Complex contagions and the diffusion of popular Twitter hashtags in Nigeria

Social media sites such as Facebook and Twitter provide highly granular time-stamped data about the interactions and communications between people and provide us unprecedented opportunities for empirically testing theory about information flow in social networks. Using publicly available data from T...

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Vydáno v:	Social network analysis and mining Ročník 6; číslo 1; s. 1
Hlavní autoři:	Fink, Clay, Schmidt, Aurora, Barash, Vladimir, Cameron, Christopher, Macy, Michael
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Vienna Springer Vienna 01.12.2016 Springer Nature B.V
Témata:	Application programming interface Applications of Graph Theory and Complex Networks Behavior Computer mediated communication Computer Science Connectedness Contagion Contagion theory Critical mass Data mining Data Mining and Knowledge Discovery Diffusion of Information and Influence in Social Networks Digital media Economics Epidemics Game Theory Humanities Information dissemination Information flow Information retrieval Information sharing Interpersonal communication Islam Kidnapping Law Marketing Mass media Media coverage Methodology of the Social Sciences Middle Eastern cultural groups News News media Original Article Participation Propagation Reinforcement Social activism Social and Behav. Sciences Social contagion Social media Social movements Social networks Statistics for Social Sciences Tagging Tags Theory Nigeria Network structure Twitter Social movements Complex contagion
ISSN:	1869-5450, 1869-5469
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Abstract	Social media sites such as Facebook and Twitter provide highly granular time-stamped data about the interactions and communications between people and provide us unprecedented opportunities for empirically testing theory about information flow in social networks. Using publicly available data from Twitter’s free API (Application Program Interface), we track the adoption of popular hashtags in Nigeria during 2014. These hashtags reference online marketing campaigns, major news stories, and events and issues specific to Nigeria, including reactions to the kidnapping of 276 schoolgirls in Northeastern Nigeria by the Islamic extremist group Boko Haram. We find that hashtags related to Nigerian sociopolitical issues, including the #bringbackourgirls hashtag, which was associated with protests against the Nigerian government’s response to the kidnapping, are more likely to be adopted among densely connected users with multiple network neighbors who have also adopted the hashtag, compared to mainstream news hashtags. This association between adoption threshold and local network structure is consistent with theory about the spread of complex contagions, a type of social contagion which requires social reinforcement from multiple adopting neighbors. Theory also predicts the need for a critical mass of adopters before the contagion can become viral. We illustrate this with the #bringbackourgirls hashtag by identifying the point at which the local social movement transforms into a more widespread phenomenon. We also show that these results are robust across both the follow and reply/mention/retweet networks on Twitter. Our analysis involves data mining records of hashtag adoption and of the social connections between adopters.
AbstractList	Social media sites such as Facebook and Twitter provide highly granular time-stamped data about the interactions and communications between people and provide us unprecedented opportunities for empirically testing theory about information flow in social networks. Using publicly available data from Twitter’s free API (Application Program Interface), we track the adoption of popular hashtags in Nigeria during 2014. These hashtags reference online marketing campaigns, major news stories, and events and issues specific to Nigeria, including reactions to the kidnapping of 276 schoolgirls in Northeastern Nigeria by the Islamic extremist group Boko Haram. We find that hashtags related to Nigerian sociopolitical issues, including the #bringbackourgirls hashtag, which was associated with protests against the Nigerian government’s response to the kidnapping, are more likely to be adopted among densely connected users with multiple network neighbors who have also adopted the hashtag, compared to mainstream news hashtags. This association between adoption threshold and local network structure is consistent with theory about the spread of complex contagions, a type of social contagion which requires social reinforcement from multiple adopting neighbors. Theory also predicts the need for a critical mass of adopters before the contagion can become viral. We illustrate this with the #bringbackourgirls hashtag by identifying the point at which the local social movement transforms into a more widespread phenomenon. We also show that these results are robust across both the follow and reply/mention/retweet networks on Twitter. Our analysis involves data mining records of hashtag adoption and of the social connections between adopters.
ArticleNumber	1
Author	Macy, Michael Schmidt, Aurora Barash, Vladimir Cameron, Christopher Fink, Clay
Author_xml	– sequence: 1 givenname: Clay surname: Fink fullname: Fink, Clay organization: The Johns Hopkins University Applied Physics Laboratory – sequence: 2 givenname: Aurora surname: Schmidt fullname: Schmidt, Aurora organization: The Johns Hopkins University Applied Physics Laboratory – sequence: 3 givenname: Vladimir orcidid: 0000-0001-9096-1830 surname: Barash fullname: Barash, Vladimir email: vlad.barash@graphika.com – sequence: 4 givenname: Christopher surname: Cameron fullname: Cameron, Christopher organization: Social Dynamics Laboratory, Cornell University – sequence: 5 givenname: Michael surname: Macy fullname: Macy, Michael organization: Social Dynamics Laboratory, Cornell University
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Cites_doi	10.1086/226707 10.1086/521848 10.1145/1963405.1963503 10.1016/j.socnet.2012.02.003 10.1038/srep02522 10.1145/1557019.1557077 10.1086/229028 10.1038/30918
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References_xml	– reference: Marwell G, Oliver PE, Prahl R (1988) Social networks and collective action: a theory of the critical mass. iii. Am J Sociol 502–534 – reference: Barash V (2011) The dynamics of social contagion. PhD thesis, Cornell University – reference: Odufuwa F (2012) Understanding what is happening in ICT in Nigeria. Technical report, Research ICT Africa – reference: GranovetterMThreshold models of collective behaviorAm J Sociol1978861420144310.1086/226707 – reference: WattsDJStrogatzSHCollective dynamics of ‘small-world’ networksNature1998393668444044210.1038/30918 – reference: Leskovec J, Backstrom L, Kleinberg J (2009) Meme-tracking and the dynamics of the news cycle. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, pp 497–506. ACM – reference: Weng L, Menczer F, Ahn YY (2013) Virality prediction and community structure in social networks. Scientific reports, 3 – reference: BarashVCameronCMacyMCritical phenomena in complex contagionsSoc Netw201234445146110.1016/j.socnet.2012.02.003 – reference: CentolaDMacyMComplex contagions and the weakness of long ties1Am J Sociol2007113370273410.1086/521848 – reference: Geonames (2015) Retrieved from http://www.geonames.org. Accessed 23 Dec 2015 – reference: Wikipedia (2014) YesAllWomen. Retrieved from https://en.wikipedia.org/wiki/YesAllWomen. Accessed 23 Dec 2015 – reference: International Telecommunications Union (2015) Statistics: percentage of individuals using the internet. Retrieved from https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx. Accessed 23 Dec 2015 – reference: BBC (2014) #BBCtrending: The man who 'disappeared' in Nigeria - #FreeCiaxon. Retrieved from http://www.bbc.com/news/blogs-trending-26979508. Accessed 23 Dec 2015 – reference: Know Your Meme (2015) #WeAreAllMonkeys. Retrieved from http://knowyourmeme.com/memes/events/weareallmonkeys. Accessed 23 Dec 2015 – reference: Wu S, Tan C, Kleinberg J, Macy M (2011) Does bad news go away faster? In: Fifth international AAAI conference on weblogs and social media ICWSM. AAAI – reference: Nahon K, Hemsley J (2013) Going viral. Polity Press, Cambridge – reference: Romero DM, Meeder B, Kleinberg J (2011) Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In: Proceedings of the 20th international conference on world wide web, pp 695–704. ACM – reference: MilgramSThe small world problemPsychol Today19672160671030627 – ident: 311_CR17 – ident: 311_CR5 – ident: 311_CR7 – ident: 311_CR3 – volume: 8 start-page: 1420 issue: 6 year: 1978 ident: 311_CR6 publication-title: Am J Sociol doi: 10.1086/226707 – ident: 311_CR8 – volume: 113 start-page: 702 issue: 3 year: 2007 ident: 311_CR4 publication-title: Am J Sociol doi: 10.1086/521848 – ident: 311_CR14 doi: 10.1145/1963405.1963503 – ident: 311_CR18 – ident: 311_CR1 – volume: 34 start-page: 451 issue: 4 year: 2012 ident: 311_CR2 publication-title: Soc Netw doi: 10.1016/j.socnet.2012.02.003 – ident: 311_CR16 doi: 10.1038/srep02522 – ident: 311_CR9 doi: 10.1145/1557019.1557077 – volume: 2 start-page: 60 issue: 1 year: 1967 ident: 311_CR11 publication-title: Psychol Today – ident: 311_CR10 doi: 10.1086/229028 – ident: 311_CR13 – volume: 393 start-page: 440 issue: 6684 year: 1998 ident: 311_CR15 publication-title: Nature doi: 10.1038/30918 – ident: 311_CR12
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Title	Complex contagions and the diffusion of popular Twitter hashtags in Nigeria
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