Assessing the role of participants in evolution of topic lifecycles on social networks

Background Topic lifecycle analysis on social networks aims to analyze and track how topics are born from user-generated content, and how they evolve. Twitter researchers have no agreed-upon definition of topics; topics on Twitter are typically derived in the form of (a) frequently used hashtags, or...
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Autor*in:	Dey, Kuntal [verfasserIn] Kaushik, Saroj Garg, Kritika Shrivastava, Ritvik

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Twitter topic lifecycle Evolution of social topics User influence in topic evolution Semantic clusters of hashtags as Twitter topics

Anmerkung:	© The Author(s) 2018

Übergeordnetes Werk:	Enthalten in: Computational Social Networks - New York, NY [u.a.] : Springer international, 2014, 5(2018), 1 vom: 02. Aug.
Übergeordnetes Werk:	volume:5 ; year:2018 ; number:1 ; day:02 ; month:08

Links:	Volltext

DOI / URN:	10.1186/s40649-018-0054-x

Katalog-ID:	SPR037115103

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520			\|a Background Topic lifecycle analysis on social networks aims to analyze and track how topics are born from user-generated content, and how they evolve. Twitter researchers have no agreed-upon definition of topics; topics on Twitter are typically derived in the form of (a) frequently used hashtags, or (b) keywords showing sudden trends of large occurrence in a short span of time (“bursty keywords”), or (c) concepts latent within the tweets that are grouped using variations of semantic clustering techniques. Methods In the current paper, we jointly model the hashtags present and the semantic concepts embedded in the content, which in turn helps us identify hashtag groups that define a “topic”—a concept space—that are used by a large number of tweets. Results We observe that different hashtags belonging to a given cluster are more prominent compared to the others, at different times. We further observe that the participation and influence levels of the different users play important roles in determining which hashtag would be more prominent than the others at given times. We thus observe topics to often morph from one to the other (via morphing of dominant hashtags representing the same semantic concept space), rather than becoming extinct outright, which is a novel insight about topic lifecycles. We further present novel observations about the role of users in determining the lifecycle of discussion topics on Twitter. Conclusions We infer that topic lifecycles are governed by user interests, and not by user influence, which is a key observation made by our work.
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allfields	10.1186/s40649-018-0054-x doi (DE-627)SPR037115103 (SPR)s40649-018-0054-x-e DE-627 ger DE-627 rakwb eng Dey, Kuntal verfasserin (orcid)0000-0001-6788-3168 aut Assessing the role of participants in evolution of topic lifecycles on social networks 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Background Topic lifecycle analysis on social networks aims to analyze and track how topics are born from user-generated content, and how they evolve. Twitter researchers have no agreed-upon definition of topics; topics on Twitter are typically derived in the form of (a) frequently used hashtags, or (b) keywords showing sudden trends of large occurrence in a short span of time (“bursty keywords”), or (c) concepts latent within the tweets that are grouped using variations of semantic clustering techniques. Methods In the current paper, we jointly model the hashtags present and the semantic concepts embedded in the content, which in turn helps us identify hashtag groups that define a “topic”—a concept space—that are used by a large number of tweets. Results We observe that different hashtags belonging to a given cluster are more prominent compared to the others, at different times. We further observe that the participation and influence levels of the different users play important roles in determining which hashtag would be more prominent than the others at given times. We thus observe topics to often morph from one to the other (via morphing of dominant hashtags representing the same semantic concept space), rather than becoming extinct outright, which is a novel insight about topic lifecycles. We further present novel observations about the role of users in determining the lifecycle of discussion topics on Twitter. Conclusions We infer that topic lifecycles are governed by user interests, and not by user influence, which is a key observation made by our work. Twitter topic lifecycle (dpeaa)DE-He213 Evolution of social topics (dpeaa)DE-He213 User influence in topic evolution (dpeaa)DE-He213 Semantic clusters of hashtags as Twitter topics (dpeaa)DE-He213 Kaushik, Saroj aut Garg, Kritika aut Shrivastava, Ritvik aut Enthalten in Computational Social Networks New York, NY [u.a.] : Springer international, 2014 5(2018), 1 vom: 02. Aug. (DE-627)815913907 (DE-600)2806588-8 2197-4314 nnns volume:5 year:2018 number:1 day:02 month:08 https://dx.doi.org/10.1186/s40649-018-0054-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2018 1 02 08
spelling	10.1186/s40649-018-0054-x doi (DE-627)SPR037115103 (SPR)s40649-018-0054-x-e DE-627 ger DE-627 rakwb eng Dey, Kuntal verfasserin (orcid)0000-0001-6788-3168 aut Assessing the role of participants in evolution of topic lifecycles on social networks 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Background Topic lifecycle analysis on social networks aims to analyze and track how topics are born from user-generated content, and how they evolve. Twitter researchers have no agreed-upon definition of topics; topics on Twitter are typically derived in the form of (a) frequently used hashtags, or (b) keywords showing sudden trends of large occurrence in a short span of time (“bursty keywords”), or (c) concepts latent within the tweets that are grouped using variations of semantic clustering techniques. Methods In the current paper, we jointly model the hashtags present and the semantic concepts embedded in the content, which in turn helps us identify hashtag groups that define a “topic”—a concept space—that are used by a large number of tweets. Results We observe that different hashtags belonging to a given cluster are more prominent compared to the others, at different times. We further observe that the participation and influence levels of the different users play important roles in determining which hashtag would be more prominent than the others at given times. We thus observe topics to often morph from one to the other (via morphing of dominant hashtags representing the same semantic concept space), rather than becoming extinct outright, which is a novel insight about topic lifecycles. We further present novel observations about the role of users in determining the lifecycle of discussion topics on Twitter. Conclusions We infer that topic lifecycles are governed by user interests, and not by user influence, which is a key observation made by our work. Twitter topic lifecycle (dpeaa)DE-He213 Evolution of social topics (dpeaa)DE-He213 User influence in topic evolution (dpeaa)DE-He213 Semantic clusters of hashtags as Twitter topics (dpeaa)DE-He213 Kaushik, Saroj aut Garg, Kritika aut Shrivastava, Ritvik aut Enthalten in Computational Social Networks New York, NY [u.a.] : Springer international, 2014 5(2018), 1 vom: 02. Aug. (DE-627)815913907 (DE-600)2806588-8 2197-4314 nnns volume:5 year:2018 number:1 day:02 month:08 https://dx.doi.org/10.1186/s40649-018-0054-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2018 1 02 08
allfields_unstemmed	10.1186/s40649-018-0054-x doi (DE-627)SPR037115103 (SPR)s40649-018-0054-x-e DE-627 ger DE-627 rakwb eng Dey, Kuntal verfasserin (orcid)0000-0001-6788-3168 aut Assessing the role of participants in evolution of topic lifecycles on social networks 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Background Topic lifecycle analysis on social networks aims to analyze and track how topics are born from user-generated content, and how they evolve. Twitter researchers have no agreed-upon definition of topics; topics on Twitter are typically derived in the form of (a) frequently used hashtags, or (b) keywords showing sudden trends of large occurrence in a short span of time (“bursty keywords”), or (c) concepts latent within the tweets that are grouped using variations of semantic clustering techniques. Methods In the current paper, we jointly model the hashtags present and the semantic concepts embedded in the content, which in turn helps us identify hashtag groups that define a “topic”—a concept space—that are used by a large number of tweets. Results We observe that different hashtags belonging to a given cluster are more prominent compared to the others, at different times. We further observe that the participation and influence levels of the different users play important roles in determining which hashtag would be more prominent than the others at given times. We thus observe topics to often morph from one to the other (via morphing of dominant hashtags representing the same semantic concept space), rather than becoming extinct outright, which is a novel insight about topic lifecycles. We further present novel observations about the role of users in determining the lifecycle of discussion topics on Twitter. Conclusions We infer that topic lifecycles are governed by user interests, and not by user influence, which is a key observation made by our work. Twitter topic lifecycle (dpeaa)DE-He213 Evolution of social topics (dpeaa)DE-He213 User influence in topic evolution (dpeaa)DE-He213 Semantic clusters of hashtags as Twitter topics (dpeaa)DE-He213 Kaushik, Saroj aut Garg, Kritika aut Shrivastava, Ritvik aut Enthalten in Computational Social Networks New York, NY [u.a.] : Springer international, 2014 5(2018), 1 vom: 02. Aug. (DE-627)815913907 (DE-600)2806588-8 2197-4314 nnns volume:5 year:2018 number:1 day:02 month:08 https://dx.doi.org/10.1186/s40649-018-0054-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2018 1 02 08
allfieldsGer	10.1186/s40649-018-0054-x doi (DE-627)SPR037115103 (SPR)s40649-018-0054-x-e DE-627 ger DE-627 rakwb eng Dey, Kuntal verfasserin (orcid)0000-0001-6788-3168 aut Assessing the role of participants in evolution of topic lifecycles on social networks 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Background Topic lifecycle analysis on social networks aims to analyze and track how topics are born from user-generated content, and how they evolve. Twitter researchers have no agreed-upon definition of topics; topics on Twitter are typically derived in the form of (a) frequently used hashtags, or (b) keywords showing sudden trends of large occurrence in a short span of time (“bursty keywords”), or (c) concepts latent within the tweets that are grouped using variations of semantic clustering techniques. Methods In the current paper, we jointly model the hashtags present and the semantic concepts embedded in the content, which in turn helps us identify hashtag groups that define a “topic”—a concept space—that are used by a large number of tweets. Results We observe that different hashtags belonging to a given cluster are more prominent compared to the others, at different times. We further observe that the participation and influence levels of the different users play important roles in determining which hashtag would be more prominent than the others at given times. We thus observe topics to often morph from one to the other (via morphing of dominant hashtags representing the same semantic concept space), rather than becoming extinct outright, which is a novel insight about topic lifecycles. We further present novel observations about the role of users in determining the lifecycle of discussion topics on Twitter. Conclusions We infer that topic lifecycles are governed by user interests, and not by user influence, which is a key observation made by our work. Twitter topic lifecycle (dpeaa)DE-He213 Evolution of social topics (dpeaa)DE-He213 User influence in topic evolution (dpeaa)DE-He213 Semantic clusters of hashtags as Twitter topics (dpeaa)DE-He213 Kaushik, Saroj aut Garg, Kritika aut Shrivastava, Ritvik aut Enthalten in Computational Social Networks New York, NY [u.a.] : Springer international, 2014 5(2018), 1 vom: 02. Aug. (DE-627)815913907 (DE-600)2806588-8 2197-4314 nnns volume:5 year:2018 number:1 day:02 month:08 https://dx.doi.org/10.1186/s40649-018-0054-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2018 1 02 08
allfieldsSound	10.1186/s40649-018-0054-x doi (DE-627)SPR037115103 (SPR)s40649-018-0054-x-e DE-627 ger DE-627 rakwb eng Dey, Kuntal verfasserin (orcid)0000-0001-6788-3168 aut Assessing the role of participants in evolution of topic lifecycles on social networks 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Background Topic lifecycle analysis on social networks aims to analyze and track how topics are born from user-generated content, and how they evolve. Twitter researchers have no agreed-upon definition of topics; topics on Twitter are typically derived in the form of (a) frequently used hashtags, or (b) keywords showing sudden trends of large occurrence in a short span of time (“bursty keywords”), or (c) concepts latent within the tweets that are grouped using variations of semantic clustering techniques. Methods In the current paper, we jointly model the hashtags present and the semantic concepts embedded in the content, which in turn helps us identify hashtag groups that define a “topic”—a concept space—that are used by a large number of tweets. Results We observe that different hashtags belonging to a given cluster are more prominent compared to the others, at different times. We further observe that the participation and influence levels of the different users play important roles in determining which hashtag would be more prominent than the others at given times. We thus observe topics to often morph from one to the other (via morphing of dominant hashtags representing the same semantic concept space), rather than becoming extinct outright, which is a novel insight about topic lifecycles. We further present novel observations about the role of users in determining the lifecycle of discussion topics on Twitter. Conclusions We infer that topic lifecycles are governed by user interests, and not by user influence, which is a key observation made by our work. Twitter topic lifecycle (dpeaa)DE-He213 Evolution of social topics (dpeaa)DE-He213 User influence in topic evolution (dpeaa)DE-He213 Semantic clusters of hashtags as Twitter topics (dpeaa)DE-He213 Kaushik, Saroj aut Garg, Kritika aut Shrivastava, Ritvik aut Enthalten in Computational Social Networks New York, NY [u.a.] : Springer international, 2014 5(2018), 1 vom: 02. Aug. (DE-627)815913907 (DE-600)2806588-8 2197-4314 nnns volume:5 year:2018 number:1 day:02 month:08 https://dx.doi.org/10.1186/s40649-018-0054-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2018 1 02 08
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Twitter researchers have no agreed-upon definition of topics; topics on Twitter are typically derived in the form of (a) frequently used hashtags, or (b) keywords showing sudden trends of large occurrence in a short span of time (“bursty keywords”), or (c) concepts latent within the tweets that are grouped using variations of semantic clustering techniques. Methods In the current paper, we jointly model the hashtags present and the semantic concepts embedded in the content, which in turn helps us identify hashtag groups that define a “topic”—a concept space—that are used by a large number of tweets. Results We observe that different hashtags belonging to a given cluster are more prominent compared to the others, at different times. We further observe that the participation and influence levels of the different users play important roles in determining which hashtag would be more prominent than the others at given times. We thus observe topics to often morph from one to the other (via morphing of dominant hashtags representing the same semantic concept space), rather than becoming extinct outright, which is a novel insight about topic lifecycles. We further present novel observations about the role of users in determining the lifecycle of discussion topics on Twitter. 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author	Dey, Kuntal
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title	Assessing the role of participants in evolution of topic lifecycles on social networks
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title_sort	assessing the role of participants in evolution of topic lifecycles on social networks
title_auth	Assessing the role of participants in evolution of topic lifecycles on social networks
abstract	Background Topic lifecycle analysis on social networks aims to analyze and track how topics are born from user-generated content, and how they evolve. Twitter researchers have no agreed-upon definition of topics; topics on Twitter are typically derived in the form of (a) frequently used hashtags, or (b) keywords showing sudden trends of large occurrence in a short span of time (“bursty keywords”), or (c) concepts latent within the tweets that are grouped using variations of semantic clustering techniques. Methods In the current paper, we jointly model the hashtags present and the semantic concepts embedded in the content, which in turn helps us identify hashtag groups that define a “topic”—a concept space—that are used by a large number of tweets. Results We observe that different hashtags belonging to a given cluster are more prominent compared to the others, at different times. We further observe that the participation and influence levels of the different users play important roles in determining which hashtag would be more prominent than the others at given times. We thus observe topics to often morph from one to the other (via morphing of dominant hashtags representing the same semantic concept space), rather than becoming extinct outright, which is a novel insight about topic lifecycles. We further present novel observations about the role of users in determining the lifecycle of discussion topics on Twitter. Conclusions We infer that topic lifecycles are governed by user interests, and not by user influence, which is a key observation made by our work. © The Author(s) 2018
abstractGer	Background Topic lifecycle analysis on social networks aims to analyze and track how topics are born from user-generated content, and how they evolve. Twitter researchers have no agreed-upon definition of topics; topics on Twitter are typically derived in the form of (a) frequently used hashtags, or (b) keywords showing sudden trends of large occurrence in a short span of time (“bursty keywords”), or (c) concepts latent within the tweets that are grouped using variations of semantic clustering techniques. Methods In the current paper, we jointly model the hashtags present and the semantic concepts embedded in the content, which in turn helps us identify hashtag groups that define a “topic”—a concept space—that are used by a large number of tweets. Results We observe that different hashtags belonging to a given cluster are more prominent compared to the others, at different times. We further observe that the participation and influence levels of the different users play important roles in determining which hashtag would be more prominent than the others at given times. We thus observe topics to often morph from one to the other (via morphing of dominant hashtags representing the same semantic concept space), rather than becoming extinct outright, which is a novel insight about topic lifecycles. We further present novel observations about the role of users in determining the lifecycle of discussion topics on Twitter. Conclusions We infer that topic lifecycles are governed by user interests, and not by user influence, which is a key observation made by our work. © The Author(s) 2018
abstract_unstemmed	Background Topic lifecycle analysis on social networks aims to analyze and track how topics are born from user-generated content, and how they evolve. Twitter researchers have no agreed-upon definition of topics; topics on Twitter are typically derived in the form of (a) frequently used hashtags, or (b) keywords showing sudden trends of large occurrence in a short span of time (“bursty keywords”), or (c) concepts latent within the tweets that are grouped using variations of semantic clustering techniques. Methods In the current paper, we jointly model the hashtags present and the semantic concepts embedded in the content, which in turn helps us identify hashtag groups that define a “topic”—a concept space—that are used by a large number of tweets. Results We observe that different hashtags belonging to a given cluster are more prominent compared to the others, at different times. We further observe that the participation and influence levels of the different users play important roles in determining which hashtag would be more prominent than the others at given times. We thus observe topics to often morph from one to the other (via morphing of dominant hashtags representing the same semantic concept space), rather than becoming extinct outright, which is a novel insight about topic lifecycles. We further present novel observations about the role of users in determining the lifecycle of discussion topics on Twitter. Conclusions We infer that topic lifecycles are governed by user interests, and not by user influence, which is a key observation made by our work. © The Author(s) 2018
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title_short	Assessing the role of participants in evolution of topic lifecycles on social networks
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author2	Kaushik, Saroj Garg, Kritika Shrivastava, Ritvik
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We thus observe topics to often morph from one to the other (via morphing of dominant hashtags representing the same semantic concept space), rather than becoming extinct outright, which is a novel insight about topic lifecycles. We further present novel observations about the role of users in determining the lifecycle of discussion topics on Twitter. 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Assessing the role of participants in evolution of topic lifecycles on social networks

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