The tube over time

Figueiredo, Flávio; Benevenuto, Fabrí­cio; Almeida, Jussara M.

doi:10.1145/1935826.1935925

Cited by 218 publications

(31 citation statements)

References 20 publications

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“…In Crane and Sornette (), an epidemic model was defined to prove that viewing activity on YouTube can be explained by different factors, for example, new popular topics. A later study also found that the popularity of videos can be predicted by, among other factors, the occurrence of external events; for example, the video being massively posted in other online social networks and blogs (Figueiredo, Benevenuto, & Almeida, ). The impact of similar events, defined with data from Google Trends, was also analyzed in Wikipedia, concluding that trending topics notably affect the popularity of articles (Ratkiewicz, Flammini, & Menczer, ).…”

Section: Previous Researchmentioning

confidence: 93%

Detecting Platform Effects in Online Discussions

Aragón¹,

Gómez²,

Kaltenbrunner³

2017

Policy & Internet

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Online discussions are the essence of many social platforms on the Internet. These platforms are receiving increasing interest because of their potential to become deliberative spaces. Many studies have proposed approaches to measure online deliberation and to evaluate which are the best design principles for deliberative online platforms. However, little research has focused on how deliberation in online platforms is affected by the arrival of events like the emergence of new topics or the modification of platform features. In this article we present a methodology to detect events that affect online deliberation in online discussions. Our results on Menéame, the most popular Spanish social news site, show that a change in how discussions are shown to the user, from a linear to a hierarchical conversation view, significantly enhanced deliberation. In particular we observe that this type of interface induced argumentative structures of online discussion.

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Section: Previous Researchmentioning

confidence: 93%

Detecting Platform Effects in Online Discussions

Aragón¹,

Gómez²,

Kaltenbrunner³

2017

Policy & Internet

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“…The rapid development of the Internet results in the availability of a huge amount of data with time information, making it possible to study the popularity dynamic of the online content. It was found that the popularity of various pieces of content on the Web, like news [ 2 ], Twitter [ 3 ], blog posts [ 4 ], videos [ 5 , 6 ], posts in online discussion forums [ 7 ] and product reviews [ 8 ], vary significantly on temporal scales. In this context, the early identification of the eventual popular content becomes an important problem [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Temporal Effects in Trend Prediction: Identifying the Most Popular Nodes in the Future

2015

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Prediction is an important problem in different science domains. In this paper, we focus on trend prediction in complex networks, i.e. to identify the most popular nodes in the future. Due to the preferential attachment mechanism in real systems, nodes’ recent degree and cumulative degree have been successfully applied to design trend prediction methods. Here we took into account more detailed information about the network evolution and proposed a temporal-based predictor (TBP). The TBP predicts the future trend by the node strength in the weighted network with the link weight equal to its exponential aging. Three data sets with time information are used to test the performance of the new method. We find that TBP have high general accuracy in predicting the future most popular nodes. More importantly, it can identify many potential objects with low popularity in the past but high popularity in the future. The effect of the decay speed in the exponential aging on the results is discussed in detail.

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“…Other research has found that "videos tend to get most of their views much earlier in their lifetimes" (Figueiredo, Benevenuto & Almeida, 2011). The research also found that "search and internal YouTube mechanisms, such as lists of related videos, are key mechanisms to attract users to the videos" which is significant to understand in order to maximise user engagement, as this would not occur at all if users could not reach the video itself.…”

Section: Related Workmentioning

confidence: 93%

Engage Me: How YouTube Motor Vloggers Can Maximise User Engagement

Kegen¹

2018

Preprint

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Without a true understanding of how users engage with content on social media, it can be difficult to understand its value, impact, and how to exploit it to one’s advantage. How a user engages can be divided up into many sub sections, but in its simplest notion it’s how a person interacts with the content that is presented to them. Engagement is also a sing that a person has payed attention to what is being presented to them.YouTube was created by former PayPal employees Steve Chen, Jawed Karim and Chad Hurley in 2005, and today with 1 billion monthly active users, it is the most used social networking and video sharing platform on the internet. YouTube also has a powerful effect in that it enables people to spread content effortlessly and freely to a wide and open audience. Understanding the incredible power of YouTube can be highlighted by examples such as Justin Bieber who rocketed to international stardom and fame via the platform, or how the world stopped and focused on Kony in 2012. Much of this success and popularity of the website has been explained by the fact that users are able to communicate “through textual and video responses as well as video rating systems” (Milliken, Gibson, O’Donnell & Singer 2008).This research paper serves to make important contributions to topics of viral marketing and engagement behaviours of users on YouTube. This is important when considering the popularity of YouTube and the numbers of users actively engaging in content, especially for business and individuals who use YouTube as their source of income. Research on various microblogging platforms has revealed “that microblogs can be tailored to facilitate informal communication between colleagues in organizations”, (Suh, Hong, Pirolli, & Chi, 2010) but there is little research that explores video blogging communities on YouTube. This research paper will explore how tags and the length of videos impact user engagement in the Motor Vlogging community.In section 2, a general overview of related in relevant research is presented, followed by the research question and hypothesis in section 3 which explores the relationship between social engagement on YouTube Motor Vlogger videos from Australia and how the presence of tags and the length of the videos impact on the levels of engagement. The methodology of the research is presented in section 4 with results presented and discussed in sections 5 and 6 respectively.

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The tube over time

Cited by 218 publications

References 20 publications

Detecting Platform Effects in Online Discussions

Detecting Platform Effects in Online Discussions

Temporal Effects in Trend Prediction: Identifying the Most Popular Nodes in the Future

Engage Me: How YouTube Motor Vloggers Can Maximise User Engagement

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