TrendLearner: Early prediction of popularity trends of user generated content
Predicting the popularity of user generated content (UGC) is a valuable task to content providers, advertisers, as well as social media researchers. However, it is also a challenging task due to the plethora of factors that affect content popularity in social systems. Here, we focus on the problem o...
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| Autor*in: |
Figueiredo, Flavio [verfasserIn] |
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E-Artikel |
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| Sprache: |
Englisch |
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2016transfer abstract |
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16 |
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Enthalten in: Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study - Petrruzziello, Carmelina ELSEVIER, 2013, an international journal, New York, NY |
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volume:349 ; year:2016 ; day:1 ; month:07 ; pages:172-187 ; extent:16 |
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10.1016/j.ins.2016.02.025 |
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| 520 | |a Predicting the popularity of user generated content (UGC) is a valuable task to content providers, advertisers, as well as social media researchers. However, it is also a challenging task due to the plethora of factors that affect content popularity in social systems. Here, we focus on the problem of predicting the popularity trend of a piece of UGC (object) as early as possible. Unlike previous work, we explicitly address the inherent tradeoff between prediction accuracy and remaining interest in the object after prediction, since, to be useful, accurate predictions should be made before interest has exhausted. Given the heterogeneity in popularity dynamics across objects, this tradeoff has to be solved on a per-object basis, making the prediction task harder. We tackle this problem with a novel two-step learning approach in which we: (1) extract popularity trends from previously uploaded objects, and then (2) predict trends for newly uploaded content. Our results for YouTube datasets show that our classification effectiveness, captured by F1 scores, is 38% better than the baseline approaches. Moreover, we achieve these results with up to 68% of the views still remaining for 50% or 21% of the videos, depending on the dataset. | ||
| 520 | |a Predicting the popularity of user generated content (UGC) is a valuable task to content providers, advertisers, as well as social media researchers. However, it is also a challenging task due to the plethora of factors that affect content popularity in social systems. Here, we focus on the problem of predicting the popularity trend of a piece of UGC (object) as early as possible. Unlike previous work, we explicitly address the inherent tradeoff between prediction accuracy and remaining interest in the object after prediction, since, to be useful, accurate predictions should be made before interest has exhausted. Given the heterogeneity in popularity dynamics across objects, this tradeoff has to be solved on a per-object basis, making the prediction task harder. We tackle this problem with a novel two-step learning approach in which we: (1) extract popularity trends from previously uploaded objects, and then (2) predict trends for newly uploaded content. Our results for YouTube datasets show that our classification effectiveness, captured by F1 scores, is 38% better than the baseline approaches. Moreover, we achieve these results with up to 68% of the views still remaining for 50% or 21% of the videos, depending on the dataset. | ||
| 700 | 1 | |a Almeida, Jussara M. |4 oth | |
| 700 | 1 | |a Gonçalves, Marcos A. |4 oth | |
| 700 | 1 | |a Benevenuto, Fabricio |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science Inc |a Petrruzziello, Carmelina ELSEVIER |t Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study |d 2013 |d an international journal |g New York, NY |w (DE-627)ELV011843691 |
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10.1016/j.ins.2016.02.025 doi GBV00000000000197A.pica (DE-627)ELV024888001 (ELSEVIER)S0020-0255(16)30084-6 DE-627 ger DE-627 rakwb eng 070 004 070 DNB 004 DNB 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Figueiredo, Flavio verfasserin aut TrendLearner: Early prediction of popularity trends of user generated content 2016transfer abstract 16 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Predicting the popularity of user generated content (UGC) is a valuable task to content providers, advertisers, as well as social media researchers. However, it is also a challenging task due to the plethora of factors that affect content popularity in social systems. Here, we focus on the problem of predicting the popularity trend of a piece of UGC (object) as early as possible. Unlike previous work, we explicitly address the inherent tradeoff between prediction accuracy and remaining interest in the object after prediction, since, to be useful, accurate predictions should be made before interest has exhausted. Given the heterogeneity in popularity dynamics across objects, this tradeoff has to be solved on a per-object basis, making the prediction task harder. We tackle this problem with a novel two-step learning approach in which we: (1) extract popularity trends from previously uploaded objects, and then (2) predict trends for newly uploaded content. Our results for YouTube datasets show that our classification effectiveness, captured by F1 scores, is 38% better than the baseline approaches. Moreover, we achieve these results with up to 68% of the views still remaining for 50% or 21% of the videos, depending on the dataset. Predicting the popularity of user generated content (UGC) is a valuable task to content providers, advertisers, as well as social media researchers. However, it is also a challenging task due to the plethora of factors that affect content popularity in social systems. Here, we focus on the problem of predicting the popularity trend of a piece of UGC (object) as early as possible. Unlike previous work, we explicitly address the inherent tradeoff between prediction accuracy and remaining interest in the object after prediction, since, to be useful, accurate predictions should be made before interest has exhausted. Given the heterogeneity in popularity dynamics across objects, this tradeoff has to be solved on a per-object basis, making the prediction task harder. We tackle this problem with a novel two-step learning approach in which we: (1) extract popularity trends from previously uploaded objects, and then (2) predict trends for newly uploaded content. Our results for YouTube datasets show that our classification effectiveness, captured by F1 scores, is 38% better than the baseline approaches. Moreover, we achieve these results with up to 68% of the views still remaining for 50% or 21% of the videos, depending on the dataset. Almeida, Jussara M. oth Gonçalves, Marcos A. oth Benevenuto, Fabricio oth Enthalten in Elsevier Science Inc Petrruzziello, Carmelina ELSEVIER Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study 2013 an international journal New York, NY (DE-627)ELV011843691 volume:349 year:2016 day:1 month:07 pages:172-187 extent:16 https://doi.org/10.1016/j.ins.2016.02.025 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 349 2016 1 0701 172-187 16 045F 070 |
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10.1016/j.ins.2016.02.025 doi GBV00000000000197A.pica (DE-627)ELV024888001 (ELSEVIER)S0020-0255(16)30084-6 DE-627 ger DE-627 rakwb eng 070 004 070 DNB 004 DNB 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Figueiredo, Flavio verfasserin aut TrendLearner: Early prediction of popularity trends of user generated content 2016transfer abstract 16 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Predicting the popularity of user generated content (UGC) is a valuable task to content providers, advertisers, as well as social media researchers. However, it is also a challenging task due to the plethora of factors that affect content popularity in social systems. Here, we focus on the problem of predicting the popularity trend of a piece of UGC (object) as early as possible. Unlike previous work, we explicitly address the inherent tradeoff between prediction accuracy and remaining interest in the object after prediction, since, to be useful, accurate predictions should be made before interest has exhausted. Given the heterogeneity in popularity dynamics across objects, this tradeoff has to be solved on a per-object basis, making the prediction task harder. We tackle this problem with a novel two-step learning approach in which we: (1) extract popularity trends from previously uploaded objects, and then (2) predict trends for newly uploaded content. Our results for YouTube datasets show that our classification effectiveness, captured by F1 scores, is 38% better than the baseline approaches. Moreover, we achieve these results with up to 68% of the views still remaining for 50% or 21% of the videos, depending on the dataset. Predicting the popularity of user generated content (UGC) is a valuable task to content providers, advertisers, as well as social media researchers. However, it is also a challenging task due to the plethora of factors that affect content popularity in social systems. Here, we focus on the problem of predicting the popularity trend of a piece of UGC (object) as early as possible. Unlike previous work, we explicitly address the inherent tradeoff between prediction accuracy and remaining interest in the object after prediction, since, to be useful, accurate predictions should be made before interest has exhausted. Given the heterogeneity in popularity dynamics across objects, this tradeoff has to be solved on a per-object basis, making the prediction task harder. We tackle this problem with a novel two-step learning approach in which we: (1) extract popularity trends from previously uploaded objects, and then (2) predict trends for newly uploaded content. Our results for YouTube datasets show that our classification effectiveness, captured by F1 scores, is 38% better than the baseline approaches. Moreover, we achieve these results with up to 68% of the views still remaining for 50% or 21% of the videos, depending on the dataset. Almeida, Jussara M. oth Gonçalves, Marcos A. oth Benevenuto, Fabricio oth Enthalten in Elsevier Science Inc Petrruzziello, Carmelina ELSEVIER Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study 2013 an international journal New York, NY (DE-627)ELV011843691 volume:349 year:2016 day:1 month:07 pages:172-187 extent:16 https://doi.org/10.1016/j.ins.2016.02.025 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 349 2016 1 0701 172-187 16 045F 070 |
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10.1016/j.ins.2016.02.025 doi GBV00000000000197A.pica (DE-627)ELV024888001 (ELSEVIER)S0020-0255(16)30084-6 DE-627 ger DE-627 rakwb eng 070 004 070 DNB 004 DNB 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Figueiredo, Flavio verfasserin aut TrendLearner: Early prediction of popularity trends of user generated content 2016transfer abstract 16 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Predicting the popularity of user generated content (UGC) is a valuable task to content providers, advertisers, as well as social media researchers. However, it is also a challenging task due to the plethora of factors that affect content popularity in social systems. Here, we focus on the problem of predicting the popularity trend of a piece of UGC (object) as early as possible. Unlike previous work, we explicitly address the inherent tradeoff between prediction accuracy and remaining interest in the object after prediction, since, to be useful, accurate predictions should be made before interest has exhausted. Given the heterogeneity in popularity dynamics across objects, this tradeoff has to be solved on a per-object basis, making the prediction task harder. We tackle this problem with a novel two-step learning approach in which we: (1) extract popularity trends from previously uploaded objects, and then (2) predict trends for newly uploaded content. Our results for YouTube datasets show that our classification effectiveness, captured by F1 scores, is 38% better than the baseline approaches. Moreover, we achieve these results with up to 68% of the views still remaining for 50% or 21% of the videos, depending on the dataset. Predicting the popularity of user generated content (UGC) is a valuable task to content providers, advertisers, as well as social media researchers. However, it is also a challenging task due to the plethora of factors that affect content popularity in social systems. Here, we focus on the problem of predicting the popularity trend of a piece of UGC (object) as early as possible. Unlike previous work, we explicitly address the inherent tradeoff between prediction accuracy and remaining interest in the object after prediction, since, to be useful, accurate predictions should be made before interest has exhausted. Given the heterogeneity in popularity dynamics across objects, this tradeoff has to be solved on a per-object basis, making the prediction task harder. We tackle this problem with a novel two-step learning approach in which we: (1) extract popularity trends from previously uploaded objects, and then (2) predict trends for newly uploaded content. Our results for YouTube datasets show that our classification effectiveness, captured by F1 scores, is 38% better than the baseline approaches. Moreover, we achieve these results with up to 68% of the views still remaining for 50% or 21% of the videos, depending on the dataset. Almeida, Jussara M. oth Gonçalves, Marcos A. oth Benevenuto, Fabricio oth Enthalten in Elsevier Science Inc Petrruzziello, Carmelina ELSEVIER Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study 2013 an international journal New York, NY (DE-627)ELV011843691 volume:349 year:2016 day:1 month:07 pages:172-187 extent:16 https://doi.org/10.1016/j.ins.2016.02.025 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 349 2016 1 0701 172-187 16 045F 070 |
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10.1016/j.ins.2016.02.025 doi GBV00000000000197A.pica (DE-627)ELV024888001 (ELSEVIER)S0020-0255(16)30084-6 DE-627 ger DE-627 rakwb eng 070 004 070 DNB 004 DNB 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Figueiredo, Flavio verfasserin aut TrendLearner: Early prediction of popularity trends of user generated content 2016transfer abstract 16 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Predicting the popularity of user generated content (UGC) is a valuable task to content providers, advertisers, as well as social media researchers. However, it is also a challenging task due to the plethora of factors that affect content popularity in social systems. Here, we focus on the problem of predicting the popularity trend of a piece of UGC (object) as early as possible. Unlike previous work, we explicitly address the inherent tradeoff between prediction accuracy and remaining interest in the object after prediction, since, to be useful, accurate predictions should be made before interest has exhausted. Given the heterogeneity in popularity dynamics across objects, this tradeoff has to be solved on a per-object basis, making the prediction task harder. We tackle this problem with a novel two-step learning approach in which we: (1) extract popularity trends from previously uploaded objects, and then (2) predict trends for newly uploaded content. Our results for YouTube datasets show that our classification effectiveness, captured by F1 scores, is 38% better than the baseline approaches. Moreover, we achieve these results with up to 68% of the views still remaining for 50% or 21% of the videos, depending on the dataset. Predicting the popularity of user generated content (UGC) is a valuable task to content providers, advertisers, as well as social media researchers. However, it is also a challenging task due to the plethora of factors that affect content popularity in social systems. Here, we focus on the problem of predicting the popularity trend of a piece of UGC (object) as early as possible. Unlike previous work, we explicitly address the inherent tradeoff between prediction accuracy and remaining interest in the object after prediction, since, to be useful, accurate predictions should be made before interest has exhausted. Given the heterogeneity in popularity dynamics across objects, this tradeoff has to be solved on a per-object basis, making the prediction task harder. We tackle this problem with a novel two-step learning approach in which we: (1) extract popularity trends from previously uploaded objects, and then (2) predict trends for newly uploaded content. Our results for YouTube datasets show that our classification effectiveness, captured by F1 scores, is 38% better than the baseline approaches. Moreover, we achieve these results with up to 68% of the views still remaining for 50% or 21% of the videos, depending on the dataset. Almeida, Jussara M. oth Gonçalves, Marcos A. oth Benevenuto, Fabricio oth Enthalten in Elsevier Science Inc Petrruzziello, Carmelina ELSEVIER Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study 2013 an international journal New York, NY (DE-627)ELV011843691 volume:349 year:2016 day:1 month:07 pages:172-187 extent:16 https://doi.org/10.1016/j.ins.2016.02.025 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 349 2016 1 0701 172-187 16 045F 070 |
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10.1016/j.ins.2016.02.025 doi GBV00000000000197A.pica (DE-627)ELV024888001 (ELSEVIER)S0020-0255(16)30084-6 DE-627 ger DE-627 rakwb eng 070 004 070 DNB 004 DNB 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Figueiredo, Flavio verfasserin aut TrendLearner: Early prediction of popularity trends of user generated content 2016transfer abstract 16 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Predicting the popularity of user generated content (UGC) is a valuable task to content providers, advertisers, as well as social media researchers. However, it is also a challenging task due to the plethora of factors that affect content popularity in social systems. Here, we focus on the problem of predicting the popularity trend of a piece of UGC (object) as early as possible. Unlike previous work, we explicitly address the inherent tradeoff between prediction accuracy and remaining interest in the object after prediction, since, to be useful, accurate predictions should be made before interest has exhausted. Given the heterogeneity in popularity dynamics across objects, this tradeoff has to be solved on a per-object basis, making the prediction task harder. We tackle this problem with a novel two-step learning approach in which we: (1) extract popularity trends from previously uploaded objects, and then (2) predict trends for newly uploaded content. Our results for YouTube datasets show that our classification effectiveness, captured by F1 scores, is 38% better than the baseline approaches. Moreover, we achieve these results with up to 68% of the views still remaining for 50% or 21% of the videos, depending on the dataset. Predicting the popularity of user generated content (UGC) is a valuable task to content providers, advertisers, as well as social media researchers. However, it is also a challenging task due to the plethora of factors that affect content popularity in social systems. Here, we focus on the problem of predicting the popularity trend of a piece of UGC (object) as early as possible. Unlike previous work, we explicitly address the inherent tradeoff between prediction accuracy and remaining interest in the object after prediction, since, to be useful, accurate predictions should be made before interest has exhausted. Given the heterogeneity in popularity dynamics across objects, this tradeoff has to be solved on a per-object basis, making the prediction task harder. We tackle this problem with a novel two-step learning approach in which we: (1) extract popularity trends from previously uploaded objects, and then (2) predict trends for newly uploaded content. Our results for YouTube datasets show that our classification effectiveness, captured by F1 scores, is 38% better than the baseline approaches. Moreover, we achieve these results with up to 68% of the views still remaining for 50% or 21% of the videos, depending on the dataset. Almeida, Jussara M. oth Gonçalves, Marcos A. oth Benevenuto, Fabricio oth Enthalten in Elsevier Science Inc Petrruzziello, Carmelina ELSEVIER Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study 2013 an international journal New York, NY (DE-627)ELV011843691 volume:349 year:2016 day:1 month:07 pages:172-187 extent:16 https://doi.org/10.1016/j.ins.2016.02.025 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 349 2016 1 0701 172-187 16 045F 070 |
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Enthalten in Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study New York, NY volume:349 year:2016 day:1 month:07 pages:172-187 extent:16 |
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Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study |
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Predicting the popularity of user generated content (UGC) is a valuable task to content providers, advertisers, as well as social media researchers. However, it is also a challenging task due to the plethora of factors that affect content popularity in social systems. Here, we focus on the problem of predicting the popularity trend of a piece of UGC (object) as early as possible. Unlike previous work, we explicitly address the inherent tradeoff between prediction accuracy and remaining interest in the object after prediction, since, to be useful, accurate predictions should be made before interest has exhausted. Given the heterogeneity in popularity dynamics across objects, this tradeoff has to be solved on a per-object basis, making the prediction task harder. We tackle this problem with a novel two-step learning approach in which we: (1) extract popularity trends from previously uploaded objects, and then (2) predict trends for newly uploaded content. Our results for YouTube datasets show that our classification effectiveness, captured by F1 scores, is 38% better than the baseline approaches. Moreover, we achieve these results with up to 68% of the views still remaining for 50% or 21% of the videos, depending on the dataset. |
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Predicting the popularity of user generated content (UGC) is a valuable task to content providers, advertisers, as well as social media researchers. However, it is also a challenging task due to the plethora of factors that affect content popularity in social systems. Here, we focus on the problem of predicting the popularity trend of a piece of UGC (object) as early as possible. Unlike previous work, we explicitly address the inherent tradeoff between prediction accuracy and remaining interest in the object after prediction, since, to be useful, accurate predictions should be made before interest has exhausted. Given the heterogeneity in popularity dynamics across objects, this tradeoff has to be solved on a per-object basis, making the prediction task harder. We tackle this problem with a novel two-step learning approach in which we: (1) extract popularity trends from previously uploaded objects, and then (2) predict trends for newly uploaded content. Our results for YouTube datasets show that our classification effectiveness, captured by F1 scores, is 38% better than the baseline approaches. Moreover, we achieve these results with up to 68% of the views still remaining for 50% or 21% of the videos, depending on the dataset. |
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Predicting the popularity of user generated content (UGC) is a valuable task to content providers, advertisers, as well as social media researchers. However, it is also a challenging task due to the plethora of factors that affect content popularity in social systems. Here, we focus on the problem of predicting the popularity trend of a piece of UGC (object) as early as possible. Unlike previous work, we explicitly address the inherent tradeoff between prediction accuracy and remaining interest in the object after prediction, since, to be useful, accurate predictions should be made before interest has exhausted. Given the heterogeneity in popularity dynamics across objects, this tradeoff has to be solved on a per-object basis, making the prediction task harder. We tackle this problem with a novel two-step learning approach in which we: (1) extract popularity trends from previously uploaded objects, and then (2) predict trends for newly uploaded content. Our results for YouTube datasets show that our classification effectiveness, captured by F1 scores, is 38% better than the baseline approaches. Moreover, we achieve these results with up to 68% of the views still remaining for 50% or 21% of the videos, depending on the dataset. |
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