [8] 3 c 23 Information Processing Society of Japan 7

8 8 Vol.23-DPS-56 No. Vol.23-GN-89 No. Vol.23-EIP-6 No. 23/9/ 7 resorted mylist count coverage= set of tag ranking 5 Fig. 5 total mylist count by tag ranking coverage - -2 view comment mylist tag ranking upto topn 6 Fig. 6 coverage the counts by tag ranking number of videos attached rank - rank - rank - rank -3 rank 3-6 rank 6- average view count rank - rank - rank -2 rank 2-4 rank 4-7 rank tag lifetime [days] tag lifetime [days] Fig. 7 7 tags duration vs. number of attached 8 Fig. 8 tags duration vs. average view counts 5. [] http: // [2] nii.ac.jp/cscenter/idr/nico/nico.html. [3] Caldarelli, G., Capocci, A., De Los Rios, P. and Muñoz, M. A.: Scale-Free Networks from Varying Vertex Intrinsic Fitness, Phys. Rev. Lett., Vol. 89, p (online), DOI:.3/PhysRevLett (22). [4] Hotho, A., Jäschke, R., Schmitz, C. and Stumme, G.: FolkRank: A ranking algorithm for folksonomies, UNI- VERSITY OF HILDESHEIM, INSTITUTE OF COM- PUTER SCIENCE, pp. 4 (26). [5] BibSonomy: BibSonomy - The blue social bookmark and publication sharing system, org/. [6] Rossini, G. and Rossi, D.: Large scale simulation of CCN networks, 4èmes Rencontres Francophones sur les Aspects Algorithmiques des Télécommunications, La Grande Motte : France (22), (online), available from (22). [7] Wetzker, R., Zimmermann, C., Bauckhage, C. and Albayrak, S.: I tag, you tag: translating tags for advanced user models, Proceedings of the third ACM international conference on Web search and data mining, WSDM, New York, NY, USA, ACM, pp. 7 8 (online), DOI:.45/ (2). [8] TCR: CGM. B, Vol. 96-B, No. 2, pp (23). c 23 Information Processing Society of Japan 8

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