Temporal Dynamics of On-Line Information Streams

Kleinberg, Jon

doi:10.1007/978-3-540-28608-0_11

Cited by 17 publications

(19 citation statements)

References 23 publications

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“…Using the collection of text produced by users in the iOS sample as a baseline, we find the most representative words used by VoiceOver users with both the absolute change metric and the probability change metric. Although the probability change metric has been most recommended and widely applied in the industry 7 , we include the results based on the absolute change metric because it favors words with higher frequencies [13]. Thus, it can offer a better sense about the prevalence of those selected words and prevent our results from being dominated by a small set of highly distinctive (but relatively infrequent) words.…”

Section: Content Analysismentioning

confidence: 98%

“…For those who do post status updates, the median ratio of questions asked is 0 for both groups. To answer these questions, we take all the textual content in status updates and photo captions by sampled users with locale 'en-US' (US English), and apply the trend detection algorithm as described in [13]. Using the collection of text produced by users in the iOS sample as a baseline, we find the most representative words used by VoiceOver users with both the absolute change metric and the probability change metric.…”

Section: Content Analysismentioning

confidence: 99%

“…Our method for identifying the most representative words is a direct application of the two-point trends detection algorithm as described in [13]: using the status updates of iOS sample users as a training set to train a language model that predicts the frequencies of terms used in the status updates of VoiceOver sample users (testing set), we want to find the words in testing set that appear significantly more than predicted. To measure the significance of the change from "predicted" to "truth", we use normalized absolute change and the probability change metrics defined as below:…”

Section: Status Updatesmentioning

confidence: 99%

See 2 more Smart Citations

Visually impaired users on an online social network

Adamic

2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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In this paper we present the first large-scale empirical study of how visually impaired people use online social networks, specifically Facebook. We identify a sample of 50K visually impaired users, and study the activities they perform, the content they produce, and the friendship networks they build on Facebook. We find that visually impaired users participate on Facebook (e.g. status updates, comments, likes) as much as the general population, and receive more feedback (i.e., comments and likes) on average on their content. By analyzing the content produced by visually impaired users, we find that they share their experience and issues related to vision impairment. We also identify distinctive patterns in their language and technology use. We also show that, compared to other users, visually impaired users have smaller social networks, but such differences have decreased over time. Our findings have implications for improving the utility and usability of online social networks for visually impaired users.

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Section: Content Analysismentioning

confidence: 98%

Section: Content Analysismentioning

confidence: 99%

Section: Status Updatesmentioning

confidence: 99%

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Visually impaired users on an online social network

Adamic

2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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“…Thematic information flows can be put in accordance with the time series (publication intensity per unit time) [3], for the analysis of which the formal methods are more and more often applied: statistical, fractal, Fourier or wavelet analysis.…”

Section: Analysis Of the Thematic Information Flows Dynamicsmentioning

confidence: 99%

OSINT as a part of cyber defense system

Lande

Shnurko-Tabakova

2019

TACS

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The paper presents the results of research on the development of fundamental and applied principles for analyzing information flows in global computer networks while conducting open source intelligence (OSINT). The relevance of this task, in particular, concerning the provision of cyber security, the parameters of the modern information space, the existing theoretical and technological solutions are substantiated. The description of methodological and instrumental means of analysis and modeling of information flows, distributed content monitoring of global networks, the creation of multilingual full-text databases, analysis of the dynamics of thematic information flows with the use of nonlinear analysis, automatic formation of models of subject areas in the field of cyber security are presented.

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“…Recently, considerable related studies have been conducted, for example, activity recognition [3][4][5][6][7][8] , dynamic emotion analysis [9][10][11][12][13] , dynamic social network analysis [14][15][16][17][18] , and social influence analysis [19][20][21][22][23] . Emotion analysis is to study how an individual's emotional state (e.g., happiness and loneliness) propagates through social relationships [9][10][11] .…”

Section: Introductionmentioning

confidence: 99%

ACTPred: Activity prediction in mobile social networks

Gong

Tang

Fong

2014

Tinshhua Sci. Technol.

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A current trend for online social networks is to turn mobile. Mobile social networks directly reflect our real social life, and therefore are an important source to analyze and understand the underlying dynamics of human behaviors (activities). In this paper, we study the problem of activity prediction in mobile social networks. We present a series of observations in two real mobile social networks and then propose a method, ACTPred, based on a dynamic factor-graph model for modeling and predicting users' activities. An approximate algorithm based on mean fields is presented to efficiently learn the proposed method. We deploy a real system to collect users' mobility behaviors and validate the proposed method on two collected mobile datasets. Experimental results show that the proposed ACTPred model can achieve better performance than baseline methods.A. C. M. Fong received his PhD from University of Auckland in 1998. He is with the University of Glasgow in Singapore. He has published papers widely in the areas of data mining and communications. He is a senior member of the IEEE and a member of the IEEE Computer Society.

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Temporal Dynamics of On-Line Information Streams

Cited by 17 publications

References 23 publications

Visually impaired users on an online social network

Visually impaired users on an online social network

OSINT as a part of cyber defense system

ACTPred: Activity prediction in mobile social networks

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