On word-of-mouth based discovery of the web

Rodrigues, Tiago; Benevenuto, Fabrí­cio; Cha, Meeyoung; Gummadi, Krishna P.; Almeida, Virgílio A. F.

doi:10.1145/2068816.2068852

Cited by 113 publications

(68 citation statements)

References 29 publications

(31 reference statements)

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“…Our dataset also contains all tweets ever posted by the collected users, which consists of 1,755,925,520 tweets. For detailed characteristics of this dataset we refer the user to [9,23].…”

Section: Twitter Datasetmentioning

confidence: 99%

Understanding factors that affect response rates in twitter

Comarela

Crovella

Almeida

et al. 2012

Proceedings of the 23rd ACM Conference on Hypertext and Social Media

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In information networks where users send messages to one another, the issue of information overload naturally arises: which are the most important messages? In this paper we study the problem of understanding the importance of messages in Twitter. We approach this problem in two stages. First, we perform an extensive characterization of a very large Twitter dataset which includes all users, social relations, and messages posted from the beginning of the service up to August 2009. We show evidence that information overload is present: users sometimes have to search through hundreds of messages to find those that are interesting to reply or retweet. We then identify factors that influence user response or retweet probability: previous responses to the same tweeter, the tweeter's sending rate, the age and some basic text elements of the tweet. In our second stage, we show that some of these factors can be used to improve the presentation order of tweets to the user. First, by inspecting user activity over time, we construct a simple on-off model of user behavior that allows us to infer when a user is actively using Twitter. Then, we explore two methods from machine learning for ranking tweets: a Naive Bayes predictor and a Support Vector Machine classifier. We show that it is possible to reorder tweets to increase the fraction of replied or retweeted messages appearing in the first p positions of the list by as much as 50-60%.

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“…Our dataset also contains all tweets ever posted by the collected users, which consists of 1,755,925,520 tweets. For detailed characteristics of this dataset we refer the user to [9,23].…”

Section: Twitter Datasetmentioning

confidence: 99%

Understanding factors that affect response rates in twitter

Comarela

Crovella

Almeida

et al. 2012

Proceedings of the 23rd ACM Conference on Hypertext and Social Media

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“…Recently, word-of-mouth [13] has emerged as a popular way of discovering the videos, particularly over online social network (OSN) sites such as Facebook and Twitter, where users discover video contents following their friends' shares. It has also been a key driving force for the traffic from many video sharing sites (VSSes).…”

Section: Introductionmentioning

confidence: 99%

“…RELATED WORK There are some pioneer data-driven analysis of content propagation in OSNs. Rorigues et al [13] studied the propagation of URL links posted in Twitter, using large data gathered from Twitter. They presented the distribution of height, width, and size of propagation trees.…”

Section: Introductionmentioning

confidence: 99%

Video requests from Online Social Networks: Characterization, analysis and generation

Wang

Liu

et al. 2013

2013 Proceedings IEEE INFOCOM

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Abstract-The deep penetration of Online Social Networks (OSNs) have made them major portals for video content sharing. It is known that a significant portion of the accesses to video sharing sites are now coming from OSN users. Yet the unique features of video sharing over OSNs and their impact remain largely unknown. In this paper, we present a measurement study towards understanding the video requests from OSNs. We closely collaborated with a large-scale Facebook-like OSN to analyze its user access logs spanning over four months. Our measurement reveals a number of distinctive features on the popularity distribution of videos shared over the OSN. In particular, we observe that the OSN amplifies the skewness of video popularity so largely that about 2% most popular videos account for 90% of total views; the video requests distribution also exhibits perfect powerlaw feature; video popularity evolution shows more dynamics. All these noticeably differ from that of conventional videos, such as YouTube videos. To further understand the characteristics, we model the video viewing and sharing behaviors in OSNs, leading to the development of a practical emulator. It reveals the gap between the sharing rate and the viewing rate, and generates user requests that well capture the video popularity distribution and dynamics as observed in our empirical data.

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“…There are many reasons to look into URL redirection other than preservation, such as the need to identify spam [41], or an interest in conversation/discourse analysis and information propagation [29].…”

Section: Preservation Of Shortened Urlsmentioning

confidence: 99%

Analyzing Clusters and Constellations from Untwisting Shortened Links on Twitter Using Conceptual Graphs

Tonkin

Pfeiffer

Tourte

2013

Conceptual Structures for STEM Research and Education

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Abstract. The analysis of big data, although potentially a very rewarding task, can present difficulties due to the complexity inherent to such datasets. We suggest that conceptual graphs provide a mechanism for representing knowledge about a domain that can also be used as a useful scaffold for big data analysis. Conceptual graphs may be used as a means to collaboratively build up a robust model forming the skeleton of a data analysis project. This paper describes a case study in which conceptual graphs were used to underpin an exploration of a corpus of tweets relating to the Transportation Security Administration (TSA). Through this process we will demonstrate the emerging model built up of the data landscape involved and of the business structures that underlie the technical frameworks relied upon by microblogging software.

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On word-of-mouth based discovery of the web

Cited by 113 publications

References 29 publications

Understanding factors that affect response rates in twitter

Understanding factors that affect response rates in twitter

Video requests from Online Social Networks: Characterization, analysis and generation

Analyzing Clusters and Constellations from Untwisting Shortened Links on Twitter Using Conceptual Graphs

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