Cumulative effect in information diffusion: empirical study on a microblogging network
Cumulative effect in social contagion underlies many studies on the spread of innovation, behavior, and influence. However, few large-scale empirical studies are conducted to validate the existence of cumulative effect in information diffusion on social networks. In this paper, using the population-...
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| Veröffentlicht in: | PloS one 2013-10, Vol.8 (10), p.e76027-e76027 |
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| description | Cumulative effect in social contagion underlies many studies on the spread of innovation, behavior, and influence. However, few large-scale empirical studies are conducted to validate the existence of cumulative effect in information diffusion on social networks. In this paper, using the population-scale dataset from the largest Chinese microblogging website, we conduct a comprehensive study on the cumulative effect in information diffusion. We base our study on the diffusion network of message, where nodes are the involved users and links characterize forwarding relationship among them. We find that multiple exposures to the same message indeed increase the possibility of forwarding it. However, additional exposures cannot further improve the chance of forwarding when the number of exposures crosses its peak at two. This finding questions the cumulative effect hypothesis in information diffusion. Furthermore, to clarify the forwarding preference among users, we investigate both structural motif in the diffusion network and temporal pattern in information diffusion process. Findings provide some insights for understanding the variation of message popularity and explain the characteristics of diffusion network. |
| doi_str_mv | 10.1371/journal.pone.0076027 |
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Findings provide some insights for understanding the variation of message popularity and explain the characteristics of diffusion network.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0076027</identifier><identifier>PMID: 24098422</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Analysis ; Blogging ; Data mining ; Diffusion ; Diffusion effects ; Exposure ; Humans ; Information Dissemination ; Information processing ; Innovations ; International conferences ; Internet ; Mechanics ; Microblogs ; Models, Statistical ; Neural Networks (Computer) ; Neurosciences ; Science ; Social behavior ; Social Media ; Social networks ; Social organization ; Studies ; World Wide Web</subject><ispartof>PloS one, 2013-10, Vol.8 (10), p.e76027-e76027</ispartof><rights>COPYRIGHT 2013 Public Library of Science</rights><rights>2013 Bao et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2013 Bao et al 2013 Bao et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-79eabb075192a2dc3f5d418b8fe90c4e2c3edf602912b9f8b47398d4fdcaa38e3</citedby><cites>FETCH-LOGICAL-c692t-79eabb075192a2dc3f5d418b8fe90c4e2c3edf602912b9f8b47398d4fdcaa38e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3788071/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3788071/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793,79600,79601</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/24098422$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Moreno, Yamir</contributor><creatorcontrib>Bao, Peng</creatorcontrib><creatorcontrib>Shen, Hua-Wei</creatorcontrib><creatorcontrib>Chen, Wei</creatorcontrib><creatorcontrib>Cheng, Xue-Qi</creatorcontrib><title>Cumulative effect in information diffusion: empirical study on a microblogging network</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Cumulative effect in social contagion underlies many studies on the spread of innovation, behavior, and influence. However, few large-scale empirical studies are conducted to validate the existence of cumulative effect in information diffusion on social networks. In this paper, using the population-scale dataset from the largest Chinese microblogging website, we conduct a comprehensive study on the cumulative effect in information diffusion. We base our study on the diffusion network of message, where nodes are the involved users and links characterize forwarding relationship among them. We find that multiple exposures to the same message indeed increase the possibility of forwarding it. However, additional exposures cannot further improve the chance of forwarding when the number of exposures crosses its peak at two. This finding questions the cumulative effect hypothesis in information diffusion. Furthermore, to clarify the forwarding preference among users, we investigate both structural motif in the diffusion network and temporal pattern in information diffusion process. Findings provide some insights for understanding the variation of message popularity and explain the characteristics of diffusion network.</description><subject>Analysis</subject><subject>Blogging</subject><subject>Data mining</subject><subject>Diffusion</subject><subject>Diffusion effects</subject><subject>Exposure</subject><subject>Humans</subject><subject>Information Dissemination</subject><subject>Information processing</subject><subject>Innovations</subject><subject>International conferences</subject><subject>Internet</subject><subject>Mechanics</subject><subject>Microblogs</subject><subject>Models, Statistical</subject><subject>Neural Networks (Computer)</subject><subject>Neurosciences</subject><subject>Science</subject><subject>Social behavior</subject><subject>Social Media</subject><subject>Social networks</subject><subject>Social organization</subject><subject>Studies</subject><subject>World Wide 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Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bao, Peng</au><au>Shen, Hua-Wei</au><au>Chen, Wei</au><au>Cheng, Xue-Qi</au><au>Moreno, Yamir</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Cumulative effect in information diffusion: empirical study on a microblogging network</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2013-10-01</date><risdate>2013</risdate><volume>8</volume><issue>10</issue><spage>e76027</spage><epage>e76027</epage><pages>e76027-e76027</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Cumulative effect in social contagion underlies many studies on the spread of innovation, behavior, and influence. However, few large-scale empirical studies are conducted to validate the existence of cumulative effect in information diffusion on social networks. In this paper, using the population-scale dataset from the largest Chinese microblogging website, we conduct a comprehensive study on the cumulative effect in information diffusion. We base our study on the diffusion network of message, where nodes are the involved users and links characterize forwarding relationship among them. We find that multiple exposures to the same message indeed increase the possibility of forwarding it. However, additional exposures cannot further improve the chance of forwarding when the number of exposures crosses its peak at two. This finding questions the cumulative effect hypothesis in information diffusion. Furthermore, to clarify the forwarding preference among users, we investigate both structural motif in the diffusion network and temporal pattern in information diffusion process. Findings provide some insights for understanding the variation of message popularity and explain the characteristics of diffusion network.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>24098422</pmid><doi>10.1371/journal.pone.0076027</doi><tpages>e76027</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Analysis Blogging Data mining Diffusion Diffusion effects Exposure Humans Information Dissemination Information processing Innovations International conferences Internet Mechanics Microblogs Models, Statistical Neural Networks (Computer) Neurosciences Science Social behavior Social Media Social networks Social organization Studies World Wide Web |
| title | Cumulative effect in information diffusion: empirical study on a microblogging network |
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